Data distribution and security in multi-tier storage infrastructure

Abstract

Techniques related to data distribution and security in a multi-tier storage infrastructure are described. An associated computer-implemented method includes receiving file data associated with a user for storage in a managed service domain, applying an ensemble learning model to design a data distribution technique for the file data based on contextual information associated with the user, and encrypting the file data. The method further includes segmenting the file data for storage in a cloud computing tier, a fog computing tier, and a local computing tier based on the data distribution technique by performing a hash transform and applying at least one cyclic error correction code. In embodiments, the method further includes receiving a data access request associated with the file data, authenticating the data access request, and recovering the file data by decryption.

Description

Background Technology

[0001] The various embodiments described herein generally relate to data distribution and security in multi-tiered storage infrastructures. More specifically, the various embodiments describe techniques for distributing and protecting file data in a multi-tiered storage infrastructure that includes a fog computing layer. Summary of the Invention

[0002] The various embodiments described herein provide data distribution and security technologies. These embodiments further provide data recovery technologies after data distribution. One associated computer-implemented method includes: receiving file data associated with a user for storage in a managed service domain; applying an ensemble learning model to design a data distribution technology for the file data based on contextual information associated with the user; and encrypting the file data. The method further includes: partitioning the file data for storage among a cloud computing layer, a fog computing layer, and a local computing layer by performing a hash transformation and applying at least one cyclic error correction code based on the data distribution technology. In embodiments, the method further includes: receiving a data access request associated with the file data; authenticating the data access request; and recovering the file data by decryption.

[0003] One or more additional embodiments relate to a computer program product including a computer-readable storage medium having program instructions embodied therein. According to such embodiments, the program instructions are executable by a computing device to cause the computing device to perform one or more steps associated with the computer-implemented method described above and / or implement one or more embodiments associated with the computer-implemented method described above. One or more further embodiments relate to a system having at least one processor and a memory storing an application program, which, when executed on the at least one processor, performs one or more steps associated with the computer-implemented method described above and / or implements one or more embodiments associated with the computer-implemented method described above. Attached Figure Description

[0004] To implement and understand the above aspects in detail, a more specific description of the embodiments briefly outlined above can be obtained with reference to the accompanying drawings.

[0005] However, it should be noted that the accompanying drawings only illustrate typical embodiments of the invention and should not be considered as limiting the scope of the invention, as the invention can allow for other equally effective embodiments.

[0006] Figure 1 A cloud computing environment according to one or more embodiments is described.

[0007] Figure 2An abstract model layer provided by a cloud computing environment according to one or more embodiments is described.

[0008] Figure 3 A management service domain associated with a cloud computing environment is described according to one or more embodiments.

[0009] Figure 4 A multi-tiered storage infrastructure associated with a management service domain is illustrated according to one or more embodiments.

[0010] Figure 5 A method for processing file data according to one or more embodiments is shown.

[0011] Figure 6 A method is illustrated for designing data distribution techniques for file data based on user context information using an application-centric learning model according to one or more embodiments.

[0012] Figure 7 A method for deriving topic context data by applying natural language processing (NLP) to user context information, according to one or more embodiments, is illustrated.

[0013] Figure 8 A method for creating multiple encoded feature vectors according to one or more implementations is shown.

[0014] Figure 9 A method for exporting multiple portions of file data by applying NLP to file data based on exported topic context data, according to one or more embodiments, is illustrated.

[0015] Figure 10 A method for partitioning file data according to one or more embodiments is shown.

[0016] Figure 11 This illustrates a method for requesting authentication data access according to one or more embodiments. Detailed Implementation

[0017] The various embodiments described herein pertain to techniques for data processing within a multi-tiered storage infrastructure that combines a cloud computing layer, a fog computing layer, and a local computing layer. The embodiments provide techniques for distributing and protecting file data within the multi-tiered storage infrastructure. The embodiments further provide techniques for recovering distributed and secure file data upon receiving an authenticated request. The embodiments further provide data backup techniques incorporating a data warehouse. In the various embodiments, the cloud computing layer of the multi-tiered storage infrastructure includes a management service domain associated with a cloud computing environment. A cloud computing environment is a virtualized environment in which one or more computing capabilities can be used as services. A cloud server system configured to implement the data distribution and security techniques associated with the embodiments described herein can leverage the artificial intelligence capabilities of machine learning knowledge models (specifically, ensemble learning models) and information from a knowledge base associated with such models.

[0018] Various embodiments may offer advantages over conventional technologies. For example, in the event of an attack on a data center and / or network infrastructure, conventional software as a service technology may make remote data vulnerable to security attacks and / or privacy breaches. Various embodiments improve computing technology by facilitating the distribution of file data across multiple tiers of a multi-tiered storage infrastructure, making it more difficult for unauthorized users to access the entire file data. Because data is partitioned between storage tiers according to various embodiments, all tiers need to be accessed to recover all file data across these tiers. Therefore, unauthorized users will be unable to extract a usable version of the file data without accessing the entire file data across all tiers. Furthermore, various embodiments facilitate the inclusion of data warehouses in the local storage tier of the multi-tiered storage infrastructure, enabling authorized users to perform complete data recovery in the event of loss of file data in one or more storage tiers. Some of the different embodiments may not include all of these advantages, and these advantages are not necessarily required in all embodiments.

[0019] In the following text, reference is made to various embodiments of the invention. However, it should be understood that the invention is not limited to the specifically described embodiments. Rather, any combination of the following features and elements (whether or not related to different embodiments) is contemplated to implement and practice the invention. Furthermore, while embodiments may achieve advantages over other possible solutions and / or over the prior art, they are not limiting regardless of whether a given embodiment achieves a particular advantage. Therefore, the following aspects, features, embodiments, and advantages are merely illustrative and should not be considered elements or limitations of the appended claims unless expressly stated in the claims. Similarly, references to “the invention” should not be construed as a generalization of any inventive subject matter disclosed herein and should not be considered elements or limitations of the appended claims unless expressly stated in one or more claims.

[0020] This invention can be a system, method, and / or computer program product with any possible level of technical detail integration. The computer program product may include a computer-readable storage medium (or media) having computer-readable program instructions thereon for causing a processor to execute aspects of the invention.

[0021] Computer-readable storage media can be tangible means for retaining and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example, but not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media includes: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital universal disk (DVD), memory sticks, floppy disks, mechanical encoding devices such as punch cards or protrusions in slots having instructions recorded thereon, and any suitable combination of the foregoing. As used herein, computer-readable storage media should not be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses passing through fiber optic cables), or electrical signals transmitted through wires.

[0022] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to a suitable computing / processing device via a network (e.g., the Internet, a local area network, a wide area network, and / or a wireless network), or to an external computer or external storage device. The network may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to a computer-readable storage medium within the suitable computing / processing device.

[0023] Computer-readable program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​(such as Smalltalk, C++, etc.) and conventional procedural programming languages ​​(such as the "C" programming language or similar programming languages). The computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as a standalone software package, partially on a user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network (including a local area network (LAN) or a wide area network (WAN)) or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs) may be personalized to execute computer-readable program instructions by utilizing state information from the computer-readable program instructions in order to perform aspects of this invention.

[0024] The present invention will now be described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0025] These computer-readable program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / actions specified in one or more blocks of a flowchart and / or block diagram. These computer-readable program instructions may also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner, such that the computer-readable storage medium storing the instructions includes an article of manufacture containing instructions that implement aspects of the functions / actions specified in one or more blocks of a flowchart and / or block diagram.

[0026] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other device to produce a computer-implemented process, such that the instructions, which execute on the computer, other programmable apparatus, or other device, perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0027] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, segment, or portion of instructions, including one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the figures. For example, two blocks shown consecutively may actually be completed as a single step, executed simultaneously, substantially simultaneously, or with partial or complete temporal overlap, or the blocks may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action or executes a combination of dedicated hardware and computer instructions.

[0028] The detailed descriptions depict techniques related to data distribution and security in multi-tiered storage infrastructures. However, it should be understood that the techniques described herein are applicable to various purposes beyond those specifically described herein. Therefore, references to particular embodiments are included in this document for illustrative purposes only and not for limiting purposes.

[0029] The various embodiments described herein can be provided to end users via cloud computing infrastructure. It should be understood that while this disclosure includes a detailed description of cloud computing, implementations of the teachings cited herein are not limited to cloud computing environments. Rather, the various embodiments described herein can be implemented in conjunction with any other type of computing environment now known or developed hereafter.

[0030] Cloud computing is a service delivery model that enables convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing power, storage, applications, virtual machines, and services), which can be rapidly provisioned and released with minimal management effort or interaction with the service provider. Therefore, cloud computing allows users to access virtual computing resources (e.g., storage, data, applications, and even complete virtualized computing systems) in the cloud, regardless of the underlying physical systems used to provide those computing resources (or the location of those systems). This cloud model may include at least five features, at least three service models, and at least four deployment models.

[0031] The features are as follows:

[0032] On-demand self-service: Cloud consumers can unilaterally and automatically provide computing power, such as server time and network storage, as needed, without requiring human interaction with the service provider.

[0033] Extensive network access: Capabilities are available through networks and accessed via standard mechanisms that facilitate the use of heterogeneous thin client or thick client platforms (e.g., mobile phones, laptops, and personal digital assistants (PDAs)).

[0034] Resource pooling: A provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, where different physical and virtual resources are dynamically assigned and reassigned as needed. There is a sense of location independence because consumers typically do not have control or knowledge of the exact location of the resources provided, but may be able to specify the location at a higher level of abstraction (e.g., country, state, or data center).

[0035] Rapid flexibility: The ability to provide capacity quickly and flexibly, automatically scaling down and up rapidly in some situations to scale up rapidly. For consumers, the available supply capacity often appears unlimited and can be purchased in any quantity at any time.

[0036] Measurable services: Cloud systems automatically control and optimize resource usage by leveraging metering capabilities at a level of abstraction appropriate to the service type (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency to both service providers and consumers.

[0037] The service model is as follows:

[0038] Software as a Service (SaaS): This provides consumers with the ability to use the provider's applications running on cloud infrastructure. Applications can be accessed from different client devices via thin client interfaces such as web browsers (e.g., web-based email). Consumers do not manage or control the underlying cloud infrastructure, including the network, servers, operating system, storage, or even individual application capabilities, with possible exceptions such as limited user-specific application configuration settings.

[0039] Platform as a Service (PaaS): This provides consumers with the ability to deploy applications created or acquired by the consumer using programming languages ​​and tools supported by the provider onto cloud infrastructure. Consumers do not manage or control the underlying cloud infrastructure, including networks, servers, operating systems, or storage, but they have control over the deployed applications and the configuration of any application hosting environment.

[0040] Infrastructure as a Service (IaaS): The capabilities offered to consumers are processing, storage, networking, and other basic computing resources that enable consumers to deploy and run arbitrary software, which may include operating systems and applications. Consumers do not manage or control the underlying cloud infrastructure, but rather have control over the operating system, storage, deployed applications, and potentially limited control over selected networking components (e.g., host firewalls).

[0041] The deployment model is as follows:

[0042] Private cloud: A cloud infrastructure that operates solely for an organization. It can be managed by the organization or a third party and can exist on-site or off-site.

[0043] Community cloud: A cloud infrastructure shared by several organizations and supporting a specific community with common concerns (e.g., mission, security requirements, policies, and compliance considerations). It can be managed by an organization or a third party and can exist on-site or off-site.

[0044] Public cloud: Cloud infrastructure available to the general public or large industry groups and owned by organizations that sell cloud services.

[0045] Hybrid cloud: A cloud infrastructure is a combination of two or more clouds (private, community, or public) that remain a single entity but are bound together by standardized or proprietary technologies that enable data and applications to be ported (e.g., cloud bursting for load balancing between clouds).

[0046] Cloud computing environments are service-oriented, focusing on statelessness, loose coupling, modularity, and semantic interoperability. At the heart of cloud computing is the infrastructure comprising a network of interconnected nodes.

[0047] Figure 1 A cloud computing environment 50 according to one or more embodiments is illustrated. As shown, the cloud computing environment 50 may include one or more cloud computing nodes 10, with local computing devices used by cloud consumers (e.g., personal digital assistants or cellular phones 54A, desktop computers 54B, laptop computers 54C, and / or automotive computer systems 54N) communicating with the cloud computing nodes 10. The nodes 10 may communicate with each other. They may be physically or virtually grouped (not shown) in one or more networks, such as private clouds, community clouds, public clouds, or hybrid clouds, or combinations thereof, as described above. Therefore, the cloud computing environment 50 can provide infrastructure, platforms, and / or software as services that cloud consumers do not need to maintain resources on their local computing devices. It should be understood that... Figure 1 The types of computing devices 54A-N shown are intended to be illustrative only, and computing node 10 and cloud computing environment 50 can communicate with any type of computerized device via any type of network and / or network-addressable connection (e.g., using a web browser).

[0048] Figure 2 A set of functional abstraction layers provided by a cloud computing environment 50 according to one or more implementations is shown. Figure 2 The components, layers, and functions shown are intended to be exemplary only; the different embodiments described herein are not limited thereto. As described, different layers and corresponding functions are provided. Specifically, hardware and software layer 60 includes hardware and software components. Examples of hardware components may include host 61, RISC (Reduced Instruction Set Computer) based server 62, server 63, blade server 64, storage device 65, and network and network components 66. In some embodiments, software components may include network application server software 67 and database software 68. Virtualization layer 70 provides an abstraction layer from which the following examples of virtual entities can be provided: virtual server 71; virtual memory 72; virtual network 73, including virtual private network; virtual application and operating system 74; and virtual client 75.

[0049] In one example, management layer 80 can provide the following functionalities: Resource provisioning 81 provides dynamic procurement of computing resources and other resources used to perform tasks within the cloud computing environment 50. Metering and pricing 82 provides cost tracking as resources are utilized within the cloud computing environment 50 and bills or invoices for the consumption of these resources. In one example, these resources may include application software licenses. Security provides authentication for cloud consumers and tasks, as well as protection for data and other resources. User portal 83 provides access to the cloud computing environment for consumers and system administrators. Service level management 84 provides cloud resource allocation and management to ensure that required service levels are met. Service level agreement (SLA) planning and fulfillment 85 provides pre-scheduling and procurement of cloud resources, anticipating future requirements for those resources according to the SLA.

[0050] Workload layer 90 provides examples of functionalities that can be utilized from cloud computing environment 50. Examples of workloads and functionalities that can be provided from this layer include: mapping and navigation 91; software development and lifecycle management 92; virtual classroom education delivery 93; data analytics and processing 94; transaction processing 95; and data distribution and recovery 96. Data distribution and recovery 96 enables the distribution of file data across storage layers and the recovery of such file data in response to authenticated requests according to the various embodiments described herein.

[0051] Figure 3A management service domain 300 within a cloud computing environment 50 is illustrated. Functions related to data distribution and recovery 96, and other workloads / functions, can be performed within the management service domain 300. The management service domain 300 includes a cloud server system 310. In an embodiment, the cloud server system 310 includes a cloud server application 315 configured to implement and / or facilitate data distribution and recovery according to various embodiments described herein. The cloud server application 315 represents a single application or multiple applications. The cloud server application 315 includes or is otherwise operatively coupled to an ensemble learning model 320. Furthermore, in one embodiment, the management service domain 300 includes a cloud computing interface 330, a database system 340, a directory server system 350, and multiple application server clusters 3601 to 360. n One or more of them. The cloud computing interface 330 enables communication between the cloud server system 310 and one or more client systems that interact with the managed service domain 300. (See also: Regarding...) Figure 4 Further described, the cloud computing interface 330 enables communication with (multiple) fog server systems, (multiple) local machines, and / or other components interfaced with the management service domain 300. Additionally, in one embodiment, the cloud server system 310 is configured to communicate with the database system 340, the directory server system 350, and / or the application server clusters 3601 to 3602. n Communication. In addition, application server clusters 3601 to 360... n The application servers within the domain can be configured to communicate with each other, with servers in other domains, and / or with other components connected to the managed service domain 300 interface.

[0052] Database system 340 optionally coordinates and manages the knowledge base of the holistic learning model 320. The knowledge base associated with the holistic learning model 320 optionally includes all repositories, ontologies, files and / or documents associated with the model, and the associated data processing. Database system 340 may include one or more database servers that coordinate and / or manage various aspects of the knowledge base. Database system 340 stores the holistic learning model 320 and multiple application server clusters 3601 to 360. n And the relationships between knowledge bases. In one embodiment, database system 340 is managed via a database management system (DBMS), optionally a relational database management system (RDBMS). In another embodiment, database system 340 includes one or more databases, some or all of which may be relational databases. In yet another embodiment, database system 340 includes one or more ontology trees or other ontology structures.

[0053] Directory server system 350 facilitates client authentication in management service domain 300, including regarding application server clusters 3601 to 360. n Client authentication for one or more applications within the management service domain 300. For authentication purposes relative to one or more applications within the management service domain 300, the client may provide environment details and credentials associated with such application(s). Application server clusters 3601 to 360 n This system stores various aspects of different applications and provides application services to one or more client systems. Cloud server application 315 and other data collection components of management service domain 300 are configured to provide appropriate notifications regarding any user context data collection. One or more aspects of management service domain 300 are also configured to provide users with the option to opt in or out of any such user context data collection at any time. Optionally, one or more aspects of management service domain 300 are also configured to send at least one notification to any affected user on a user-specified basis (e.g., periodically or whenever any such user context data collection occurs).

[0054] Figure 4 A multi-tiered storage infrastructure 400 is illustrated, comprising a cloud computing layer 403 (i.e., cloud layer), a fog computing layer 413 (i.e., fog layer), and a local computing layer 423 (i.e., local layer). The cloud layer 403 of the multi-tiered storage infrastructure 400 includes, operatively coupled to, and / or communicatively coupled to, other aspects of a cloud server system 310 and a management service domain 300. The cloud server system 310 stores cloud layer data 405, which optionally includes file data distributed according to the various embodiments described herein. The cloud server system 310 resides within one or more storage and / or database components associated with the managed service domain 300 (e.g., in relation to...). Figure 3 The storage of cloud data is stored or facilitated within one or more components described and / or within one or more other hardware-based storage or database components optionally virtualized within cloud layer 403. The fog layer 413 of the multi-tier storage infrastructure 400 includes fog server systems 4101 to 410. n Fog server systems 4101 to 410 n Transfer fog server data 4151 to 415 n Stored separately, for example, in fog server systems 4101 to 410. n Within one or more associated hardware-based storage or database components. Such hardware-based storage or database components may optionally be virtualized within fog layer 413. In an alternative configuration, fog layer 413 includes a single fog server system corresponding to elements 4101 to 410. n One or a combination thereof, storing elements 4151 to 415n One or a combination of fog layer data. In the context of the various embodiments described herein, fog calculation within a fog layer (specifically, Figure 4 In the context of fog (413), it refers to distributed computing involving one or more devices or systems located on the periphery of a cloud computing environment between cloud resources and on-premises resources. Fog computing associated with such a fog layer can be used as a remote cloud computing layer associated with the cloud layer and a local computing device or system at the on-premises layer (specifically, in...). Figure 4 The decentralized intermediaries between local layers 423 in the context of the cloud, specifically in Figure 4 This is related to cloud layer 403 in the context of [the cloud]. Fog computing allows data associated with multiple devices to be aggregated into storage nodes with regional connectivity.

[0055] The local tier 423 of the multi-tier storage infrastructure 400 includes local machines 4201 to 420. n Local machines 4201 to 420 n Each of these represents or includes at least one device and / or system incorporating at least one hardware component. Local machines 4201 to 420 n For example, in relation to local machines 4201 to 420 respectively n Local layer data 4251 to 425 are stored within one or more associated hardware-based storage or database components. n In addition, local machines 4201 to 420 n These include, for example, local machines 4201 to 420 respectively. n Data warehouses 4351 to 435 within one or more associated hardware-based storage or database components. n Fog server systems 4101 to 410 n Each of these can optionally send to one or more local machines 4201 to 420 n Provides regional connectivity and / or data aggregation capabilities. In an alternative configuration, local layer 423 includes elements 4201 to 420. n One or a combination of these single local machines, whose storage corresponds to elements 4251 to 425. n Local layer data of one of them, and containing (or operably coupled to) data corresponding to elements 4351 to 435 n A data range containing one or more of the following.

[0056] In an alternative configuration, the multi-tier storage infrastructure 400 includes multiple intermediate layers in addition to the three layers described above, such as an intermediate layer between cloud layer 403 and fog layer 413 and / or an intermediate layer between fog layer 413 and local layer 423. Such intermediate layers optionally buffer data for transfer between cloud layer 403, fog layer 413, and / or local layer 423. Additionally or alternatively, such intermediate layers incorporate characteristics of adjacent layers. For example, the intermediate layer between cloud layer 403 and fog layer 413 optionally combines certain aspects of both cloud layer 403 and fog layer 413. In another instance, the intermediate layer between fog layer 413 and local layer 423 optionally incorporates certain aspects of both fog layer 413 and local layer 423.

[0057] In one embodiment, the management service domain 300 is a hybrid cloud interface communicatively coupled to the fog layer 413 of the multi-tier storage infrastructure 400 via at least one network connection. According to such an embodiment, the management service domain 300 is further communicatively coupled via at least one network connection, optionally via the fog layer 413, to at least one local machine (e.g., local machines 4201 to 420) associated with a user in the local tier 423 of the multi-tier storage infrastructure 400. n (at least one of them). According to such embodiments, fog layer 413 may optionally be a component of a virtual private cloud within a hybrid cloud environment, wherein an on-demand pool of configurable resources is allocated to one or more designated users. In another embodiment, at least one local machine is communicatively coupled to management service domain 300 (specifically, one or more of its components) via an application programming interface (API) that facilitates cloud connectivity. In a further embodiment, local machines 4201 to 420 n At least one of the components of the managed service domain 300 communicates via a data exchange format such as JavaScript Object Notation (JSON) or Extensible Markup Language (XML). In another embodiment, local machines 4201 to 420 n At least one of them is an edge computing device. According to such a further embodiment, if the file data to be processed, according to different embodiments, involves data collected from sensors or distributed computing locations (e.g., in the context of Internet of Things (IoT) infrastructure), then such a local machine can interface with cloud layer 403 and / or fog layer 413 via an edge computing device. Local machines 4201 to 420... n At least one of them includes a fuzzy edge computing hybrid implementation within a multi-tiered storage infrastructure 400 for edge computing devices, thereby providing the local system benefits of edge computing with the interconnectivity and aggregation capabilities of fuzzy computing.

[0058] Figure 5A method 500 for processing file data is illustrated. Specifically, method 500 involves encrypting and distributing file data between storage tiers of a multi-tiered storage infrastructure (e.g., multi-tiered storage infrastructure 400), and further involves decrypting and recovering file data in response to receiving an authenticated data access request. In one embodiment, one or more steps associated with method 500 are performed in an environment where computing power is provided as a service (e.g., cloud computing environment 50). According to such embodiments, one or more steps associated with method 500 are performed in a management service domain (e.g., management service domain 300) within that environment. This environment is optionally a hybrid cloud environment. In another embodiment, one or more steps associated with method 500 are performed in one or more other environments (such as a client-server network environment or a peer-to-peer network environment). A centralized cloud server system in the management service domain (e.g., cloud server system 310 in management service domain 300) can facilitate processing according to method 500 and other methods further described herein. More specifically, a cloud server application (e.g., cloud server application 315) in a cloud server system may perform or facilitate the execution of one or more steps of method 500 and other methods described herein. Data processing techniques facilitated or otherwise performed via a cloud server system in a management service domain may be associated with data distribution and recovery workloads within a workload layer of a functional abstraction layer provided by the environment (e.g., data distribution and recovery 96 within workload layer 90 of cloud computing environment 50). In a further embodiment, the cloud server application performs one or more steps of method 500 and other methods described herein via one or more programming instructions encoded in a high-level programming language (e.g., Python, C, C++, and / or C#).

[0059] Method 500 begins at step 505, wherein a cloud server application receives file data associated with a user for storage in a managed service domain. The received file data may optionally be data intended by the user or associated entity to be stored within a tiered storage infrastructure for privacy, accessibility, and / or security purposes. In an embodiment, the received file data originates from a single file. Alternatively, the received file data originates from multiple files. In an embodiment, the cloud server application may optionally receive the file data within the managed service domain via a cloud computing interface (e.g., cloud computing interface 330). According to such an embodiment, the cloud server application receives and continues to process the file data received within the cloud server system. In a further embodiment, at step 505, the cloud server application receives the file data via a client interface associated with the user. According to such further embodiments and other embodiments described herein, the client interface is a user interface in the form of a graphical user interface (GUI) and / or a command-line interface (CLI), which is accessed via local machines 4201 to 420 within the tiered storage infrastructure (e.g., tiered storage infrastructure 400). n It is presented by at least one client application within a user system or device or otherwise accessible or operatively / communically coupled to a multi-tier storage infrastructure.

[0060] In step 510, the cloud server application applies an ensemble learning model (e.g., ensemble learning model 320) to design a data distribution technique for the file data based on contextual information associated with the user. In the context of the various embodiments described herein, the ensemble learning model combines multiple artificial intelligence techniques, including machine learning techniques and optionally deep learning techniques, which work together to identify topics and / or groups of topics associated with the file data and classify the file data based on such topic identifications. The various artificial intelligence techniques may include one or more natural language processing (NLP) models and / or one or more multi-class classification techniques. In the context of the various embodiments described herein, the data distribution technique partitions the file data between storage tiers of a multi-tiered storage infrastructure and further distributes redundant data between storage tiers to preserve dependencies necessary for file data recovery and / or transmission. The cloud server application designs the data distribution technique based on topic identification and classification achieved via the ensemble learning model. The data distribution technique allocates portions or sub-portions of the file data to various storage tiers. As further described herein, the respective storage tiers are optionally associated with corresponding data buckets into which the cloud server application partitions the file data. In various embodiments, a data bucket is a data structure configured to store distributed data. A data bucket may optionally be or include a container or buffer.

[0061] During the application of the overall learning model according to step 510, the cloud server application locally analyzes the received file data based on contextual information associated with the user. In an embodiment, the contextual information associated with the user is based on historical and / or currently observed user patterns of application and data access, as well as different attributes associated with the user. According to such an embodiment, the cloud server application may optionally apply an ensemble learning model to design a data distribution technique based at least in part on one or more of a plurality of user contextual factors. The plurality of user contextual factors may optionally include: user frequency of data use, user frequency of application use, user system configuration, user file storage mode, user attributes (e.g., user occupation, organization affiliated with the user, user's personal or business contacts), and / or user data content (e.g., user data file type and / or size) associated with historically, recently, or currently used applications or files. See reference. Figure 6 The method for applying the holistic learning model according to step 510 is described.

[0062] In step 515, the cloud server application encrypts the file data. The cloud server application encrypts the file data to prepare for the implementation of the data distribution technology designed in step 510. In one embodiment, the cloud server application designs an encryption strategy combining one or more encryption technologies based on user context information (e.g., based on security and / or privacy settings associated with such information). Additionally or alternatively, the cloud server application designs the encryption strategy based on the data type or data size associated with the file data and / or portions of the file data. In another embodiment, the cloud server application completes the encryption according to step 515 before further file data processing to avoid security risks associated with data interception, such as man-in-the-middle (MitM) attacks. According to such an additional embodiment, the cloud server application encrypts the file data before partitioning the file data according to the designed data distribution technology. Additionally or alternatively, the cloud server application encrypts the file data before performing any hash transformation. In a further embodiment, the cloud server application applies a first iteration of encryption to the entire file data and then applies one or more subsequent iterations of encryption to the corresponding portions of the file data to be distributed across layers of a multi-tiered storage infrastructure, as determined by an ensemble learning model. In a further embodiment, one or more encryption techniques associated with the encryption strategy include symmetric-key algorithms, such as Triple Data Encryption (TDES) and Advanced Encryption Standard (AES). According to such another embodiment, the cloud server application optionally encrypts file data by applying TDES. Additionally or alternatively, the cloud server application optionally encrypts file data by applying AES. In a further embodiment, the one or more encryption techniques include asymmetric-key algorithms, such as Rivest–Shamir–Adleman (RSA). In a further embodiment, the cloud server application applies multiple encryption algorithms to all or part of the file data to enhance security and / or privacy. According to such a further embodiment, the cloud server application optionally applies relatively stronger encryption algorithms and / or a larger number of encryption algorithms to file data and / or a portion of file data that is marked as confidential or otherwise associated with confidential data (as opposed to non-fidelity data).

[0063] At step 520, based on the data distribution technology designed at step 510, the cloud server application partitions the file data encrypted at step 515 for distribution and storage among storage layers of the multi-tiered storage infrastructure. According to step 520, the cloud server application implements the designed data distribution technology. In an embodiment, the cloud server application partitions the file data among the cloud computing layer, fog computing layer, and local computing layer of the multi-tiered storage infrastructure (e.g., cloud layer 403, fog layer 413, and local layer 423 of the multi-tiered storage infrastructure 400). In an alternative embodiment, the cloud server application partitions the file data among one or more intermediate layers other than the cloud layer, fog layer, and local layer. These one or more intermediate layers may include an intermediate layer between the cloud layer and the fog layer, optionally combining certain aspects of both. Additionally or alternatively, such one or more intermediate layers may include an intermediate layer between the fog layer and the local layer, optionally combining certain aspects of both. By partitioning the file data among storage layers, the cloud server application completes the distribution of the file data portion according to the designed data distribution technology.

[0064] In the context of partitioning the file data according to step 520, the cloud server application performs a hash transformation and applies at least one cyclic error correction code. The cloud server application performs the hash transformation via the application of one or more cryptographic hash functions. In one embodiment, the cloud server application performs the hash transformation by applying the MD5 message digest algorithm (MD5). In another embodiment, the cloud server application performs the hash transformation by applying the Secure Hash Algorithm 2 (SHA-2). In a further embodiment, the cloud server application performs the hash transformation by applying multiple cryptographic hash functions (e.g., a combined application of MD5 and SHA-2). The cloud server application applies one or more cryptographic hash functions to generate a hash value in the form of a bit array. In another embodiment, the cloud server application adds further security to the context of hash value generation by randomizing such a hash value by adding a random string (i.e., a salt) to the file data string to be hashed before generating the hash value. In a further embodiment, the cloud server application performs the hash transformation by separately hashing the file data partitioned between each storage layer of the multi-tiered storage infrastructure, thereby generating separate hash values ​​for the strings of file data portions distributed between these storage layers. Specifically, the cloud server application optionally generates a first set of hash values ​​for file data assigned to the cloud computing layer, a second set of hash values ​​for file data assigned to the fog computing layer, and a third set of hash values ​​for file data assigned to the local computing layer. According to such a further embodiment, the cloud server application optionally applies different corresponding cryptographic hash functions to generate corresponding sets of hash values ​​in response to different security requirements at the respective computing layers. In a further embodiment, the cloud server application integrates hash transformation and error correction codes into a single algorithm or process. In a further embodiment, the cloud server application applies the at least one cyclic error correction code by applying at least one error correction code from the Bosch-Chadhury-Hokungam (BCH) error correction code category (i.e., by applying at least one BCH error correction code). According to such another embodiment, the cloud server application optionally applies a Reed-Solomon subset of BCH error correction codes. (See also...) Figure 10 The method for dividing file data according to step 520 is described.

[0065] In one embodiment, the cloud server applies a holistic learning model, encrypts file data, and / or partitions file data across storage tiers by executing a bash script comprising a series of tasks and subtasks related to the method steps, according to steps 510-520. More specifically, the cloud server application encrypts file data by running such a bash script based on the output of the holistic learning model. In various embodiments, the bash script is a file comprising a series of commands to facilitate the execution of a command-line program. According to such embodiments, the bash script facilitates the execution of automation techniques that combine all aspects of the holistic learning process with encryption and file data partitioning. Therefore, the cloud server application may optionally execute a bash script to automate the process flow in the context of one or more embodiments. In another embodiment, the cloud server application also stores the unpartitioned file data intact in a hardware-based or virtualized data warehouse associated with the user's local machine (e.g., data warehouses 4351 to 4352 associated with one of the local machines 4201 to 420n within the tiered storage infrastructure 400). n (One of them). According to such an embodiment, a cloud server application can recover file data via a data warehouse in response to determining that segmented file data has been lost between one or more layers of a multi-tiered storage infrastructure.

[0066] In step 525, the cloud server application receives a data access request associated with file data. In an embodiment, the cloud server application receives the data access request via a client interface associated with a user. The client interface may optionally be the same interface used to receive file data in step 505, or alternatively, a separate client interface. In step 530, the cloud server application authenticates the data access request. The cloud server application may optionally authenticate using a directory server system (e.g., directory server system 350) associated with the managed service domain. In one embodiment, the cloud server application authenticates the data access request by applying one or more cryptographic hash functions, optionally in a manner similar to applying one or more cryptographic hash functions in the context of partitioning the file data in step 520. Optionally, if the data access request fails to authenticate, the cloud server application repeats step 530 or proceeds to the end of method 500. See also... Figure 11 The method for accessing data based on step 530 is described.

[0067] In step 535, the cloud server application recovers the file data via decryption. In one embodiment, the cloud server application recovers the file data segmented between storage tiers of a multi-tiered storage infrastructure in the same format as the original constructed format. Alternatively, the cloud server application recovers the file data in a modified format according to user preference, such as in a compressed format and / or an encoded format. In another embodiment, the cloud server application decrypts the file data by applying one or more decryption techniques corresponding to one or more encryption techniques applied at step 515, using a key. In yet another embodiment, in response to determining that file data is lost between one or more storage tiers (e.g., due to corruption of aspects of the data in one or more storage tiers), the cloud server application recovers the file data holistically by accessing a data warehouse containing the file data stored in an unsegmented form. In a further embodiment, the cloud server application performs one or more of steps 525-535 in a process separate from steps 505-520.

[0068] Figure 6 A method 600 for applying a holistic learning model is illustrated. According to one or more embodiments, method 600 provides sub-steps within the context of step 510 of method 500. Method 600 begins at step 605, where a cloud server application derives topic context data by applying NLP to contextual information associated with a user. According to step 605, the cloud server application applies NLP to the user context information to derive topic context data. The cloud server application applies one or more NLP models / algorithms to the user context information to derive topic context data. The derived topic context data includes topics and / or topic groups associated with the user context information. Such topics / topic groups may optionally include topics associated with one or more of a plurality of user context factors, such as topics associated with user application activities, topics associated with user data activities, topics associated with user resource usage or user storage patterns, and / or topics associated with user attributes such as user occupation, (one or more) user-affiliated organizations, or (one or more) user contacts. Optionally, the derived topic context data further includes topic metadata describing the association between the user and the topic / topic group and / or describing the association between the topics / topic groups. Specifically, topic metadata can describe the relationship information between the corresponding topic / topic group and user data, (or more) user applications and / or (or more) user attributes, such as date / time information or access information about user data involving security-related topics.

[0069] Cloud server applications utilize the unsupervised learning capabilities of an overall learning model through Natural Language Processing (NLP) to identify user contextual aspects within user context information, thereby identifying topic patterns within the user context information. In embodiments, user contextual aspects include data aspects parsed or otherwise obtained from one or more of a plurality of user contextual factors. In embodiments, the cloud server application may optionally apply NLP to the user context information by incorporating at least one Natural Language Understanding (NLU) technique. Additionally or alternatively, the cloud server application may apply NLP to the user context information by incorporating at least one Automatic Speech Recognition (ASR) technique. In a further embodiment, the cloud server application parses text from the textual aspect of the user context information to identify user contextual aspects for which NLP is applied. Additionally or alternatively, the cloud server application may identify user contextual aspects for NLP analysis purposes by applying audiovisual processing to the audiovisual aspect of the user context information. When identifying audio associated with a user (e.g., voice utterances of the user or associated contacts), the cloud server application may optionally apply speech recognition (e.g., speech-to-text) to derive a text-based aspect from the audio and subsequently apply NLP to the text-based aspect. Alternatively, when identifying visual images associated with a user (e.g., still images and / or videos of user activity or activity of associated contacts), the cloud server application may optionally apply video recognition (e.g., video-to-text) to derive a text-based aspect from the visual image and subsequently apply NLP to the text-based aspect. The cloud server application may optionally apply one or more other forms of audiovisual processing to audio and / or visual images within the user context information to identify further user context aspects. In a further embodiment, the cloud server application derives user context aspects for NLP analysis purposes by analyzing surveillance data collected in association with the user (e.g., after collecting Internet of Things (IoT) sensor data from multiple surveillance sensors attached to the user, attached to devices associated with the user, and / or installed in the environment associated with the user). The cloud server application may optionally apply NLP to the textual aspect derived from sensor data and / or sensor metadata.

[0070] In one embodiment, the cloud server application quantifies the relevance of topics among derived topic context data by assigning a corresponding user context score to each topic and / or each group of topics. The user context score indicates the relative importance of each topic and / or each group of topics to a user in terms of file storage access. The cloud server application may optionally prioritize the storage of data associated with topics and / or groups of topics having relatively high user context scores to facilitate relatively fast user access. Conversely, the cloud server application may optionally allow more flexible storage of data associated with topics and / or groups of topics having relatively low user context scores, for example, by allowing such data to be stored in locations with relatively low storage costs but resulting in relatively high data access latency. According to such an embodiment, the cloud server application may optionally assign the corresponding user context score on a predefined numerical scale (e.g., an integer scale from 0 to 100).

[0071] As further described herein, optionally, the cloud server application encodes feature vectors based on topic patterns identified within user context information, and derives topic context data based on the processed, encoded feature vectors (e.g., via the application of at least one clustering algorithm). In various embodiments, the feature vector is an n-dimensional representation of a data point in numerical format describing the corresponding feature of that data point. In the context of different embodiments, a data point is an entity represented within the data, such as an individual, organization, or application aspect. In the context of different embodiments, a feature is a measurable attribute or characteristic associated with a data point. See also... Figure 7 A method for obtaining topic context data according to step 605 is described.

[0072] In step 610, the cloud server application applies NLP to the file data based on the topic context data exported in step 605 to export multiple file data portions. According to step 610, the cloud server application applies NLP to the file data based on the exported topic context data to export multiple file data portions. Using the exported topic context data, including topics / topic groups associated with user context information, as input, the cloud server application applies the unsupervised learning capabilities of the overall learning model to aspects of the file data through NLP to identify topic patterns in the file data. In one embodiment, the cloud server application optionally applies NLP to the file data by combining at least one NLU technique. Additionally or alternatively, the cloud server application applies NLP to the file data by combining at least one ASR technique. In a further embodiment, the cloud server application parses text from the textual aspects of the file data to identify aspects of the file data to which NLP is applied. Additionally or alternatively, the cloud server application identifies aspects of the file data for NLP analysis purposes by applying audiovisual processing to audiovisual aspects within the file data. When identifying audio (e.g., spoken words or other audio sounds) within file data, the cloud server application may optionally apply speech recognition (e.g., speech-to-text) to identify text-based aspects from the audio and subsequently apply NLP to those text-based aspects. When identifying visual images within file data (e.g., still images and / or videos), the cloud server application may optionally apply video recognition (e.g., video-to-text) to identify text-based aspects from the visual images and subsequently apply NLP to those text-based aspects. The cloud server application may optionally apply one or more other forms of audiovisual processing to the audio and / or visual images within the file data to identify further aspects of the file data.

[0073] As further described herein, based on the topic patterns identified in the file data, the cloud server application may optionally create multiple file data portions and, for example, distribute data points within the file data to the multiple file data portions by applying at least one clustering algorithm. About Figure 9 A method for exporting multiple file data portions according to step 610 is described.

[0074] In step 615, the cloud server application applies at least one multi-class classification technique to multiple file data portions exported in step 610. According to step 615, the cloud server application associates the exported file data portions and / or their corresponding sub-portions with one or more of the multiple categories. Based on the topic tags determined via NLP applied in steps 605-610, the cloud server application applies the supervised learning capabilities of an ensemble learning model to associate the exported file data portions or their corresponding sub-portions with one or more of the multiple categories. In an embodiment, the cloud server application applies an ensemble of Random Forest (RFC) classifier decision trees. Alternatively or alternatively, the cloud server application applies a Support Vector Machine (SVM) model, optionally applying an SVM ensemble model. The cloud server application uses the exported file data portions as input to perform supervised learning. Based on the processing at the corresponding decision tree nodes associated with one or more of the at least one classification technique, the cloud server application generates an output including information identifying the association between the corresponding file data portion and one or more of the multiple categories. In related embodiments, the output includes numerical output (e.g., integer, binary, and / or one-hot encoded vector output) that corresponds to or otherwise references one or more specific categories among a plurality of categories for a corresponding file data portion or sub-portion. In further related embodiments, the output includes data relating to the distribution of file data portions based on multiple classes, such as statistics and / or metadata associated with the relationships between the multiple classes and the corresponding file data portions or sub-portions.

[0075] According to step 615, the cloud server application associates file data portions and / or their corresponding sub-portions with one or more categories based on one or more specific aspects of user context information, such as one or more of multiple user context factors. In one embodiment, the cloud server application categorizes file data portions by combining multiple user context aspects, including one or more aspects related to data usage frequency, data relevance, data type, data size, data complexity, data confidentiality, and / or data priority in the context of a specific user task. In a further embodiment, the cloud server application categorizes file data portions(s) based on association with corresponding data types. Specifically, the cloud server application may optionally categorize file data portions with the most frequently used data types (or data associated with the most frequently used applications) into one or more categories associated with the local layer, file data portions associated with less frequently used data types into one or more categories associated with the fog layer, and file data portions associated with the least frequently used data types into one or more categories associated with the cloud layer. Additionally or alternatively, the cloud server application categorizes file data portions based on data relevance. Specifically, the cloud server application may optionally categorize file data portions with data determined to be most relevant to user activity into one or more categories associated with the local tier, file data portions with less relevant data into one or more categories associated with the fog tier, and file data portions with minimally relevant data into one or more categories associated with the cloud tier. Additionally or alternatively, the cloud server application may categorize file data portions based on application relevance. Specifically, the cloud server application may optionally categorize file data portions related to applications most frequently used during user activity into one or more categories associated with the local tier, file data portions related to applications less frequently used during user activity into one or more categories associated with the fog tier, and file data portions related to applications least frequently used during user activity into one or more categories associated with the cloud tier. Additionally or alternatively, the cloud server application may categorize file data portions based on file size. Specifically, the cloud server application may optionally classify the portion of file data related to larger user files into one or more categories associated with the cloud layer, the portion of file data related to more compact user files into one or more categories associated with the fog layer, and the portion of file data related to the most compact user files into one or more categories associated with the local layer.

[0076] In another embodiment, the cloud server application categorizes less relevant, less compact, and / or less frequently used file data portions or sub-portions thereof into one or more categories associated with the cloud layer; more relevant, more compact, and / or more frequently used file data portions or sub-portions thereof into one or more categories associated with the fog layer; and the most relevant, most compact, and / or most frequently used file data portions or sub-portions thereof into one or more categories associated with the local layer. Alternatively, for accessibility purposes, the cloud server application may categorize file data portions with more complex and / or confidential characteristics into one or more categories associated with the cloud layer. Additionally or alternatively, the cloud server application may categorize file data portions with the highest relative priority in the context of the user task into categories associated with all storage layers to ensure user access to such file data portions in any data storage scenario. Additionally or alternatively, cloud server applications may classify portions or sub-portions of file data required for remote data processing and / or associated with remote data access availability into one or more categories associated with the cloud layer and / or into one or more categories associated with the fog layer. Additionally or alternatively, cloud server applications may classify portions or sub-portions of file data associated with local resources into one or more categories associated with the local layer.

[0077] In a further embodiment, according to step 615, the cloud server application classifies the corresponding file data portion and / or its sub-portions into one or more categories of a plurality of categories, at least in part, based on the corresponding user context score assigned to each topic or topic group in the exported topic context data. According to such a further embodiment, the cloud server application may optionally allocate the corresponding file data portion or its sub-portions associated with topics and / or topic groups having relatively high user context scores to one or more categories of a plurality of categories associated with relatively high storage priority, to facilitate relatively fast user access. Conversely, the cloud server application may optionally allocate the corresponding file data portion or its sub-portions associated with topics and / or topic groups having relatively low user context scores to one or more categories of a plurality of categories associated with more flexible storage settings.

[0078] The cloud server application classifies each portion of file data into one or more categories according to step 615 based on one or more classification techniques. In one embodiment, the one or more classification techniques classify relative to storage tiers (e.g., cloud, fog, and local tiers) associated with a multi-tiered storage infrastructure. According to a first classification technique, each of the multiple categories is associated with only one of these storage tiers. Specifically, a first category is associated with the cloud tier, a second category with the fog tier, and a third category with the local tier. According to the first classification technique, the cloud server application associates a portion of file data to be distributed entirely to a certain tier within the storage tiers with a single category (i.e., cloud tier, fog tier, or local tier) associated with that tier. For example, the cloud server application may associate a portion of file data to be distributed entirely to the cloud tier with the cloud tier category. Conversely, the cloud server application may distribute file data portions across multiple tiers based on tiers, associating them with multiple categories among multiple classes. In such cases, the cloud server application may subdivide such file data portions into corresponding sub-parts based on the associated categories. For example, a cloud server application can associate portions of file data to be distributed in clouds and fog with both cloud and fog categories. In this case, the cloud server application can identify cloud sub-parts of the file data based on their association with cloud categories, and can also identify fog sub-parts of the file data based on their association with fog categories.

[0079] According to the second classification technique, each of the multiple categories may optionally be associated with one or a combination of these storage layers. Specifically, the first category is associated with the cloud layer, the second with the fog layer, the third with the local layer, the fourth optionally with both the cloud and fog layers, the fifth optionally with both the cloud and local layers, the sixth optionally with both the fog and local layers, and the seventh optionally with all of the cloud, fog, and local layers. Similar to the first classification technique, the portion of file data that a cloud server application intends to distribute entirely to a particular layer within the storage layers is associated with a single category (i.e., cloud category, fog category, or local layer category) associated with that particular layer. Conversely, the portion of file data that a cloud server application intends to distribute across multiple layers is associated with a category reflecting each of the multiple layers. For example, a cloud server application may associate the portion of file data to be distributed across the cloud and fog layers with categories associated with both the cloud and fog layers.

[0080] In one embodiment, a cloud server application associates one or more numeric category values ​​with each of a plurality of file data portions and / or their respective sub-portions. According to such an embodiment, the cloud server application optionally creates an encoded feature vector comprising numeric data that associates the one or more numeric category values ​​with a corresponding file data portion among the plurality of file data portions and / or their sub-portions. In an alternative embodiment, according to one or more classification techniques, the cloud server application also classifies intermediate layers associated with a multi-tiered storage infrastructure, in addition to cloud, fog, and local layers. According to such an alternative embodiment, one or more classification techniques include corresponding categories associated with intermediate layers between cloud and fog layers and / or intermediate layers between fog and local layers.

[0081] In step 620, the cloud server application determines a distribution plan for multiple file data portions based on the application of at least one multi-class classification technology. The distribution plan determined in step 620 is an implementation of the data distribution technology designed after step 510. In one embodiment, the cloud server application determines the distribution plan based on the classification of file data portions via at least one multi-class classification technology. Based on the computational intelligence discussed previously, the cloud server application calculates the distribution proportion of file data stored at corresponding tiers (e.g., cloud, fog, and local tiers) of a multi-tiered storage infrastructure. The cloud server application may optionally calculate the allocation proportion based on the classification of file data portions into multiple categories. In another embodiment, the cloud server application includes a process of allocating file data to corresponding buckets within the cloud, fog, and local tiers in the allocation plan. According to such an additional implementation, the cloud server application may optionally specify an allocation order that specifies the order in which file data is allocated to the corresponding buckets. Depending on a variation in the allocation order, the cloud server application may first allocate file data to cloud buckets, then to fog buckets, and then to local tier buckets. Depending on the alternative distribution order, a cloud server application can first distribute file data to a cloud bucket, then to a local bucket, and finally to a fog bucket. Alternatively, the distribution order can be: First, distribute file data to a fog bucket, then to a cloud bucket, and finally to a local bucket. If the multi-tiered storage infrastructure includes a middle tier, the distribution order can optionally merge the corresponding buckets within the middle tier, in addition to the cloud bucket, fog bucket, and local bucket. In a further embodiment, the cloud server application appends holistic learning metadata to the file data based on the NLP and multi-class classification applied in steps 605-615. According to such a further embodiment, the cloud server application may optionally utilize the holistic learning metadata to determine the distribution ratio between the tiers of a multi-tiered storage infrastructure. The holistic learning metadata may optionally incorporate information related to the file data portion derived in step 610. Information associated with the corresponding file data portion optionally incorporated into the integrated learning metadata includes the file data portion size, the data type associated with the file data portion, any applications associated with the file data portion, the topic classification associated with the file data portion, and / or the class associated with the file data portion.

[0082] In step 625, the cloud server application trains an ensemble learning model based on the NLP and multi-class classification applied in steps 605-615. According to step 625, the cloud server application updates the ensemble learning model based on aspects derived and / or determined by the model application. In one embodiment, the cloud server application stores one or more topic context data aspects based on the topic context data derived in step 605 for future analysis about users with similar data usage and / or application usage characteristics. According to such an embodiment, the cloud server application optionally analyzes one or more topic context data aspects and, based on such analysis, identifies and stores topic context data patterns (e.g., topic patterns based on one or more of multiple user context factors). The cloud server application optionally utilizes the stored topic context data patterns to facilitate future topic context data processing. In another embodiment, the cloud server application stores one or more file data aspects based on multiple file data portions derived at step 610 to facilitate future organization of file data portions associated with users or users with similar data and / or application usage characteristics. According to such an additional embodiment, the cloud server application optionally analyzes one or more aspects of file data and, based on such analysis, identifies and stores file data patterns (e.g., subject patterns or other file data patterns in the file data). The cloud server application optionally utilizes the stored file data patterns to facilitate future file data processing. In a further embodiment, the cloud server application stores one or more classification aspects determined at step 615 regarding the derived plurality of file data portions. According to such another embodiment, the cloud server application optionally analyzes one or more classification aspects and, based on such analysis, identifies and stores classification patterns that associate one or more categories with corresponding file data portions among the plurality of file data portions and / or their sub-portions. Specifically, the cloud server application optionally stores encoded feature vectors that associate numeric category values ​​with corresponding file data portions among the plurality of file data portions and / or their sub-portions. The cloud server application optionally utilizes the stored classification patterns to facilitate future multi-class classification processing. In a further embodiment, the cloud server application trains the ensemble learning model based on the created ensemble learning metadata and / or based on the patterns determined in the context of method 600, and optionally stores such ensemble learning metadata and / or patterns in a knowledge base associated with the ensemble learning model.

[0083] In summary, applying the ensemble learning model according to method 600 includes deriving topic context data by applying NLP to contextual information associated with the user, deriving multiple file data portions by applying NLP to file data based on the topic context data, applying at least one multi-class classification technique to the multiple file data portions, determining a distribution plan for the multiple file data portions based on the application of the at least one multi-class classification technique, and training the ensemble learning model based on the applied NLP and the applied at least one multi-class classification technique.

[0084] Figure 7 A method 700 for obtaining topic context data is illustrated. According to one or more embodiments, method 700 provides a sub-step within the context of step 605 of method 600. Method 700 begins at step 705, wherein a cloud server application creates multiple encoded feature vectors by applying at least one NLP model to raw data associated with a user. According to step 705, the cloud server application applies at least one NLP model to the raw data associated with the user to create multiple encoded feature vectors. The cloud server application creates multiple encoded feature vectors based on topic patterns identified in the raw data. The raw data associated with the user optionally includes unstructured or structured data within user context information that has not undergone NLP processing. In one embodiment, in the context of applying at least one NLP model in step 705, the cloud server application applies a Named Entity Recognition (NER) model to the user context aspect. This NER model processes user context information by recognizing and classifying entities in the raw text of the raw data based on topics. The application of the NER model produces at least one encoded topic assignment vector. In another embodiment, in a context where at least one NLP model is applied, the cloud server application applies Latent Dirichlet Allocation (LDA) to the user context to identify relationships between topics and text. According to such an additional embodiment, the cloud server application may optionally apply LDA modeling to the raw text in the raw data to perform topic classification on data points represented in the raw text. The application of the LDA model results in at least one encoded topic assignment vector. In another embodiment, in a context where at least one NLP model is applied, the cloud server application applies a bidirectional encoder representation from a transformer (BERT) to the user context. When applying BERT, the cloud server application applies it to the raw text in a transformer-based NLP that utilizes neural network attention techniques. By utilizing the attention of the neural network, the cloud server application enhances its focus on important aspects of the raw text input to perform sentence embedding for such input. The application of the BERT model results in at least one encoded real-valued vector based on the sentence embedding. In the context of the various embodiments, sentence embedding includes a set of techniques for mapping sentences to real-valued vectors.

[0085] In a further embodiment, in a context where at least one NLP model is applied, the cloud server application applies a recurrent neural network (RNN) model to user context aspects to establish machine learning (deep learning)-based connections between sequential data points associated with user context information. The application of the RNN model results in at least one encoded feature vector reflecting a combination of topic and time series. According to such a further embodiment, the cloud server application optionally utilizes a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) architecture configured to store time-series pattern data about textual aspects associated with raw data or, more generally, user context information. The cloud server application optionally applies LSTM-RNN modeling for the purpose of storing time-series pattern data. In the context of LSTM-RNN modeling, the cloud server application stores usage characteristics as a function of time for each user context aspect that can be used as input to one or more of at least one NLP model. Based on the usage characteristics about user context aspects that change over time, the cloud server application optionally uses the LSTM-RNN data to determine patterns between user context aspects. The determined patterns between user context aspects optionally reflect user usage over time of one or more previously used (e.g., recently used or most recently used within a predetermined time period) applications, data types, or system resources. Specifically, using LSTM-RNN modeling, the cloud server application optionally derives at least one timestamped pattern relative to one or more user context aspects, thereby identifying captured data usage patterns, captured application usage patterns, and / or captured system resource usage patterns based on timestamps. The application of the LSTM model results in an encoded time-series pattern vector, and furthermore, the application of LSTM-RNN modeling results in encoded feature vector information reflecting the topic and time series. In a further embodiment, the cloud server application applies NLP to user context aspects within the raw data or more generally combines user context information from a Gated Recurrent Unit (GRU) architecture. Based on fewer parameters than LSTM, the application of GRU produces an encoded time-series pattern vector. Optionally, in scenarios where at least one NLP model is applied, the cloud server application combines features from one or more of the aforementioned models as described above.

[0086] In a further embodiment, in step 705, the cloud server application assigns a weight value to each of the plurality of encoded feature vectors to specify the relative importance of the feature vectors created by applying at least one of the corresponding NLP models. For example, the cloud server application may assign a relatively high weight value to NEP-related encoded feature vectors and a relatively low weight value to LDA-related encoded feature vectors to indicate that, in some cases, the application of the NEP model has a higher relative importance than the application of the LDA model. In a further embodiment, the cloud server application creates the plurality of encoded feature vectors by deriving data representations of application type or data type accessed by the user, the frequency of application access or user data access, computing resource usage associated with application access or user data access, and / or storage patterns associated with application access or user data access. (Refer to...) Figure 8 A method for creating multiple encoded feature vectors according to step 705 is described.

[0087] In step 710, the cloud server application obtains a digital topic output by applying at least one clustering algorithm to multiple encoded feature vectors created in step 705. According to step 710, the cloud server application applies at least one clustering algorithm to multiple encoded feature vectors to obtain the digital topic output. Multiple encoded feature vectors or aspects thereof created at step 705 are input to at least one clustering algorithm. In one embodiment, the cloud server application concatenates one or more of the multiple encoded feature vectors before applying the at least one clustering algorithm. In another embodiment, the cloud server application associates a corresponding feature of one of the multiple encoded feature vectors with a corresponding topic based on the digital topic output obtained for the corresponding feature after applying the at least one clustering algorithm. For example, the cloud server application may associate the corresponding feature of obtaining a digital topic output value "1" with a security-related topic, the corresponding feature of obtaining a digital topic output value "2" with a topic involving client participation, the corresponding feature of obtaining a digital topic output value "3" with a topic involving quantum computing, and so on. According to such an additional embodiment, the cloud server application associates data points in the raw data associated with the user with one or more corresponding topics based on topic associations, such as those representing data point features as created in step 705 within an encoded feature vector. For example, the cloud server application may associate data points in the raw data with security-related topics and client-engagement-related topics based on data points having features associated with security-related topics and client-engagement-related topics, as determined by the corresponding digital topic output values ​​obtained for such features. According to such an additional embodiment, the cloud server application may optionally derive the digital topic output values ​​of the data points from the raw data by concatenating or otherwise processing the corresponding digital topic output values ​​of the corresponding features of the data points. These digital topic output values ​​of the data points may optionally be represented as feature vectors, i.e., cluster-derived supplements representing the encoded feature vectors of the data points as created in step 705. In a further embodiment, the cloud server application may optionally encode the corresponding values ​​of the digital topic outputs in binary form or in a one-hot encoded form. In a further embodiment, at least one clustering algorithm applied in the context of step 710 includes the k-means clustering algorithm. Additionally or alternatively, the at least one clustering algorithm includes an expectation-maximization algorithm. Additionally or alternatively, the at least one clustering algorithm includes a hierarchical clustering algorithm, such as agglomerative hierarchical clustering. Additionally or alternatively, the at least one clustering algorithm includes a mean-shift clustering algorithm. Based on the encoding and clustering in steps 705-710, the cloud server application associates features from the user context information with digital subject information. The cloud server application can then utilize this digital subject information to process and classify file data received from the user.

[0088] In summary, deriving topic context data according to method 700 involves creating multiple encoded feature vectors by applying at least one NLP model to raw data associated with the user and obtaining digital topic outputs by applying at least one clustering algorithm to the multiple encoded feature vectors.

[0089] Figure 8 A method 800 for creating multiple encoded feature vectors is illustrated. According to one or more embodiments, method 800 provides a sub-step within the context of step 705 of method 700. Method 800 begins at step 805, where a cloud server application derives at least one data representation based on the application type or data type accessed by the user. In implementations, the encoded vector representation includes an encoded set of feature vectors. The encoded set of feature vectors includes one or more encoded feature vectors. Optionally, the cloud server application derives the encoded set of feature vectors by deriving binary vectors (e.g., bit arrays) based on an application access history derived via one-hot encoding. According to such embodiments, one-hot encoding enables the representation of categorical variables in binary vector format. Additionally or alternatively, the cloud server application derives the encoded feature set via word embedding. In the context of different embodiments, word embedding refers to assigning corresponding values ​​to text words based on context, such that the corresponding values ​​of relatively relevant words are more closely related in value than the corresponding values ​​of relatively unrelated words. In related embodiments, the cloud server application applies word embedding to derive an encoded feature set representing relatively more relevant words that are closer in real-valued vectors. In another related embodiment, the cloud server application applies word embeddings to derive an encoded feature set representing a relatively more relevant data type associated with user context information that is closer to a real-valued vector. In a further embodiment, the cloud server application associates data points with data types in attribute-value pair format.

[0090] In step 810, the cloud server application exports at least one data representation related to the frequency of application access or user data access. In an embodiment, the cloud server application encodes user access regarding a specific application or a specific data point in an attribute-value pair format. Such an attribute-value pair format can associate a specific application / data point with metadata scalar or vector values ​​describing the frequency of application / data usage, the date / time of application / data usage, and / or other aspects of application / data usage. In a further embodiment, the cloud server application encodes user access for a specific data type in the attribute-value pair format. Such an attribute-value pair format can associate a specific data type with metadata vector values ​​describing the frequency of data type usage, the date / time of data type usage, and / or other aspects of data type usage.

[0091] In step 815, the cloud server application derives at least one data representation relating to computing resource usage and / or storage patterns related to application access or user data access. In embodiments, the cloud server application collects information relating to historical storage patterns of user data access. For example, the cloud server may record storage patterns reflecting the storage of frequently used user application data across all storage tiers. The cloud server application may optionally store such resource usage information and / or storage pattern information in a standardized encoding format (e.g., binary vector format and / or attribute value pair format). The cloud server application combines the data representation or aspects thereof derived in steps 805-815 into one or more of a plurality of encoded feature vectors. Depending on the embodiment, the cloud server application may optionally perform a subset of steps 805-815 and / or optionally perform steps 805-815 in any order.

[0092] In summary, creating multiple encoded feature vectors according to method 800 includes deriving at least one data representation based on the application type or data type accessed by the user, deriving at least one data representation relating to the frequency of application access or data access by the user, and deriving at least one data representation relating to the computing resource usage or storage patterns associated with application access or data access by the user.

[0093] Figure 9A method 900 for exporting multiple portions of file data is illustrated. According to one or more embodiments, method 900 provides a sub-step within the context of step 610 of method 600. Method 900 begins at step 905, where a cloud server application identifies a theme pattern among data points within the file data by considering the theme context data exported according to step 605 and applying at least one NLP model. According to step 905, the cloud server application considers the theme context data and applies at least one NLP model to identify theme patterns. In an embodiment, the cloud server application considers the theme context data to export theme information related to the file data. According to such an embodiment, the cloud server application may optionally identify portions of file data by associating data points within the file data with themes or groups of themes within the theme context data. Specifically, based on the theme context data, the cloud server application applies at least one NLP model to the file data to determine theme patterns among data points within the file data. In a related embodiment, the cloud server application associates corresponding features of file data points with corresponding themes based on the identified theme patterns. For example, the cloud server application may associate features of file data points with security-related themes in response to identifying a pattern between such features and corresponding features in the theme context data associated with such security-related themes. According to this implementation, a cloud server application may optionally associate file data points with respective topics based on the topic associations of the characteristics of these file data points. For example, in response to determining that the characteristics of a given file data point are associated with security-related topics and client engagement-related topics based on identified topic patterns, the cloud server application may associate the given file data point with security-related topics and client engagement-related topics.

[0094] In one embodiment, in the context of applying at least one NLP model in step 905, the cloud server application applies a NER model to the file data aspect of the file data. Such an NER model extracts information from the file data based on topic context data, using topic identifiers and categorized entities. Based on the topic identifiers via the NER model, the cloud server application optionally identifies all data points or subsets of data points within the topic context data related to the file data. In another embodiment, in the context of applying at least one NLP model, the cloud server application applies LDA to identify topics and relationships in the text within the file data. According to such a further embodiment, the cloud server application optionally applies LDA modeling to the file data aspect based on topic context data to categorize the data points represented within the file data on topics. In a further embodiment, in the context of applying at least one NLP model, the cloud server application applies BERT to the file data aspect based on topic context data. When applying BERT, the cloud server application applies it to the text within a file data transformer-based NLP using neural network attention techniques. By utilizing neural network attention, the cloud server application enhances its focus on important aspects of the original text input to perform sentence embedding for such input.

[0095] In another embodiment, within the context of applying at least one NLP model, the cloud server application applies an RNN model to establish machine learning-based connections between sequential data points associated with file data, given topic context data. According to such a further embodiment, the cloud server application optionally utilizes an LSTM-RNN architecture configured to store time-series pattern data about aspects of the file data. The cloud server application optionally applies LSTM-RNN modeling for the purpose of storing time-series pattern data. In the context of LSTM-RNN modeling, the cloud server application stores usage characteristics of each aspect of the file data, which can be used as input to one or more of at least one NLP model, as a function of time. Based on the usage characteristics of the file data aspects over time, the cloud server application optionally uses the LSTM-RNN data to predict patterns between aspects of the file data, such as any deterministic relationship between file data patterns and user usage patterns over time. Specifically, using LSTM-RNN modeling, the cloud server application optionally derives at least one timestamped pattern about one or more aspects of the file data. The cloud server application can optionally identify relationships between one or more aspects of file data and one or more previously used applications, data points, or system resources, and can therefore associate such one or more aspects of file data with usage patterns recorded over time relative to one or more previously used applications, data points, or system resources. Thus, in the context of deriving a portion of file data, the cloud server application can associate such one or more aspects of file data with one or more portions of file data based at least in part on such usage patterns. For example, in response to determining that a aspect of file data is associated with an application previously used by a user (e.g., recently used or most recently used within a predetermined time period), the cloud server application can associate such aspect of file data with one or more portions of file data reflecting compatibility or other associations with such previously used applications. In a further embodiment, the cloud server application applies NLP to aspects of file data in conjunction with a GRU architecture based on topic context data. The cloud server application can optionally combine features of one or more of the above models during the application of NLP techniques. Based on the application of at least one NLP model based on topic context data, the cloud server application associates each data point within the file data with one or more identified topic patterns.

[0096] In step 910, the cloud server application assigns data points within the file data to multiple file data portions by applying at least one clustering algorithm based on the topic patterns identified in step 905. According to step 910, the cloud server application applies at least one clustering algorithm based on the identified topic patterns to assign data points within the file data to multiple file data portions. Based on the identified topic patterns associated with data points within the file data (which are optionally represented in the form of encoded feature vectors), the cloud server application applies at least one clustering algorithm to assign data points to multiple file data portions based on topic pattern associations between data points. The cloud server application may optionally organize each file data portion such that the corresponding data points within each file data portion have certain topic pattern correlations as determined by at least one clustering algorithm. In one embodiment, the at least one clustering algorithm applied in the context of step 910 includes a k-means clustering algorithm. Additionally or alternatively, the at least one clustering algorithm includes an expectation-maximization algorithm. Additionally or alternatively, the at least one clustering algorithm includes a hierarchical clustering algorithm, such as agglomerative hierarchical clustering. Furthermore or alternatively, the at least one clustering algorithm includes a mean-shift clustering algorithm. In a further embodiment, the cloud server application assigns a numerical value, such as an integer value, a binary value, or a one-hot encoded value, to each of the plurality of file data portions. Optionally, the cloud server application assigns a numerical value to each of the plurality of file data portions based on a corresponding numerical value associated with a corresponding aspect within the topic context data derived in step 605. For example, based on the foregoing example discussed with respect to step 710, the cloud server application may assign the numerical value "1" to a file data portion that includes file data points and their security-related features, assign the numerical value "2" to a file data portion that includes data points and their features related to client participation, assign the numerical value "3" to a file data portion that includes data points and their features related to quantum computing, and so on. In response to determining that a corresponding file data portion includes file data points and their features related to multiple topics, the cloud server application may optionally assign multiple numerical values ​​to such file data portions corresponding to multiple topics, or alternatively assign a single numerical value to such file data portions corresponding to the most relevant topic among the multiple topics.

[0097] In summary, deriving multiple file data portions according to method 900 includes identifying topic patterns in data points within the file data by applying at least one NLP model considering topic context data, and assigning data points within the file data to multiple file data portions by applying at least one clustering algorithm based on the identified topic patterns.

[0098] Figure 10A method 1000 for partitioning file data is illustrated. According to one or more embodiments, method 1000 provides sub-steps within the context of step 520 of method 500. Method 1000 begins at step 1005, where a cloud server application stores separate portions of file data across a multi-tiered storage infrastructure, including a cloud computing layer, a fog computing layer, and a local computing layer. In the context of different embodiments, storing separate portions means storing non-redundant components. In one embodiment, the cloud server application stores the partitioned data between storage layers in buckets at the respective storage layers. According to such an embodiment, the cloud server application stores cloud data in a cloud server bucket at the cloud layer, fog data in a fog server bucket at the fog layer, and local data in a local machine bucket at the local layer. Optionally, the cloud server application indexes the corresponding buckets based on bucket key values, such that portions or sub-portions of file data with the same bucket key value are stored in a single bucket. The bucket key value of the corresponding bucket may optionally correspond to or otherwise relate to a category value assigned to a portion or sub-portion of file data. The cloud server application may optionally specify or retrieve access control data for each bucket. Bucket access control can be specified for a bucket via a corresponding access control list (ACL). In another embodiment, the cloud server bucket is the primary bucket, i.e., the location where the cloud server application initiates the partitioning of file data according to the data distribution technology designed in step 510. According to this further embodiment, the cloud server application performs corresponding partitioning on the file data located in the primary bucket so that individual portions of the file data are stored in buckets corresponding to other storage layers. The cloud server application may optionally perform the corresponding partitioning sequentially or alternatively perform the corresponding partitioning simultaneously. In an alternative embodiment, in step 1005, the cloud server application stores individual portions of the file data in one or more intermediate layers of a multi-tiered storage infrastructure other than the cloud layer, fog layer, and local layer. According to this alternative embodiment, the cloud server application may optionally store data categorized for storage in the intermediate layers in intermediate buckets associated with such intermediate layers.

[0099] In step 1010, the cloud server application stores redundant dependencies between each of the cloud computing layer, fog computing layer, and local computing layer of the multi-tiered storage infrastructure. Redundant dependencies stored between layers include libraries or portions / sub-parts thereof related to the execution of file data. Alternatively, redundant dependencies may include application packages associated with file data or portions / sub-parts thereof. Additionally or alternatively, redundant dependencies may contain information linking corresponding portions or sub-parts of file data, for example, to facilitate file data recovery or transfer. In an alternative embodiment, in step 1010, the cloud server application stores redundant dependencies between one or more intermediate layers of the multi-tiered storage infrastructure other than the cloud computing layer, fog computing layer, and local computing layer.

[0100] In summary, partitioning file data according to method 1000 involves storing individual portions of the file data between the cloud computing layer, the fog computing layer, and the local computing layer, and storing redundant dependencies between each of the cloud computing layer, the fog computing layer, and the local computing layer.

[0101] Figure 11 A method 1100 for authenticating a data access request is illustrated. According to one or more embodiments, method 1100 provides sub-steps within the context of step 530 of method 500. Method 1100 begins at step 1105, where a cloud server application parses an identifier and a password from the data access request. In step 1110, the cloud server application extracts a stored hash value associated with the user based on the identifier parsed in step 1105. In an embodiment, the cloud server application searches for and retrieves the stored hash value by interacting with a directory server system associated with the managed service domain. In step 1115, the cloud server application hashes the password parsed in step 1105 by applying one or more cryptographic hash functions corresponding to the stored hash value to the parsed password. In one embodiment, the cloud server application communicates with the directory server system to facilitate the application of one or more cryptographic hash functions. In step 1120, the cloud server application determines whether the result of the cryptographic hash matches the stored hash value. In response to determining that the result of the cryptographic hash matches the stored hash value, in step 1125, the cloud server application completes the authentication of the data access request. Conversely, in response to a mismatch between the determined password hash and the stored hash value, the cloud server application continues until the end of method 1100 without completing the authentication of the data access request.

[0102] In summary, authenticating a data access request according to method 1100 includes parsing an identifier and password from the data access request, extracting a stored hash value associated with the user based on the parsed identifier, hashing the password by applying one or more password hash functions corresponding to the stored hash value to the parsed password, and completing the authentication of the data access request in response to determining that the result of the password hash matches the stored hash value.

[0103] Various embodiments of the invention have been described for illustrative purposes, but are not intended to be exhaustive or limited to the disclosed embodiments. All kinds of modifications to the described embodiments and equivalent arrangements should fall within the scope of this invention. Therefore, the scope of the invention should be interpreted most broadly according to the following claims relating to the specific embodiments, and should cover all possible equivalent variations and arrangements. Many modifications and variations will be apparent to those skilled in the art without departing from the scope of the described embodiments. The terminology used herein has been chosen to best explain the principles of the embodiments, their practical application, or technical improvements superior to those found in the market, or to enable those skilled in the art to understand the embodiments described herein.

Claims

1. A computer implementation method, comprising: Receive file data associated with the user and store it in the management service domain; An ensemble learning model is applied to design a data distribution technology for the file data based on contextual information associated with the user, wherein applying the ensemble learning model includes: Topic context data is derived by applying Natural Language Processing (NLP) to the context information; and Multiple file data portions are derived by applying NLP to the file data based on the topic context data; Encrypt the file data; and Based on the data distribution technology, the file data is segmented and stored in the cloud computing layer, fog computing layer and local computing layer by performing hash transformation and applying at least one cyclic error correction code.

2. The computer-implemented method according to claim 1, further comprising: Receive a data access request associated with the file data; Authenticate the data access request; as well as File data can be recovered by decryption.

3. The computer-implemented method according to claim 1, wherein, The application of ensemble learning models further includes: At least one multi-category classification technique is applied to the multiple file data portions.

4. The computer-implemented method according to claim 3, wherein, The application of the ensemble learning model further includes: The distribution plan for the multiple file data portions is determined based on the application of at least one multi-category classification technique.

5. The computer-implemented method according to claim 1, wherein, Obtaining the topic context data includes: Multiple encoded feature vectors are created by applying at least one NLP model to the raw data associated with the user; and Digital topic output is obtained by applying at least one clustering algorithm to the plurality of encoded feature vectors.

6. The computer-implemented method according to claim 5, wherein, Creating the multiple encoded feature vectors includes: Derive at least one data representation based on the application type or data type accessed by the user.

7. The computer-implemented method according to claim 5, wherein, Creating the multiple encoded feature vectors includes: Export at least one data representation related to the frequency of a user's application access or data access; and Derive at least one data representation relating to computing resource usage or storage patterns that are related to a user’s application access or data access.

8. The computer-implemented method according to claim 5, wherein, A corresponding feature of one of the plurality of encoded feature vectors is associated with a corresponding topic based on the digital topic output obtained for the corresponding feature.

9. The computer-implemented method as described in claim 1, wherein, The section on exporting multiple file data includes: By considering topic context data, at least one NLP model is applied to identify topic patterns within data points of the document data; and At least one clustering algorithm is applied based on the identified topic patterns to distribute data points within the file data to the multiple file data portions.

10. The computer-implemented method according to claim 1, wherein, The split file data includes: Different parts of the file data are stored in the cloud computing layer, the fog computing layer, and the local computing layer.

11. The computer-implemented method according to claim 1, wherein, The split file data includes: Redundant dependencies are stored in each of the cloud computing layer, the fog computing layer, and the local computing layer.

12. The computer-implemented method according to claim 2, wherein, Authentication data access requests include: Parse the identifier and password from the data access request; Based on the parsed identifier, obtain the stored hash value that is pre-associated with the user; The password is hashed by applying one or more cryptographic hash functions corresponding to the stored hash value to the parsed password; and The authentication of the data access request is completed in response to the determination that the result of the cryptographic hash matches the stored hash value.

13. A computer program product comprising program instructions executable by a computing device to cause the computing device to: Receive file data associated with the user and store it in the management service domain; An ensemble learning model is applied to design a data distribution technology for the file data based on contextual information associated with the user, wherein, The application of the ensemble learning model includes: Topic context data is derived by applying Natural Language Processing (NLP) to the context information; and Multiple file data portions are derived by applying NLP to the file data based on the topic context data; Encrypt the file data; and Based on the data distribution technology, the file data is segmented and stored in the cloud computing layer, fog computing layer and local computing layer by performing hash transformation and applying at least one cyclic error correction code.

14. The computer program product according to claim 13, wherein, The program instructions further enable the computing device to: Receive a data access request associated with the file data; Authenticate the data access request; and File data can be recovered by decryption.

15. A system comprising: At least one processor; as well as The memory stores an application program that performs operations when executed on the at least one processor, the operations including: Receive file data associated with the user and store it in the management service domain; An ensemble learning model is applied to design a data distribution technology for the file data based on contextual information associated with the user, wherein applying the ensemble learning model includes: Topic context data is derived by applying Natural Language Processing (NLP) to the context information; and Multiple file data portions are derived by applying NLP to the file data based on the topic context data; Encrypt the file data; and Based on the data distribution technology, the file data is segmented and stored in the cloud computing layer, fog computing layer and local computing layer by performing hash transformation and applying at least one cyclic error correction code.

16. The system according to claim 15, wherein, The operation further includes: Receive a data access request associated with the file data; Authenticate the data access request; and File data can be recovered by decryption.

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