System and method for spatiotemporal data partitioning in blockchain networks

Abstract

The present disclosure relates to a system and method for spatio-temporal data partitioning in a blockchain network (BC-NW). The system receives blockchain data comprising information associated with a task for recording in the BC-NW, and determines a time dimension and a space dimension from the received blockchain data to analyze a metric or usage pattern in various time windows of a predefined time. The system assigns various levels of hierarchical structures to the spatio-temporal partitions in the BC-NW based on the determined time dimension and space dimension. The system aggregates the arrival data processing rates of the tasks to achieve across the assigned space-time partitions for a single space-time partition, and processes the data in the hierarchical structure of the BC-NW across the space-time partitions if the processing of the aggregated arrival data should be performed across one or more space-time partitions.

Description

Technical Field

[0001] Reservation of Rights Part of the disclosure of this patent document includes materials that are subject to intellectual property rights, such as copyrights, designs, trademarks, IC layout designs, and / or trade dress protection, belonging to Jio Platforms Limited (JPL) or its related companies (hereinafter referred to as the present owner), but not limited to these. The present owner does not object to any copying by any person of the patent document or patent disclosure as described in the patent file or record of the Patent and Trademark Office, but in other cases, all rights are reserved. All rights to such intellectual property are fully reserved by the present owner.

[0002] Embodiments of the present disclosure generally relate to dividing data in a blockchain network. More specifically, the present disclosure relates to systems and methods for spatio-temporal data division in a blockchain network.

Background Art

[0003] The following description of related technologies is intended to provide background information related to the field of the present disclosure. This section may include specific aspects of the art related to various features of the present disclosure. However, it should be understood that this section is used only to deepen the reader's understanding of the present disclosure and is not used as an admission of prior art.

[0004] Generally, a blockchain network can be a distributed ledger network that records transactions between one or more participants without a central authority to authenticate transactions. A company may have multiple private blockchain networks, each specialized for a specific use case, department, or business vertical. In a traditional configuration of a blockchain network, the ledger can be distributed across multiple nodes, each node maintaining a complete copy. The size of the ledger increases with the number of transactions in the network, resulting in each node in the blockchain network having to process and store more data. In such cases, adding nodes exacerbates the problem, as the verification process can be time-consuming. To support a large number of devices and blockchain transactions, blockchain networks may need to be highly scalable, high-performance, secure, and support excessively frequent micropayments. Furthermore, techniques such as sharding may have been developed in the past to scale blockchain networks. Sharding techniques can be mechanisms for distributing workload processing and data storage within a blockchain network to reduce the transaction load on each node in the blockchain network. In particular, sharding can divide data into smaller chunks called shards across multiple servers, with each node assigned to an individual shard after division. Therefore, each node only needs to process a portion of the total data on the blockchain network, which can enable parallel processing of data. As a result, the number of transactions in a given period can be increased. Thus, sharding is effective in improving the performance of the blockchain network while reducing the amount of data processed at each node within the blockchain network, thereby scaling the blockchain network.

[0005] However, in sharding techniques, each shard can function as an individual blockchain network rather than a segment of a larger system, and communication between shards can be difficult because special development work is required to implement communication mechanisms. Even with such mechanisms, inter-shard communication can incur significant overhead. Therefore, users and applications in one domain may not be able to communicate with users and applications in another domain. Furthermore, sharding techniques may also have security issues, as a single shard can be easily hacked due to the reduced hash power required to control individual segments. Depending on the implementation of sharding techniques, users may not be able to download and verify the entire transaction history, thereby compromising reliability and immutability. Another challenge of blockchain sharding techniques can be related to consensus and verification. Various blockchain methods rely on different algorithms to reach consensus among nodes. The impact of such algorithms may also depend on the implementation of the sharding technique. There may not be such a standardized technique for implementing sharding techniques. The above problems can be mitigated by increasing the block size. With a larger block size, more transactions can be executed against the block. However, as block sizes increase, more computing power is required to verify the blocks. A non-patent document titled "Towards Scaling Blockchain Systems via Sharding" by H. Dang et al., ACM Sigmod 2019, describes a technique for scaling blockchain systems via sharding. This document identifies problems arising from fundamental differences in failure models between traditional distributed databases and blockchain systems.To overcome such problems, the cited document proposes methods for improving the performance of consensus protocols and designing efficient sharding protocols and general distributed transaction protocols. Furthermore, the document proposes an extensive evaluation of the designs on both multiple platforms. However, the document does not provide an effective solution to overcome the aforementioned challenges. In addition, a non-patent document titled "On Sharding Open Blockchains with Smart Contracts" by Y Tao et al., 2020, IEEE 36th International Conference on Data Engineering (ICDE), describes a novel distributed dynamic sharding system for significantly improving the throughput of blockchain systems based on smart contracts. In particular, the document proposes an inter-shard merging algorithm with an incentive to encourage merging smaller shards to form larger shards. However, the document does not provide an effective solution to overcome the aforementioned challenges.

[0006] Therefore, in this field of technology, there is a need to provide robust, reliable, and efficient systems and methods for partitioning spatiotemporal data in blockchain networks. [Prior art documents] [Non-patent literature]

[0007] [Non-Patent Document 1] "Towards Scaling Blockchain Systems via Sharding" by H. Dang et al., ACM Sigmod 2019. [Non-Patent Document 2] "On Sharding Open Blockchains with Smart Contracts" by Y Tao et al., 2020, IEEE 36th International Conference on Data Engineering (ICDE). [Overview of the project] [Problems that the invention aims to solve]

[0008] Some of the purposes of this disclosure that can be satisfied by at least one embodiment described herein are listed below.

[0009] In a general form, this disclosure provides a system and method for partitioning spatiotemporal data in a blockchain network.

[0010] In one embodiment, the object of the present disclosure is to enable faster and more simultaneous processing across the entire spatiotemporal division.

[0011] In another aspect, the object of this disclosure is to provide a system and method that operates under a variable data processing rate, enabling the dynamic scaling of a blockchain network.

[0012] In another aspect, the purpose of this disclosure is to support the complexity of variable tasks in a blockchain network.

[0013] In another embodiment, an object of the present disclosure is to enable communication between spatiotemporal divisions based on a hierarchical structure of spatiotemporal divisions.

[0014] In another aspect, the object of the disclosure is to provide performance improvements based on the number of spatiotemporal divisions and the total rate of concurrent task processing across all spatiotemporal divisions compared to the rate of processing of a single spatiotemporal division.

[0015] In another aspect, the object of this disclosure is to provide a spatiotemporal partition formed in a virtualized stateless microservices-based blockchain network, thereby enabling blockchain nodes to continue accessing data across the spatiotemporal partition based on access control permissions configured in the system.

[0016] This section is provided to introduce in a brief form certain purposes and aspects of the present invention, which are further described in the detailed description below. This summary is not intended to identify the main features or scope of the subject matter claimed. [Means for solving the problem]

[0017] In one embodiment, the disclosure provides a system for spatiotemporal data partitioning in a blockchain network. The system receives blockchain data containing various types of information associated with one or more tasks for recording in the blockchain network. Furthermore, the system determines spatiotemporal dimensions from the received blockchain data to analyze metrics or usage patterns in various spatiotemporal windows of a predefined time. Furthermore, the system assigns various levels of hierarchical structure to one or more spatiotemporal partitions in the blockchain network based on the determined spatiotemporal dimensions. Subsequently, the system aggregates the arrival data processing rates of one or more tasks to be achieved across the assigned spatiotemporal partitions for a single spatiotemporal partition. Furthermore, the system processes the data within the hierarchical structure of the blockchain network across one or more spatiotemporal partitions if the aggregated arrival data processing should be performed across one or more spatiotemporal partitions.

[0018] In another aspect, the disclosure further provides a method for spatiotemporal data partitioning in a blockchain network. The method includes receiving blockchain data having various kinds of information associated with one or more tasks for recording in the blockchain network. Furthermore, the method includes determining a time dimension and a spatial dimension from the received blockchain data in order to analyze metrics or usage patterns in various time windows of a predefined time. Furthermore, the method includes assigning various levels of hierarchical structure to one or more spatiotemporal partitions in the blockchain network based on the determined time and spatial dimensions. Subsequently, the method includes a step of aggregating the arrival data processing rates of one or more tasks to be achieved across the assigned spatiotemporal partitions for a single spatiotemporal partition. Furthermore, if the processing of the aggregated arrival data is performed across one or more spatiotemporal partitions, the method includes a step of processing the data in the hierarchical structure of the blockchain network across one or more spatiotemporal partitions.

[0019] The accompanying drawings incorporated herein and constituting part of the present invention illustrate exemplary embodiments of the disclosed methods and systems, and similar reference numerals throughout the different drawings refer to the same parts. Components in the drawings are not necessarily to scale, and instead the emphasis is on clearly illustrating the principles of the present invention. Some drawings may use block diagrams to show components, and the internal circuitry of each component may not be shown. It will be understood by those skilled in the art that the inventions in such drawings include inventions of electrical components, electronic components, or circuits commonly used to mount such components. [Brief explanation of the drawing]

[0020] [Figure 1]FIG. is an exemplary network architecture in which the system of the present disclosure can be implemented or implemented using the system of the present disclosure for spatio-temporal data partitioning in a blockchain network. [Figure 2] FIG. is an exemplary representation of a system for spatio-temporal data partitioning in a blockchain network according to an embodiment of the present disclosure. [Figure 3A] FIG. is an exemplary graphical representation of data processing over spatio-temporal partitioning in a blockchain network according to an embodiment of the present disclosure. [Figure 3B] FIG. is an exemplary graphical representation of data processing at a variable data processing rate over spatio-temporal partitioning according to an embodiment of the present disclosure. [Figure 3C] FIG. is an exemplary graphical representation of data processing over spatio-temporal partitioning with aggregation of spatio-temporal partitioning based on computational requirements according to an embodiment of the present disclosure. [Figure 3D] FIG. is an exemplary graphical representation of data processing over spatio-temporal partitioning with aggregation of spatio-temporal partitioning based on storage considerations according to an embodiment of the present disclosure. [Figure 4A] FIG. is an exemplary flowchart representation of a hierarchical structure over spatio-temporal partitioning having two levels of hierarchy within a blockchain network according to an embodiment of the present disclosure. [Figure 4B] FIG. is an exemplary sequence diagram representation of communication between network constraint (NC) spatio-temporal (TS) partitioning within a blockchain network according to an embodiment of the present disclosure. [Figure 4C] FIG. is an exemplary flowchart representation of a hierarchical structure over spatio-temporal partitioning having three levels of hierarchy in a blockchain network according to an embodiment of the present disclosure. [Figure 4D] FIG. is an exemplary flowchart representation of a hierarchical structure over spatio-temporal partitioning in which aggregated processing is provided in a blockchain network according to an embodiment of the present disclosure. [Figure 5]This is an exemplary method flowchart illustrating a method for spatiotemporal data partitioning in a blockchain network according to one embodiment of the present disclosure. [Figure 6] This figure shows an exemplary computer system that can utilize or be utilized by embodiments of the present invention according to the embodiments of the present disclosure. [Modes for carrying out the invention]

[0021] The above will become clearer from the following more detailed description of the present invention.

[0022] The following description provides various specific details to give a complete understanding of the embodiments of this disclosure for illustrative purposes. However, it will be apparent that embodiments of this disclosure may be carried out without these specific details. Some of the functions described below can be used independently of each other or in any combination of other functions. Some individual functions may not address all of the above issues, or may address only some of them. Some of the above issues may not be fully addressed by any of the functions described herein.

[0023] The following description provides only exemplary embodiments and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the following description of exemplary embodiments provides a useful explanation for implementing the exemplary embodiments for those skilled in the art. It should be understood that various modifications may be made to the function and arrangement of the elements without departing from the spirit and scope of the invention described herein.

[0024] Specific details are given in the following description to provide a complete understanding of the embodiments. However, it will be understood by those skilled in the art that embodiments can be carried out without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form so as not to obscure the embodiments with unnecessary details. In other examples, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary details to avoid obscuring the embodiments.

[0025] Furthermore, it should be noted that individual embodiments may be described as processes shown as flowcharts, flow diagrams, data flow diagrams, structural diagrams, or block diagrams. While flowcharts may describe operations as sequential processes, many operations can be performed in parallel or simultaneously. Moreover, the order of operations can be rearranged. A process terminates when its operations are complete, but there may be additional steps not included in the diagram. A process can correspond to a method, function, procedure, subroutine, subprogram, etc. If a process corresponds to a function, its termination may correspond to the function returning to the calling function or the main function.

[0026] The terms “exemplary” and / or “demonstrative” are used herein to mean that something serves as an example, illustration, or demonstration. To avoid misunderstanding, the subject matter disclosed herein is not limited by such examples. Furthermore, any aspect or design described herein as “exemplary” and / or “demonstrative” should not necessarily be construed as being preferable or advantageous to other aspects or designs, nor should it be meant to exclude equivalent exemplary structures and techniques known to those skilled in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar terms are used in either the detailed description or the claims, such terms are intended to be comprehensive without addition or exclusion, in a similar manner to the term “comprising” as an open transition word.

[0027] Throughout this specification, any reference to “one embodiment,” “an embodiment,” “an instance,” or “one instance” means that a particular feature, structure, or characteristic described in relation to an embodiment is included in at least one embodiment of the present invention. Therefore, occurrences of the phrase “in one embodiment” or “in an embodiment” in various places throughout this specification do not necessarily all refer to the same embodiment. Furthermore, particular features, structures, or characteristics can be combined in any suitable manner in one or more embodiments.

[0028] The terms used herein are intended solely to describe specific embodiments and are not intended to limit the invention. Where used herein, the singular forms “a,” “an,” and “the” are intended to include the plural form unless the context explicitly indicates otherwise. Where used herein, the terms “comprises” and / or “compose” specify the presence of the described functions, integers, steps, actions, elements, and / or components, but do not exclude the presence or addition of one or more other functions, integers, steps, actions, elements, components, and / or groups thereof. Where used herein, the terms “and / or” include any and all combinations of one or more of the associated list items.

[0029] Various embodiments of this disclosure provide systems and methods for spatiotemporal data partitioning in blockchain networks. This disclosure facilitates the optimization of blockchain data processing across both the temporal and spatial dimensions, thereby promoting faster and more concurrent processing across spatiotemporal partitions within a blockchain network. In particular, processing data in a blockchain network based on spatiotemporal partitioning allows blockchain nodes to access information across the entire spatiotemporal partition. The performance improvements through the proposed systems and methods can be anticipated based on the number of spatiotemporal partitions and the total rate of concurrent task processing across the entire spatiotemporal partition compared to the rate of processing of a single spatiotemporal partition.

[0030] Embodiments of this disclosure can be implemented in a distributed ledger network, such as a blockchain network, where transactions can be executed between different nodes of the network, and nodes can also reference entities. A blockchain network implemented as a peer-to-peer network may enable entities to execute secure and immutable transactions. Spatiotemporal partitioning allows blockchain nodes to access information across the entire spatiotemporal partition. In one embodiment, spatiotemporal partitioning is formed in a virtualized stateless microservices-based blockchain network, thereby allowing blockchain nodes to continue accessing data across the entire spatiotemporal partition based on access control permissions configured in the system.

[0031] The proposed systems and methods based on spatiotemporal partitioning improve blockchain processing for each specific use case as much as possible, provided that such spatiotemporal dimensions can be identified in the use case and can be utilized across different blockchain use cases. The spatiotemporal dimensions of spatiotemporal partitioning may vary depending on the use case considered in data processing within the blockchain network.

[0032] Referring to Figure 1, an exemplary network architecture of a spatiotemporal data partitioning system (100) (also referred to as network architecture (100)) can be implemented, or used in, an embodiment of the System (110) of the Disclosure. As shown, the exemplary network architecture (100) may comprise a System (110) for spatiotemporal data partitioning in a blockchain network based on blockchain data received from users (102-1, 102-2, ..., 102-N) (individually referred to as users (102), and collectively referred to as users (102)) associated with one or more first computing devices (104-1, 104-2…104-N) (individually referred to as first computing device (104), and collectively referred to as first computing device (104)). The System (110) may be further operably coupled to a second computing device (108) associated with an entity (114). The entity (114) may include a vendor, network operator, company, organization, university, research facility, enterprise, defense facility, or any other secure facility. In some implementations, the system (110) may also be associated with a second computing device (108). Furthermore, the system (110) may also be communicably coupled to one or more first computing devices (104) via a communication network (106). The communication network (106) may include a wireless network, a wired network, or a combination thereof, which can be implemented as one of various types of networks such as an intranet, local area network (LAN), wide area network (WAN), or the internet. Furthermore, the communication network (106) may be either a dedicated network or a shared network.A shared network can represent an association of various types of networks that can use various protocols, such as the Hypertext Transfer Protocol (HTTP), the Transmission Control Protocol / Internet Protocol (TCP / IP), and the Wireless Application Protocol (WAP).

[0033] In some implementations, one or more first computing devices (104) and second computing devices (108) may include, but are not limited to, handheld wireless communication devices (e.g., mobile phones, smartphones, phablet devices, etc.), wearable computer devices (e.g., head-mounted display computer devices, head-mounted camera devices, wristwatch computer devices, etc.), global positioning system (GPS) devices, laptop computers, tablet computers, or other types of portable computers, media playback devices, portable game systems, and / or any other types of computing devices with wireless communication capabilities.

[0034] In some implementations, the system (110) may be coupled to a blockchain network (112). Although a single blockchain network is shown in Figure 1, it will be understood that multiple blockchain networks may be utilized for the spatiotemporal data partitioning functionality described herein. The blockchain network (112) may also be operably coupled to one or more first computing devices (104) and second computing devices (108) via a communication network (106). As shown in Figure 1, the blockchain network (112) is an exemplary example according to at least one embodiment of this disclosure. The blockchain network (112) represents a simplified blockchain having blocks. The blocks may include a genesis block. Each block may include an ID or hash that uniquely identifies the block, a timeline that identifies previous blocks in chronological order (e.g., the hash number of the previous block), specific information such as a transaction that records all transfers between the sender and receiver, and a public key that identifies at least one sender and at least one receiver. Therefore, linked blocks form a chain, and each link or block in the chain uniquely identifies the previous link or block by including a hash or the previous link or block. A blockchain network (112), possibly a distributed ledger, or blockchain, can be distributed or replicated on a network. A distributed ledger can be replicated and maintained on a database within the underlying blockchain network (112). A blockchain network (112) (or a distributed secure transaction ledger) can be maintained by nodes within a distributed network. Each block in a blockchain network (112) / ledger may contain differentiated information and may have different purposes, but each block may contain communications, messages, information, data, etc., but is not limited to these.

[0035] Figure 1 shows exemplary components of the network architecture (100), but in other implementations, the network architecture (100) may include fewer components, different components, components in different arrangements, or additional functional components than those shown in Figure 1. Furthermore, one or more components of the network architecture (100) may perform functions described as being performed by one or more other components of the network architecture (100).

[0036] In some implementations, the system (110) may be a standalone device or may be communicatively coupled to a computing device (not shown in Figure 1) and / or a central server (not shown in Figure 1). In other implementations, the system (110) may be associated with a computing device or a centralized server. The system (110) may be implemented in, but is not limited to, electronic devices, mobile devices, wireless devices, wired devices, servers, etc. Such servers may include, but are not limited to, standalone servers, remote servers, cloud servers, dedicated servers, etc. In one embodiment, the system (110) may communicate with other devices or one or more first computing devices (104) or second computing devices (108), or a blockchain network (112), using a low point-to-point communication protocol such as Bluetooth®. In other embodiments, the system may also communicate via a variety of other protocols and technologies such as WiFi®, WiMAX®, iBeacon®, and Near Field Communication (NFC). In other embodiments, the system (110) may be connected to a device in a wired manner. Examples of entity devices may include, but are not limited to, computer monitors, television sets, light-emitting diodes (LEDs), and liquid crystal displays (LCDs).

[0037] In one embodiment, the system (110) may include one or more processors coupled to memory, which may store instructions that, when executed by one or more processors, cause the system (110) to perform congestion determination in a telecommunications deployment area. An exemplary representation of the system (110) for determining congestion in a telecommunications deployment area according to one embodiment of the present disclosure is shown in Figure 2. In one aspect, the system (110) may include one or more processors (202). One or more processors (202) may be implemented as one or more microprocessors, microcomputers, microcontrollers, edge or fog microcontrollers, digital signal processors, central processing units, logic circuits, and / or any device that processes data based on operational instructions. Among other functions, one or more processors (202) may be configured to fetch and execute computer-readable instructions stored in the system (110)'s memory (204). The memory (204) may be configured to store one or more computer-readable instructions or routines in a non-temporary computer-readable storage medium that can be fetched and executed to create or share data packets over network services. The memory (204) may include any non-temporary storage device, such as volatile memory like RAM, or non-volatile memory like EPROM or flash memory.

[0038] In one embodiment, the system (110) may include an interface (206). The interface (206) may provide various interfaces, such as interfaces for data input and output devices, referred to as I / O devices, storage devices, etc. The interface (206) may facilitate communication of the system (110). The interface (206) may also provide a communication path for one or more components of the system (110). Examples of such components include, but are not limited to, a processing unit / engine (208) and a database (210).

[0039] A processing unit / engine (208) may be implemented as a combination of hardware and programming (e.g., programmable instructions) to implement one or more functions of the processing engine (208). In the examples described herein, such a combination of hardware and programming may be implemented in several different ways. For example, the programming of the processing engine (208) may be processor-executable instructions stored in a non-temporary machine-readable storage medium, and the hardware of the processing engine (208) may include processing resources (e.g., one or more processors) to execute such instructions. In this example, the machine-readable storage medium may store instructions that implement the processing engine (208) when executed by the processing resources. In such an example, the system (110) may include a machine-readable storage medium for storing instructions and processing resources for executing instructions, and the machine-readable storage medium may be separate but accessible to the system (110) and the processing resources. In other examples, the processing engine (208) may be implemented by electronic circuits.

[0040] The processing engine (208) may include one or more modules / engines selected from among the receiving module (212), decision module (214), allocation module (216), aggregation module (218), processing module (220), and other modules (222). The processing engine (208) may also be, but is not limited to, edge-based microservice event processing.

[0041] In one embodiment, a receiving module (212) may receive blockchain data comprising various types of information associated with one or more tasks for recording on a blockchain network (112). In one embodiment, various types of information may include, but are not limited to, sensor data / information, petroleum data / information, telecommunications information, and information about different groups of users, which may be segmented based on geographical areas such as cities and districts and processed in different spatiotemporal segmentations. In another example, in the field of healthcare applications such as pandemic management, tracking information may be recorded in local spatiotemporal segmentations. Local spatiotemporal segmentations may record data based on demographics such as migrant workers, lower / middle / upper class segments, or frontline healthcare workers, and across various geographical areas such as cities, villages, states, federal territories, or based on age, in order to analyze the impact of a pandemic across the region and to process the data for recording information such as test results, or recovery / failure hospitalization processes, or treatment techniques. Spatiotemporal segmentation data based on geographical areas may be dynamically recorded in edge spatiotemporal segmentations and aggregated in common segmentations. In one embodiment, pub-sub notifications may be configured to communicate across common spatiotemporal dimensions, such as elderly or child demographics in different geographical regions, and to transfer information across spatiotemporal segments. In another embodiment, a machine learning (ML) model may be configured for any subset of the data being recorded. In an exemplary embodiment, supply chain information can be analyzed to determine trends in the declining availability of medical devices or kits, thereby enabling the redistribution of medical devices or kits across different spatiotemporal segments. For example, aircraft such as drones may be used to transport medical devices or kits as needed. In one embodiment, if there is high demand for medical devices or kits in a particular spatiotemporal segment, the demand may be replenished from another nearby spatiotemporal segment.

[0042] In one embodiment, the aggregation process of information in a hierarchical structure can be performed across TS partitions based on geographical areas such as states or countries, by processing blockchain data in TS partitions of various sub-regions such as cities and villages within a geographical area. The aggregation process across the entire spatiotemporal partition can be transmitted to the TS partitions if necessary for comparison or to generate machine learning predictive models that perform a weighted combination of local and aggregate metrics across the entire TS partition.

[0043] In one embodiment, various types of information may refer to other blockchain use cases / fields as well. In an exemplary embodiment, blockchain data from different supply chains can be recorded in different spatiotemporal divisions. Furthermore, blockchain data from different segments of the same supply chain can be recorded in various spatiotemporal divisions, and information is transferred through connections to process the data across the spatiotemporal divisions.

[0044] In one embodiment, one or more tasks may refer to any task applicable to the blockchain network (112), including, but not limited to, aggregation, fusion, hierarchical machine learning, federated learning microservices, etc. For example, one or more tasks may store data related to the handover of a user (102) of a first computing device (104) in a t=a communication area. Furthermore, tasks may include managing a pandemic, tracking information, etc. In another example, a task may include recording any kind of data on the blockchain network (112).

[0045] In one embodiment, a determination module (214) may determine time and spatial dimensions from received blockchain data in order to analyze metrics or usage patterns in various time windows of a predefined time. In one embodiment, an allocation module (216) may allocate various levels of hierarchical structures to one or more spatiotemporal partitions in the blockchain network (112) based on the determined time and spatial dimensions. The spatiotemporal partitions may be allocated based on computing resources, storage resources, and at least one of the network, requirements, and constraints in each spatiotemporal partition within the blockchain network. Spatiotemporal partitions within a level in the hierarchical structure may be connected through a first connection, and spatiotemporal partitions between levels may be connected through a second connection.

[0046] Furthermore, the aggregation module (218) may aggregate the arrival data processing rates of one or more tasks to be achieved across one or more allocated spatiotemporal partitions for a single spatiotemporal partition. Furthermore, the processing module (220) may process data in the hierarchical structure of the blockchain network (112) across one or more spatiotemporal partitions if the processing of the aggregated arrival data is performed across one or more spatiotemporal partitions. In one embodiment, the arrival data processing rates may be aggregated to periodically archive blockchain data from dynamic partition memory (DPM) to archive partition memory (APM) to free up space in DPM. Furthermore, the periodic archiving of blockchain data may be based on the arrival data processing rates of data in one or more spatiotemporal partitions, and when the blockchain data stored in DPM reaches a threshold, a portion of the blockchain data from DPM is archived to APM. In one embodiment, one or more spatiotemporal partitions are aggregated into an aggregation set, and the aggregated arrival data processing rates of one or more spatiotemporal partitions in the aggregation set are less than or equal to the maximum arrival data processing rate supported by dynamic partition memory (DPM) and archive partition memory (APM).

[0047] In one embodiment, the allocation module (216) can allocate one or more edge spatiotemporal partitions to process edge blockchain data that includes mobility data. If the mobility data includes a handover, each of the one or more edge spatiotemporal partitions hands off the processing of the edge blockchain data to a second edge spatiotemporal partition. For processing the edge blockchain data, the processor (202) may send a handover trigger request from the first edge spatiotemporal partition to a spatiotemporal partition in order to manage the session during processing of the edge blockchain data. The spatiotemporal partition is a common spatiotemporal partition. Furthermore, upon receiving the request, the processor (202) may interact with one or more edge spatiotemporal partitions via the common spatiotemporal partition by continuing to process the edge blockchain data and determine a second edge spatiotemporal partition based on the mobility data. Furthermore, the processor (202) may send the edge blockchain data to the second edge spatiotemporal partition determined from the common spatiotemporal partition. Furthermore, the processor may notify the first edge spatiotemporal partition via a common spatiotemporal partition about the success of the handover of mobility data to the second edge spatiotemporal partition.

[0048] In one embodiment, the system (110) may be, but is not limited to, a system-on-a-chip (SoC) system. In another embodiment, the on-site data capture, storage, matching, processing, decision-making, and operational logic may be coded using a microservices architecture (MSA), but is not limited to that. Multiple microservices may be containerized and may be event-based to support portability.

[0049] In one embodiment, the network architecture (100) is modular and can be flexible to accommodate any kind of change in the system (110), and proximity processing can be obtained for inventory reestimation. Details of the system (110) configuration can be modified on the fly.

[0050] In one embodiment, the system (110) may be remotely monitored, and the data, applications, and physical security of the system (110) can be fully guaranteed. In one embodiment, the data may be collected with great care and stored in a cloud-based data lake so that it is processed to extract actionable insights. Thus, a form of predictive maintenance can be achieved.

[0051] In exemplary embodiments, the communication network (106) may include, but not limited to, at least part of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or perform any combination thereof of one or more messages, packets, signals, waves, voltage or current levels, or any combination thereof. The network may include, but not limited to, one or more wireless networks, wired networks, the Internet, intranets, public networks, private networks, packet-switched networks, circuit-switched networks, ad-hoc networks, infrastructure networks, public switched telephone networks (PSTNs), cable networks, cellular networks, satellite networks, fiber optic networks, and any combination thereof.

[0052] In one embodiment, one or more first computing devices (104) or a second computing device may communicate with the system (110) via a set of executable instructions residing in any operating system, including but not limited to Android®, iOS®, Kai OS®, etc. In one embodiment, one or more first computing devices (104) may include, but not limited to, electrical equipment, electronic equipment, electromechanical equipment, or one or more combinations of the above devices, such as mobile phones, smartphones, virtual reality (VR) devices, augmented reality (AR) devices, laptops, general-purpose computers, desktops, personal digital assistants, tablet computers, mainframe computers, or any other computing devices, and the computing device may include, but not limited to, one or more built-in or externally coupled accessories, such as visual aids like cameras, audio aids, microphones, keyboards, and input devices for receiving user input, such as touchpads, touch-enabled screens, and electronic pens. It should be understood that one or more first computing devices (104) are not limited to the devices mentioned and a variety of other devices may be used. Smart computing devices can be one of the suitable systems for storing data and other personal / confidential information.

[0053] Figure 3A shows an exemplary graphical representation of data processing across spatiotemporal partitioning in a blockchain network according to embodiments of the present disclosure. As shown in Figure 3A, block (301) may represent a spatiotemporal block in active memory of high-speed access memory, such as dynamically partitioned memory (DPM) (313) for accelerating processing. Furthermore, block (303) may represent a spatiotemporal block in archive partitioned memory (APM) 311. Block (301) may contain recent information, while block (303) may contain older information. For example, if the blockchain network (112) records, for example, Internet of Things (IoT) sensor data, the spatial dimension may be associated with the geographical region associated with the IoT sensor. In this way, IoT data streams associated with different geographical regions may be processed and recorded in different spatiotemporal partitions within the blockchain network (112).

[0054] In another example, if a blockchain network (112) records information related to mobile user data, the spatial dimension could be associated with any one or combination of, but not limited to, an alphabetical name range, demographic values ​​such as age or income ranges, a statically determined geospatial home / work location, or gender. Furthermore, a current context specific to the use case could also be utilized, such as the current geolocation for dynamic processing. In one example, if the "K" spatial dimension is used, this would be a "K+1" spatiotemporal partition of the blockchain data being processed in system (110), where the data can be represented by "K+1" tuples, where "1" tuple values ​​represent time intervals and the remaining "K" tuple values ​​represent spatial intervals.

[0055] Embodiments of this specification may employ various methods that can be used to archive old data in a blockchain network (112). For example, data from DPM(313) may be periodically moved to APM(311) to free up space within DPM(313). The frequency at which data is archived may depend on the data arrival rate in different spatiotemporal partitions (hereinafter referred to as the arrival data processing rate). When the data stored in DPM(313) reaches a threshold, a portion of the data from DPM(313) is periodically archived to APM(311).

[0056] Figure 3B shows an exemplary graphical representation of data processing at a variable data processing rate across spatiotemporal partitioning according to an embodiment of the present disclosure. The rate of data processing that occurs each time and the spatiotemporal partitioning may differ between partitions. Therefore, microservices allocated for data processing in the blockchain network (112) can be dynamically scaled up. As shown in Figure 3B, there may be two data streams at data processing rate "λ", two data streams at data processing rate "λ / 2", and two data streams at data processing rate "λ / 3".

[0057] In one embodiment, the degree of performance improvement for a blockchain use case depends on the number of concurrent spatiotemporal partitions that can be created, and the expected benefit is based on the total reachable rate of task processing that can be achieved across all partitions related to a single spatiotemporal partition based on the spatiotemporal dimensions available for a particular blockchain use case. Furthermore, a virtual central processing unit (vCPU), such as system (110), may refer to a central processing unit that processes one or more spatiotemporal partitions. Data processing at a variable data rate across spatiotemporal partitions can be illustrated by the following example.

[0058] Exemplary Scenario If "λi" represents the task arrival rate per second for a spatiotemporal partition "TSi", and each task within the partition "TSi" can utilize vCPU computing resources in units of "αi", then the partition "TSi" may consume "αi λi" resources per second. If each vCPU can support resources in units of "β" per second, then one or more vCPUs can be allocated based on a comparison between "αi λi" and "β". Let's consider Case 1: αi λi > β:

[0059] In this case, multiple vCPUs may be required to execute a single spatiotemporal partition. The number of vCPUs required to process the tasks of partition "TSi" may be "αi λi / β". The number of vCPUs required may vary dynamically for each partition "TSi" based on the dynamic value of "λi", which changes over time. In this way, the computational processing requirements can be dynamically scaled for each partition "TSi", independently of other spatiotemporal partitions. Let's consider Case 2: αi λi <β:

[0060] In this case, multiple spatiotemporal partitions can be aggregated into an aggregation set "B" (i∈B) such that the aggregated reach rate is less than or equal to the vCPU capacity "β", as shown in Figures 3C and 3D. In other words, for aggregation, Equation 1 is provided below. Σ i∈B λi≦β…Equation 1

[0061] Figure 3C shows an exemplary graphical representation of data processing across spatiotemporal divisions by aggregation of spatiotemporal divisions based on computational requirements, according to an embodiment of the present disclosure.

[0062] In Figure 3C, blocks (306-1, 306-2, 306-3, and 306-4) (collectively referred to as block 306) represent vCPUs that process spatiotemporal partitions within the blockchain network (112). Each of blocks (306-1) and (306-3) may process spatiotemporal partitions associated with the reach rate "λ", while each of blocks (306-2) and (306-4) may process aggregated spatiotemporal partitions associated with the reach rates "λ / 3" and "λ / 2", respectively. As shown in Figure 3C, DPM(313) or APM(311) may support one or more spatiotemporal partitions associated with a single vCPU-constrained stream, such as (306-1), (306-2), (306-3), and (306-4), and Figure 3D shows DPM(313) or APM(311) which may support one or more TS partitions associated with multiple vCPU-constrained streams, such as (306-1), (306-2), (306-3), and (306-4).

[0063] In one embodiment, spatiotemporal partitioning may be performed based on the computational requirements of each spatiotemporal partition within the blockchain network. However, computational constraints may not coincide with storage processing considerations. For example, "λ DPM " and "λ APM Consider the case where "" is the maximum reach rate that can be supported by DPM(313) and APM(311), respectively. Then, the spatiotemporal partitions of set "A" can be aggregated into aggregated set "A" (i∈A) such that the aggregated reach rate of the spatiotemporal partitions of set "A" is less than or equal to the maximum reach rate supported by DPM(313) and APM(311), which is expressed in equations 2 and 3 below. Σ i∈B λi ≤ λDPM for DPM aggregation...Equation 2 Σ i∈B λi ≤ λAPM for DPM aggregation...Equation 3

[0064] In some cases, aggregation cannot be performed across an entire subset of spatiotemporal dimensions. Depending on the use case, spatiotemporal partitioning may be aggregated based on computational or storage constraints in the blockchain network.

[0065] Figure 3D shows an exemplary graphical representation of data processing across spatiotemporal partitions with aggregation of spatiotemporal partitions based on storage considerations, according to embodiments of the present disclosure. As shown in Figure 3D, a single DPM or APM may support multiple spatiotemporal partitions that can be combined across an aggregated vCPU-constrained stream. Figure 3D shows a group of spatiotemporal partitions associated with different reach rates, such as "λ", "λ / 3", and "λ / 2". The DPM or APM may have the capability to handle a combined reach rate of tasks that is greater than the aggregate reach rate of the spatiotemporal partitions, where the aggregate reach rate is "λ + λ / 3 + λ / 2", and may be equal to, for example, "11λ / 6".

[0066] For example, spatiotemporal partitioning can be performed for fifth-generation (5G) or sixth-generation (6G) edge data processing. Spatiotemporal partitioning may be network-restricted or based on network constraints. For instance, mobile users can be dynamically partitioned based on their current geographical location in the spatial dimension, which can be called network-constrained time-spatial (NC TS) partitioning. Since such partitioning may be performed due to latency / bandwidth constraints associated with high-speed edge processing, spatiotemporal partitioning can be performed similarly across distributed data centers. For example, edge blockchain data processing for ultra-high reliability low-latency communications (URLLC) based on a use case may be heavily constrained by latency, requiring processing to be completed in milliseconds. After partitioning based on network constraints, further partitioning can be performed in the data center based on other spatiotemporal factors to adapt to the requirements and constraints of computational and storage resources.

[0067] Figure 4A shows an exemplary flowchart representation of a hierarchical structure across spatiotemporal partitions having two levels of hierarchy within a blockchain network, according to embodiments of the present disclosure. In one example, processing between spatiotemporal partitions may be performed based on a hierarchical structure between spatiotemporal partitions within a blockchain network (112). Blockchain data associated with a user may be recorded in a lower-level spatiotemporal partition providing services to the user (102). As shown in Figure 4A, if the mobility of a moving user (410-1) needs to be managed for highly mobile users across the service area in a 5G network, data processing for such users may be performed in a higher-level common network constraint (NC) spatiotemporal (TS) partition (401-1) that supports multiple lower-level network constraint (NC) spatiotemporal (TS) partitions (403-1), (403-2), and (403-3) (collectively referred to as NC TS partitions (403) and individually as NC TS partitions (403)) in the hierarchical structure.

[0068] For example, information related to processing in the blockchain network (112) may include information related to the user plane function (UPF) and control plane function (CPF) of the 5G network. User information related to an active UPF associated with a user may be recorded on the blockchain network (112) / DLT platform of the blockchain or distributed ledger technology (DLT) transactions associated with the user. Additionally or alternatively, but not limited to, information related to processing using different CPFs or network slice allocation information, such as Access and Mobility Function (AMF), Session Management Function (SMF), Authentication Server Function (ASF), or Unified Data Management Function (UDMF), or user information stored in a user data repository, may also be recorded on the blockchain / DLT platform in edge TS partitioning.

[0069] In exemplary embodiments, each edge NC TS division (403) can support different nearby geographical regions. Each TS division can be connected through a connection. For example, TS divisions within a level of a hierarchical structure can be connected through a first connection (shown as connection 1 in Figure 4A), while TS divisions between levels can be connected through a second connection (shown as connection 2 in Figure 4A).

[0070] For example, blockchain data associated with the mobility of a moving user (410-1) may be recorded based on the degree of the user's mobility. For instance, if the user's mobility is high, the blockchain data may be recorded across edge NC TS partitions (403-1), (403-2), and (403-3). Alternatively, if the user is static or semi-static, the corresponding blockchain data may be recorded in edge NC TS partition (403-2), which is likely to be closest to the moving user (410-1), as shown in Figure 4A.

[0071] In another example, a common NC TS partition (401-1) could perform blockchain data processing of user data only when the user is near multiple possible service areas, and could also record data associated with network functions and user mobility information, at which point a handover may occur. Subsequently, after the handover, the data associated with the user can be managed by the nearest edge NC TS partition (403). Furthermore, since the edge NC TS partition can periodically update the common NC TS partition, the common NC TS partition may be prepared to handle user mobility and handovers when they occur again in the future. In another example, if it is necessary to manage the user mobility of highly mobile users spanning different AMF service areas in a 5G network, data processing for such users can be performed in specific network constraint (NC) TS partitions at the required network edges without the need for a common NC TS partition. For example, when a user experiences a handover from a first AMF service area to a second AMF service area, the first edge NC TS partition can hand off the processing of user data to the second edge NC TS partition. In such a case, the first edge NC TS partition can store the user information in the second edge NC TS partition and keep the second edge NC TS partition up-to-date to enable further processing by the UE after the handover.

[0072] Figure 4B is a diagram illustrating an exemplary sequence diagram representation of communication between network constraint (NC) spatiotemporal (TS) partitions within a blockchain network, according to an embodiment of the present disclosure.

[0073] In step (401), the edge time partition (403-2) may share user information with the common spatiotemporal partition (401-1). In step (403), the edge spatiotemporal partition (403-2) may send a handover trigger request to the common spatiotemporal partition (401-1) to manage the user session. In step (405), upon receiving the request, the common spatiotemporal partition (401-1) may interact with other edge TS partitions (403-1) and (403-3). In step (407), the common spatiotemporal partition (401-1) may continue the user's blockchain data and, based on the user's mobility information, determine a new edge spatiotemporal partition for the user. In step (409), based on the determination or selection of a new edge spatiotemporal partition (403-3), the common spatiotemporal partition (401-1) may notify the selected edge spatiotemporal partition (403-3) about the user. In step (411), the common spatiotemporal division (401-1) may transmit user information to the selected edge spatiotemporal division (403-3) relating to the user. In step (413), the common spatiotemporal division (401-1) may notify the old edge spatiotemporal division (403-2) of the success of the user handover to the new edge TS division (403-3).

[0074] Figure 4C shows an exemplary flowchart representation of a hierarchical structure across spatiotemporal partitions having three levels of hierarchy in a blockchain network, according to an embodiment of the present disclosure. As shown in Figure 4C, the hierarchical processing can be extended to multiple common NC TS partitions (401-1) and (401-2), so that a cloud / in-network data center NC TS partition (405-1) can perform information processing across multiple common NC TS partitions (401-1) and (401-2), a common TS partition (403-1) can process data across edge NC TS partitions (403-1), (403-2), and (403-3), and a common TS partition (403-2) can process data across TS partitions (403-4) and (403-5). Figure 4D shows an exemplary flowchart representation of a hierarchical structure across spatiotemporal partitions providing aggregated processing in a blockchain network, according to an embodiment of the present disclosure. Processing in the hierarchical structure of a blockchain network (112) can be performed across multiple spatiotemporal dimensions if some aggregation processing needs to be performed across the entire partition. For example, but not limited to, at least one of aggregation, fusion, hierarchical machine learning, federative learning microservices can be configured to process trusted blockchain data across different spatiotemporal partitions. For example, if data associated with IoT camera or vehicle movement / traffic, or sensation or pollution in a given geographical region is being processed in one spatiotemporal partition (412-1), and the same data needs to be merged with user mobility data in another geographical region configured in another user data edge NC TS partition (412-2), the data from all of these data streams can be processed hierarchically in a common TS partition (414-1) that combines the data received from these two TS partitions (412-1) and (412-2). Additional TS partition data streams (412-3) can also be fused at the same level in the hierarchy.

[0075] In one example, the results of processing can be recorded in a common TS partition (414-1) for the entire data stream, and also in lower-level TS partitions associated with the user. In an alternative embodiment, data processing may be performed in a common TS partition (414-1), and recording of the processing results can be done in a blockchain ledger TS partition to record the results performed on behalf of the user based on the processed data. For example, information may be transferred directly to an NC TS partition of a user data edge that may currently support the user, and data fusion can be performed directly in the user data edge NC TS partition, with the results recorded in the blockchain ledger of the latter partition. Connection 1 shown in Figure 4D may enable federated learning and edge processing, and connection 2 shown in Figure 4D may enable the construction of a learning model across the entire edge network. Thus, all predictions and user recommendations can be processed in either an edge TS partition or a common TS partition. Thus, processing in a hierarchical structure is also performed across TS partitions corresponding to the user's different spatiotemporal dimensions. Furthermore, aggregate processing across time dimensions can be performed against different time dimensions along with other spatial dimensions, such as determining metrics or usage patterns in different time windows of a day.

[0076] The embodiments described herein may also be applicable to other blockchain use cases / fields. For example, blockchain data from different supply chains can be recorded in different TS splits. Alternatively or additionally, blockchain data from different segments of the same supply chain can be recorded in different TS splits, and information can be transferred through connections to process the data across the TS splits. As an example, in the oil or telecommunications sector, information from different user groups segmented based on geographical areas such as cities or districts can be processed in different TS splits. In another example, in the field of healthcare applications such as pandemic management, tracking information can be recorded in local TS splits. Local TS splits can record data based on demographics such as migrant workers, lower / middle / upper class segments, or frontline healthcare workers, and across various geographical areas such as cities, villages, states, or federal territories, or based on age, in order to analyze the impact of a pandemic across the region and to process information such as test results, or recovery / failure hospitalization processes, or treatment techniques. Geographical area-based TS split data is dynamically recorded in edge TS splits and aggregated in a common cloud or in-network data center.

[0077] In one embodiment, pub-sub notifications may be configured to communicate across common spatiotemporal dimensions, for example, elderly or child demographics in different geographical regions, and to transfer information across TS divisions. In another embodiment, a machine learning (ML) model may be configured for any subset of the data being recorded. In an exemplary embodiment, supply chain information can be analyzed to determine a trend of declining availability of a medical device or kit, which would allow for the redistribution of the medical device or kit across different TS divisions. For example, if necessary, aircraft such as drones may be used to transport the medical device or kit. In one embodiment, if there is high demand for a medical device or kit in a particular TS division, the demand may be replenished from another nearby TS division. In one embodiment, aggregation processing of information in a hierarchical structure can be performed across TS divisions based on geographical regions such as states or countries by processing blockchain data in TS divisions of various sub-regions such as cities or villages in a geographical region. Aggregation processing across TS divisions can be transmitted to TS divisions if necessary for comparison or to generate machine learning predictive models that perform a weighted combination of local and aggregated metrics across TS divisions.

[0078] Figure 5 shows an exemplary method flowchart illustrating a method for partitioning spatiotemporal data in a blockchain network according to an embodiment of the present disclosure.

[0079] As shown in Figure 5, Method (500) includes one or more blocks that demonstrate a method for spatiotemporal data partitioning in a blockchain network. Method (500) can be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions that execute functions or implement abstract data types.

[0080] The order in which Method (500) is described is not intended to be construed as limiting, and any number of described Method blocks can be combined in any order to implement Method (500). Furthermore, individual blocks may be removed from the Method without departing from the scope of the subject matter described herein. Moreover, Method (500) may be implemented in any suitable hardware, software, firmware, or combination thereof.

[0081] In block (502), the method (500) may include the step of receiving blockchain data having various kinds of information associated with one or more tasks, in order to record the blockchain network (112) by a processor (202) associated with the system (110).

[0082] In block (504), the method (500) may include the step of determining the time and spatial dimensions from received blockchain data in order to analyze metrics or usage patterns in various time windows of a predefined time by the processor (202).

[0083] In block (506), the method (500) may include the step of having a processor (202) assign various levels of hierarchical structures to one or more spatiotemporal partitions in the blockchain network (112) based on the determined time and spatial dimensions.

[0084] In block (508), the method (500) may include the step of aggregating the reach data processing rates of one or more tasks to be achieved across one or more assigned spatiotemporal divisions for a single spatiotemporal division, using a processor (202).

[0085] In block (510), the method (500) may include the step of processing data in the hierarchical structure of the blockchain network (112) across one or more spatiotemporal divisions if the processing of aggregated arrival data is performed by the processor (202) across one or more spatiotemporal divisions.

[0086] Figure 6 shows an exemplary computer system (600) that can utilize, or be utilized by, embodiments of the present invention according to embodiments of the present disclosure.

[0087] As shown in Figure 6, the computer system (600) may include an external storage device (610), a bus (620), main memory (630), read-only memory (640), a mass storage device (650), a communication port (660), and a processor (670). Those skilled in the art will understand that the computer system may include multiple processors and communication ports. Examples of the processor (670) include, but are not limited to, an Intel® Itanium® or Itanium 2 processor, or an AMD® Opteron® or Athlon MP® processor, a Motorola® series processor, a FortiSOC® system-on-chip processor, or other future processors. The processor (670) may include various modules associated with embodiments of the present invention. The communication port (660) may be an RS-232 port used for modem-based dial-up connections, a 10 / 100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or any other existing or future port. The communication port (660) may be selected depending on the network, such as a local area network (LAN), a wide area network (WAN), or any network to which the computer system connects. Memory (630) may be random access memory (RAM) or any other dynamic storage device commonly known in the art. Read-only memory (640) may be any static storage device, such as a programmable read-only memory (PROM) chip for storing static information, such as processor (670) boot or BIOS instructions. Mass storage (650) may be any current or future mass storage solution that can be used to store information and / or instructions.Exemplary high-capacity storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., with Universal Serial Bus (USB) and / or Firewire interfaces), e.g., those available from Seagate (e.g., the Seagate Barracuda 782 family) or Hitachi (e.g., the Hitachi Deskstar 13K800), one or more optical discs, and Redundant Array of Independent Disks (RAID) storage, e.g., arrays of disks (e.g., SATA arrays) available from various vendors, including Dot Hill Systems Corp., LaCie, Nexsan Technologies, Inc., and Enhance Technology, Inc.

[0088] The bus (620) connects the processor (670) to other memory, storage, and communication blocks in a communicative manner. The bus (620) may be, for example, a Peripheral Interconnection (PCI) / PCI Expansion (PCI-X) bus, Small Computer System Interface (SCSI), USB, etc., for connecting expansion cards, drives, and other subsystems, as well as other buses such as the Front Side Bus (FSB) that connects the processor (670) to software systems.

[0089] If necessary, operator and management interfaces, such as displays, keyboards, and cursor control devices, may also be coupled to the bus (620) to support direct interaction between the operator and the computer system. Other operator and management interfaces may be provided through network connections connected via communication ports (660). The external storage device (610) may be any type of external hard drive, floppy drive, IOMEGA® Zip drive, compact disc-read-only memory (CD-ROM), compact disc-rewritable (CD-RW), or digital video disc-read-only memory (DVD-ROM). The above components are for illustrative purposes only to illustrate various possibilities. The above-described exemplary computer system is not intended to limit the scope of this disclosure.

[0090] Various embodiments of this disclosure provide systems and methods for spatiotemporal data partitioning in blockchain networks. This disclosure facilitates the optimization of blockchain data processing across both the temporal and spatial dimensions, thereby promoting faster and more concurrent processing across spatiotemporal partitions within a blockchain network. In particular, processing data in a blockchain network based on spatiotemporal partitioning allows blockchain nodes to access information across the entire spatiotemporal partition. The performance improvements through the proposed systems and methods can be anticipated based on the number of spatiotemporal partitions and the total rate of concurrent task processing across the entire spatiotemporal partition compared to the rate of processing of a single spatiotemporal partition.

[0091] Embodiments of this disclosure can be implemented in a distributed ledger network, such as a blockchain network, where transactions can be executed between different nodes of the network, and nodes can also reference entities. A blockchain network implemented as a peer-to-peer network may enable entities to execute secure and immutable transactions. Spatiotemporal partitioning allows blockchain nodes to access information across the entire spatiotemporal partition. In one embodiment, spatiotemporal partitioning is formed in a virtualized stateless microservices-based blockchain network, thereby allowing blockchain nodes to continue accessing data across the entire spatiotemporal partition based on access control permissions configured in the system.

[0092] The proposed systems and methods based on spatiotemporal partitioning improve blockchain processing for each specific use case as much as possible, provided that such spatiotemporal dimensions can be identified in the use case and can be utilized across different blockchain use cases. The spatiotemporal dimensions of spatiotemporal partitioning may vary depending on the use case considered in data processing within the blockchain network.

[0093] While this specification places considerable emphasis on preferred embodiments, it should be understood that many embodiments can be made and many modifications can be made in the preferred embodiments without departing from the principles of the present invention. These and other modifications in the preferred embodiments of the present invention will be apparent to those skilled in the art from this disclosure, and it should be clearly understood that the foregoing descriptions are not limiting to the invention and should be implemented merely as examples. [Explanation of Symbols]

[0094] 100 Spatiotemporal Data Division System 100 Network Architectures 102 users 102-1, 102-2, ... 102-N users 104 The First Computing Device 104-1, 104-2…104-N First Computing Devices 106 Communication Networks 108 The Second Computing Device 110 System 112 Blockchain Network 114 Entities 202 processors 204 memory 206 Interfaces 208 Processing Units / Engines 208 Processing Engines 210 Databases 212 Receiver Module 214 Decision Module 216 allocation modules 218 Aggregation Module 220 Processing Modules 222 Other modules 311 Archive Partition Memory (APM) 313 Dynamically Partitioned Memory (DPM) 500 ways 600 Computer Systems 610 External storage devices 620 bus 630 Main Memory 640 Read-only memory 650 High-Capacity Storage Devices 660 communication ports 670 processor

Claims

1. A system (110) for spatiotemporal data partitioning in a blockchain network (112), Processor (202), The memory (204) is coupled to the aforementioned processor (202) and The memory (204) includes processor-executable instructions, and when the processor-executable instructions are executed, the processor (202) is provided with To receive blockchain data containing information associated with one or more tasks in order to record it on the blockchain network (112), In order to analyze metrics or usage patterns in various time windows of a predetermined time, the time and spatial dimensions are determined according to the use and nature of the information contained in the received blockchain data, In the blockchain network (112), a hierarchical structure comprising at least two layers is assigned to a plurality of spatiotemporal divisions based on the determined time and spatial dimensions, wherein the at least two layers comprise a first layer that processes the information and a second layer located above the first layer that processes the information by aggregating the spatiotemporal divisions corresponding to the first layer. For a single spatiotemporal division, the data to be processed by one or more tasks is aggregated by summing the data processing rates of one or more tasks to be achieved across the multiple spatiotemporal divisions that have been assigned to it. If the processing of the aggregated arrival data is to be performed across the multiple spatiotemporal divisions, then the processing of the data within the hierarchical structure of the blockchain network (112) across one or more spatiotemporal divisions is to be performed. A system (110) that causes this to happen.

2. The system (110) according to claim 1, wherein the processor (202) is further configured to allocate one or more edge spatiotemporal partitions corresponding to the first tier to process mobility data and edge blockchain data corresponding to the first tier, and if the mobility data includes a handover, each edge spatiotemporal partition of the one or more edge spatiotemporal partitions hands off the processing of the edge blockchain data to a second edge spatiotemporal partition corresponding to the first tier.

3. For processing the aforementioned edge blockchain data, the processor (202) To manage sessions during the processing of the edge blockchain data, a request for a handover trigger is sent from a first edge spatiotemporal division corresponding to the first tier to a higher spatiotemporal division, wherein the higher spatiotemporal division is a spatiotemporal division that aggregates the first edge spatiotemporal division and the second edge spatiotemporal division. Upon receiving the request in the aforementioned higher-level spatiotemporal division, the system continues to process the edge blockchain data, interacting with one or more edge spatiotemporal divisions via the higher-level spatiotemporal division, and determining the second edge spatiotemporal division based on the mobility data. Transmitting the edge blockchain data from the higher-level spatiotemporal division to the determined second edge spatiotemporal division, Regarding the success of the handover of the mobility data to the second edge spatiotemporal division, the first edge spatiotemporal division is notified via the higher spatiotemporal division. The system (110) according to claim 2, further configured to perform the following:

4. The system (110) according to claim 1, wherein the spatiotemporal divisions are allocated based on at least one of computing resources, storage resources, networks, requirements, and constraints in each of the spatiotemporal divisions within the blockchain network (112).

5. The system (110) according to claim 1, wherein the spatiotemporal divisions within a level in the hierarchical structure are connected through a first connection, and the spatiotemporal divisions between levels are connected through a second connection.

6. The system (110) according to claim 1, wherein the incoming data processing rate is aggregated in order to periodically archive the blockchain data from the dynamically partitioned memory (DPM) (313) to archive partitioned memory (APM) (311) and free up space in the DPM (313).

7. The system (110) according to claim 6, wherein the periodic archiving of the blockchain data is based on the data processing rate of the data within the plurality of spatiotemporal divisions, and when the blockchain data stored in DPM(313) reaches a threshold, a portion of the blockchain data from DPM(313) is archived in APM(311).

8. The processor (202) The aforementioned multiple spatiotemporal divisions are aggregated into an aggregation set, The system (110) according to claim 1, wherein the aggregated reach data processing rates of the plurality of spatiotemporal divisions of the aggregation set are configured to be less than or equal to the maximum reach data processing rate supported by the dynamic partition memory (DPM) (313) and archive partition memory (APM) (311).

9. A method for spatiotemporal data partitioning in a blockchain network (112), A processor (202) associated with the system (110) receives blockchain data containing information associated with one or more tasks for recording on the blockchain network (112), The processor (202) performs the steps of determining the time and spatial dimensions according to the use and nature of the information contained in the received blockchain data in order to analyze metrics or usage patterns in various time windows of a predetermined time, The steps of assigning a hierarchical structure, comprising at least two layers, to a plurality of spatiotemporal divisions in the blockchain network (112) based on the determined time dimension and spatial dimension, wherein the at least two layers comprise a first layer that processes the information and a second layer located above the first layer that processes the information by aggregating the spatiotemporal divisions corresponding to the first layer. The processor (202) aggregates the arrival data of one or more tasks by summing the arrival data processing rates of one or more tasks to be achieved across the assigned spatiotemporal divisions for a single spatiotemporal division, When the processing of the aggregated arrival data is performed across the multiple spatiotemporal divisions, the processor (202) performs the steps of processing the data in the hierarchical structure of the blockchain network (112) across one or more spatiotemporal divisions. Methods that include...

10. The method according to claim 9, further comprising the step of allocating one or more edge spatiotemporal divisions included in the plurality of spatiotemporal divisions to process edge blockchain data comprising mobility data and corresponding to the first tier, wherein if the mobility data comprises a handover, each of the one or more edge spatiotemporal divisions hands off the processing of the edge blockchain data to a second edge spatiotemporal division corresponding to the first tier.

11. The aforementioned processing of the edge blockchain data The processor (202) transmits a request for a handover trigger from a first edge spatiotemporal partition corresponding to the first layer to a higher spatiotemporal partition in order to manage the session during the processing of the edge blockchain data, wherein the higher spatiotemporal partition is a spatiotemporal partition that aggregates the first edge spatiotemporal partition and the second edge spatiotemporal partition. Upon receiving the request in the higher-level spatiotemporal division, the processor (202) continues to process the edge blockchain data, thereby interacting with one or more edge spatiotemporal divisions via the higher-level spatiotemporal division, and determining the second edge spatiotemporal division based on the mobility data. The processor (202) transmits the edge blockchain data from the higher spatiotemporal division to the determined second edge spatiotemporal division, The processor (202) notifies the first edge spatiotemporal division via the higher spatiotemporal division of the success of the handover of the mobility data to the second edge spatiotemporal division. The method according to claim 10, further comprising:

12. The method according to claim 9, wherein the spatiotemporal divisions are allocated based on at least one of computing resources, storage resources, networks, requirements, and constraints in each of the spatiotemporal divisions within the blockchain network (112).

13. The method according to claim 9, wherein the spatiotemporal divisions within a level in the hierarchical structure are connected through a first connection, and the spatiotemporal divisions between levels are connected through a second connection.

14. The method according to claim 9, wherein the incoming data processing rate is aggregated in order to periodically archive the blockchain data from the dynamically partitioned memory (DPM) (313) to the archive partitioned memory (APM) (311) and free up space in the DPM (313).

15. The method according to claim 14, wherein the periodic archiving of the blockchain data is performed based on the data processing rate of the data within the plurality of spatiotemporal divisions, and when the blockchain data stored in DPM(313) reaches a threshold, a portion of the blockchain data from DPM(313) is archived in APM(311).

16. The steps include: aggregating the aforementioned multiple spatiotemporal divisions into an aggregate set; The method according to claim 9, further comprising the step of configuring the aggregated reach data processing rate of the plurality of spatiotemporal divisions of the aggregation set to be less than or equal to the maximum reach data processing rate supported by the dynamic partition memory (DPM) (313) and archive partition memory (APM) (311).

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