System and method for mapping and analyzing data for data visualization and extraction operations

Abstract

The present disclosure provides a system(100) and method(400) for mapping and an- alyzing data for performing data visualization and extraction operations using pre-de- fined network geographies. The method(400) includes receiving(402) at a user inter- face (UI)(206) of a user device, a user request from a user. The method(400) further includes forwarding(404) the user request from the load balancer unit(208) to a work- flow engine(212). The method(400) further includes sending(406), from the workflow engine(212), a computation data. The method(400) further includes computing(408), by the distributed computing engine(214) associated with the user interface(102), data. The method(400) further includes sending(410) computed data from the distributed computing engine(214) via a distributed data lake(234). The method(400) further in- cludes forwarding(412) the computed data and rendering the computed data to the user via the UI(206).

Description

SYSTEM AND METHOD FOR MAPPING AND ANALYZING DATA FOR DATA VISUALIZATION AND EXTRACTION OPERATIONSRESERVATION OF RIGHTS

[0001] A portion of the disclosure of this patent document contains material which is subject to intellectual property rights such as, but are not limited to, copyright, design, trademark, Integrated Circuit (IC) layout design, and / or trade dress protection, belonging to Jio Platforms Limited (JPL) or its affiliates (hereinafter referred as owner). The owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all rights whatsoever. All rights to such intellectual property are fully reserved by the owner.FIELD OF DISCLOSURE

[0002] The embodiments of the present disclosure generally relate to communication networks. In particular, the present disclosure relates to a system and method for mapping and analyzing data for data visualization and extraction operations.DEFINITION

[0003] As used in the present disclosure, the following terms are generally intended to have the meaning as set forth below, except to the extent that the context in which they are used to indicate otherwise.

[0004] The expression ‘Geography’ may refer to the spatial distribution of network elements like cell towers and data centers. It affects network coverage, performance, and planning.

[0005] The expression ‘Filtering Options’ may refer to tools or criteria used to sort and manage data, allowing users to focus on specific subsets based on parameters such as time, location, or error type.

[0006] The expression ‘CRR (Call Release Reason)’ may refer to codes that explain why a call ended, used to monitor and improve network performance by identifying normal and abnormal call terminations.

[0007] The expression ‘Raw Error Code Data’ refers to numerical codes from network components indicating specific errors or issues, used for diagnosing and troubleshooting network problems.

[0008] The expression ‘Data Formatting’ may refer to a process of organizing and structuring data to make it readable and usable, often involving standardization to ensure consistency across systems.

[0009] The expression ‘Dashboard and Report Integration’ may refer to combining data visualization tools with reporting capabilities to provide comprehensive insights and analyses in a single interface, aiding in decision-making and performance monitoring.BACKGROUND OF DISCLOSURE

[0010] The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

[0011] Typically, data visualization and extraction for geography is based on different network geographies such as, but not limited to, circle, centre, and point, and are limited to only support filtering and analysis based on a specific option, for example, circle. In other words, current dashboards are limited in their data visualization and extraction capabilities, only supporting the circle option for geographical analysis. This constraint hampers the ability to explore and interpret data effectively, especially in complex networks where various geographic shapes and structures play a crucial role. By relying solely on circles, users miss out on valuable insights that could be gleaned from other geographical frameworks like Supercore, R4G State, Centre, and Point.

[0012] Each of these geographic models offers unique perspectives and advantages. The Supercore model, for example, can reveal dense clusters of activity, highlighting areas of high significance or intensity. On the other hand, the R4G State framework might provide insights into regional variations and trends, allowing users to understand differences across larger areas. The Centre and Point options can help in pinpointing specific locations and analyzing their impact or role within a broader network.

[0013] These limitations highlight the need for a more versatile dashboard that can accommodate diverse network geographies. Each geographic model offers unique perspectives and advantages. For instance, the Supercore model might reveal dense clusters of activity, while the R4G State framework could provide insights into regional variations. Without these options, analysis remains one-dimensional and less informative, affecting decision-making processes that rely on comprehensive data interpretation.

[0014] There is, therefore, a need in the art to provide an enhanced visual representation functionality that includes support for mapping and analysis of data using network geographies.SUMMARY

[0015] In an exemplary embodiment, a method for performing geography-based analytics is described. The method includes receiving, by a workflow engine, a request through a user interface (UI) of a user device, the request comprising at least one of a user selection of one or more geography and one or more filtering options, wherein the one or more filtering options comprising: a normal call release reason or an abnormal call release reason (CRR), one or more geographies, and computation of data for data analysis, processing, by the workflow engine, the request, to map information associated with the one or more selected geographies with network data and communicate the processed request, to a distributed computing engine, aggregating and computing, by the distributed computing engine, data associated with the mapped information for the selected one or more geographies based on the request, communicating, by the distributed computing engine, the computed data to the workflow engine, the computed datacomprising geography-based network information to the UI, and rendering, by the workflow engine, the computed data comprising geography-based network information on the UI.

[0016] In some embodiments, the method includes performing, by the workflow engine, at least one of: a geographical mapping, a dashboard and report integration, a call release reason (CRR) management, and a data formatting, performing at least one of: generating or processing, by the workflow engine, a forward request based on the geography selected, and generating the computation data based on the forward request.

[0017] In some embodiments, the method includes enhancing user interface to support network geographies-based analytics, and providing the user with an insight based on for decision-making and analysis.

[0018] In an exemplary embodiment, a system for performing geography-based analytics is disclosed. The system includes a user interface (UI) comprising at least one of one or more geographies selection options and one or more filtering options, wherein the one or more filtering options comprising at least one of: a normal call release reason or an abnormal call release reason (CRR), one or more geographies, and computation of data for data analysis. The system includes a workflow engine configured to receive a request through the user interface, the request comprising at least one of a user selection of at least one of the one or more geographies and the one or more filtering options and process the request to map data associated with the one or more selected geographies with network data and communicate the processed request, to a distributed computing engine, to a distributed computing engine. The distributed computing engine of the system is configured to: aggregate and compute the mapped data for the selected one or more geographies based on the request, and communicate the computed data to the workflow engine, the computed data comprising geography-based network information to the UI (206). The workflow engine (212) is further configured to render the computed data comprising geography-based network information on the UI (206).

[0019] In some embodiments, the workflow engine performs at least one of: generating or processing a forward request based on the geography selected.

[0020] In some embodiments, the system includes a dashboard and a report integration mechanism, for integrating one or more geographies support into a plurality of xProbe dashboards and reports.

[0021] In an exemplary embodiment, a computer program product comprising a non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of: receiving, by a workflow engine, a request through a user interface (UI) of a user device, the request comprising at least one of a user selection of one or more geography and one or more filtering options, wherein the one or more filtering options comprising: a normal call release reason or an abnormal call release reason (CRR), one or more geographies, and computation of data for data analysis, processing, by the workflow engine, the request to map data associated with the one or more selected geographies with network data and communicate the processed request, to a distributed computing engine, aggregating and computing, by the distributed computing engine, the mapped data for the selected one or more geographies based on the request, communicating, by the distributed computing engine, the computed data to the workflow engine, the computed data comprising geography-based network information to the UI, and rendering, by the workflow engine, the computed data comprising geography-based network information on the UI.

[0022] In an exemplary embodiment, a user equipment (UE) configured for mapping and analyzing data for performing data visualization and extraction operations using pre-defined network geographies, the user equipment comprising a processor, and a computer readable storage medium storing programming for execution by the processor, the programming including instructions to receive, by a workflow engine, a request through a user interface of the UE, the request comprising at least one of a user selection of one or more geography and one or more filtering options, wherein the one or more filtering options comprising: a normal call release reason or an abnormal call release reason (CRR), one or more geographies, and computation of data for data analysis, and responsive to the request, and receive and render, on the UI, the computed data comprising geography -based clear code based on processing the request using the method for performing geography-based analytics as claimed in the method claim.

[0023] The foregoing general description of the illustrative embodiments and the following detailed description thereof are merely exemplary aspects of the teachings of this disclosure and are not restrictive.OBJECTS OF THE PRESENT DISCLOSURE

[0024] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.

[0025] An object of the present disclosure is to provide system and a method for performing mapping and analysis of data using predefined network geographies.

[0026] An object of the present disclosure is to develop a mapping mechanism to link network- enriched data with network geographies from raw network data for analysis and visualization.

[0027] An object of the present disclosure is to provide an enhanced User Interface (UI) for selection and filtering of options related to additional geographies (such as normal Call Release Reason (CRR) / abnormal CRR, geographies, etc.) and computation of huge data for data analysis.

[0028] An object of the present disclosure is to implement visual representations like bar chart, line chart, and region-specific charts to display data based on the selected geographies.BRIEF DESCRIPTION OF DRAWINGS

[0029] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes the disclosureof electrical components, electronic components or circuitry commonly used to implement such components.

[0030] FIG. 1 illustrates an example network architecture for implementing a system for mapping and analyzing data for performing data visualization and extraction operations using pre-defined network geographies, in accordance with an embodiment of the present disclosure.

[0031] FIG. 2A illustrates an example block diagram a system for mapping and analyzing data for performing data visualization and extraction operations using pre-defined network geographies, in accordance with an embodiment of the present disclosure.

[0032] FIG. 2B illustrates a flowchart showing the mapping and analysis of data using predefined network geographies, in accordance with an embodiment of the present disclosure.

[0033] FIG. 3 illustrates a process flow showing mapping and analysis of data using the predefined network geographies, in accordance with an embodiment of the present disclosure.

[0034] FIG. 4 illustrates a flowchart of a method for mapping and analyzing data for performing data visualization and extraction operations using pre-defined network geographies, in accordance with an embodiment of the present disclosure.

[0035] FIG. 5 illustrates an exemplary block diagram of a computer system in which or with which embodiments of the present invention may be implemented.

[0036] The foregoing shall be more apparent from the following more detailed description of the disclosure.LIST OF REFERENCE NUMERALS100 - Network architecture102-1, 102-2...102-N - Users104-1, 104-2...104-N - User equipment106 - Network108 - System 202- Processor204- Memory206- User Interface208- Load Balancer Unit210- Database 212- W orkflo w engine214- Distributed computing engine216- Dashboard218- Method220- 228 Steps 230-Distributed File System232-Distributed Data Lake300-Implementation of the system301-UI Server302- User304- 326 Steps400-Method402-412 Steps500- Computer system510- External storage device520- Bus530- Main memory540- Read only memory550- Mass Storage Device560- Communication Port570- Computer System ProcessorDETAILED DESCRIPTION OF THE DISCLOSURE

[0037] In the following description, for the purposes of explanation, various specific details are set forth to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all the problems discussed above or might address only some of the problems discussed above.Some of the problems discussed above might not be fully addressed by any of the features described herein.

[0038] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

[0039] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.

[0040] Also, it is noted that individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

[0041] The word “exemplary” and / or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosedherein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and / or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising” as an open transition word without precluding any additional or other elements.

[0042] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0043] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. As used herein, the term “and / or” includes all combinations of one or more of the associated listed items.

[0044] The aspects of the present disclosure are directed to a method and system for mapping and analyzing data for performing data visualization and extraction operations using pre-defined network geographies. In examples, the geographies as discussed may refer to the physical locations and spatial distribution of network infrastructure, such as cell towers, cables, and datacenters. In current context, the geographies involve understanding how these telecommunication elements are arranged across different regions to optimize network coverage, capacity, and performance. In examples, the network infrastructure elements such as, for example, cells may be mapped on the physical geography, and the cells may be tagged with the physical geography. The disclosed system and method support mapping and analysis of the data, in addition to performing data visualization and extraction operations, based on different predefined network geographies. This capability may be implemented and made available across all visual representations and reports in xProbe. The disclosed system and method provide a mapping mechanism to link network-enriched data with network geographies from raw network data for analysis and visualization. The disclosed system and method extend data extraction capabilities to extract specific data subsets based on chosen geographies. The disclosed system and method implement an enhanced user interface with more geographies selection and filtering options (e.g., normal / abnormal CRR, geographies, etc), and computation of huge data for data analysis. Implementation of visualizations like bar chart, line chart, and region-specific charts may be used to display the data based on the selected geographies. In an embodiment, the disclosed system and method uses a mapping mechanism to associate predefined network geographies with data points and create a configuration module for administrators to manage and upload geographies mapping. In another embodiment, the disclosed system and method provides a data extraction and reporting enhancement to modify data extraction and reporting functionalities based on the network geographies. In addition, filters and options may be incorporated for extracting data specific to the defined geographies. In another embodiment, the disclosed system and method provides a data visualization enhancement. Enhanced data visualization capabilities may support the different network geographies and may facilitate to develop interactive charts and region-specific graphs for geographies-based data representation. In yet another embodiment, the disclosed system and method provides a dashboard and a report integration mechanism, for seamlessly integrating geographies support into all xProbe dashboards and reports. This facilitates modification of existing modules to include selection and analysis of data based on the predefined geographies. The current xProbe dashboards lack the ability to perform data visualization and extraction based on different network geographies such as Supercore, Circle,R4G State, Centre, and Point. The existing dashboard only supports filtering and analysis based on the circle option. To address this limitation, the requirement is to enhance the dashboard functionality to include support for mapping and analyzing data using these predefined network geographies. This capability should be made available across all dashboards and reports in xProbe by developing mapping mechanism to link network-enriched data with network geographies from raw network data for analysis and visualization. The present technology extends data extraction capabilities to extract specific data subsets based on chosen geographies. The present system performs implementation of enhance user interface with more geographies selection and filtering options (normal / abnormal CRR, geographies, etc) and computation of huge data for data analysis. The present technology implements visualizations like bar chart, line chart and region-specific charts to display data based on selected geographies. The inventive step is being performed at the application server level.

[0045] According to an embodiment, the geographies mapping mechanism comprises designing and developing a mapping mechanism to associate predefined network geographies with data points and create a configuration module for administrators to manage and upload geographies mapping. The method also includes modifying data extraction and reporting functionality to enable extraction based on network geographies and incorporating filters and options for extracting data specific to defined geographies. The method also includes seamlessly integrating geographies support into all xProbe dashboards and reports. The method also includes modifying existing modules to include selection and analysis of data based on predefined geographies. The present disclosure enhances xProbe dashboards to support network geographies- based analytics, providing users with valuable insights for better decision-making and analysis.

[0046] The various embodiments throughout the disclosure will be explained in more detail with reference to FIGs. 1-5.

[0047] Referring to FIG. 1, a network architecture (100) may include one or more computing devices or user equipment (104-1, 104-2... 104-N) (used interchangeably with the term “user device”) associated with one or more users (102-1, 102-2...102-N) in an environment. A personof ordinary skill in the art will understand that one or more users (102-1, 102-2... 102-N) may be individually referred to as the user (102) and collectively referred to as the users (102). Similarly, a person of ordinary skill in the art will understand that one or more user equipment (104- 1, 104-2... 104-N) may be individually referred to as the user equipment (104) and collectively referred to as the user equipment (104). A person of ordinary skill in the art will appreciate that the terms “computing device(s)” and “user equipment” may be used interchangeably throughout the disclosure. Although two user equipment (104) are depicted in FIG. 1, however any number of the user equipment (104) may be included without departing from the scope of the ongoing description.

[0048] In an embodiment, the user equipment (104) may include, but is not limited to, a handheld wireless communication device (e.g., a mobile phone, a smart phone, a phablet device, and so on), a wearable computer device (e.g., a head-mounted display computer device, a headmounted camera device, a wristwatch computer device, and so on), a global positioning system (GPS) device, a laptop computer, a tablet computer, or another type of portable computer, a media playing device, a portable gaming system, and / or any other type of computer device with wireless communication capabilities, and the like. In an embodiment, the user equipment (104) may include, but is not limited to, any electrical, electronic, electro-mechanical, or an equipment, or a combination of one or more of the above devices such as virtual reality (VR) devices, augmented reality (AR) devices, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or any other computing device, where the user equipment (104) may include one or more in-built or externally coupled accessories including, but not limited to, a visual aid device such as a camera, an audio aid, a microphone, a keyboard, and input devices for receiving input from the user (102) or the entity such as touch pad, touch enabled screen, electronic pen, and the like. A person of ordinary skill in the art will appreciate that the user equipment (104) may not be restricted to the mentioned devices and various other devices may be used.

[0049] In an embodiment, the user equipment (104) may include smart devices operating in a smart environment, for example, an Internet of Things (loT) system. In such an embodiment, the user equipment (104) may include, but is not limited to, smart phones, smart watches, smart sensors (e.g., mechanical, thermal, electrical, magnetic, etc.), networked appliances, networked peripheral devices, networked lighting system, communication devices, networked vehicle accessories, networked vehicular devices, smart accessories, tablets, smart television (TV), computers, smart security system, smart home system, other devices for monitoring or interacting with or for the users (102) and / or entities, or any combination thereof. A person of ordinary skill in the art will appreciate that the user equipment (104) may include, but is not limited to, intelligent, multi-sensing, network-connected devices, that can integrate seamlessly with each other and / or with a central server or a cloud-computing system or any other device that is net- work-connected.

[0050] Referring to FIG. 1, the user equipment (104) may communicate with the system (108) through a network (106). In an embodiment, the network (106) may include at least one of a Fifth Generation (5G) network, 6G network, or the like. The network (106) may enable the user equipment (104) to communicate with other devices in the network architecture (100) and / or with the system (108). The network (106) may include a wireless card or some other transceiver connection to facilitate this communication. In another embodiment, the network (106) may be implemented as, or include any of a variety of different communication technologies such as a wide area network (WAN), a local area network (LAN), a wireless network, a mobile network, a virtual private network (VPN), the Internet, the public switched telephone network (PSTN), or the like. In an embodiment, each of the UE (104) may have a unique identifier attribute associated therewith. In an embodiment, the unique identifier attribute may be indicative of mobile station international subscriber directory number (MSISDN), international mobile equipment identity (IMEI) number, international mobile subscriber identity (IMSI), subscriber permanent identifier (SUPI) and the like.

[0051] In an embodiment, the system (108) may receive and analyse call record data from each cell site. The call record data may include, but not limited to, call records, call duration, frequency, user location, minutes of usage, etc. The system (108) may normalize and pre-process the call record data. The system (108) may extract one or more relevant features, such as network load, time of day, and user behavior, from the pre-processed call record data to capture call patterns and user characteristics. The system (108) may feed the call record data and the extracted features to an artificial intelligence (AI) / machine learning (ML) model, and the AI / ML model may be trained based on the call record data and the extracted features. These call-related details may be included with network network-related information that may be mapped to geographies. Further, the system (108) may process the network related information to perform analytics. When the user requests for geography-based analytics, the system (108) may generate the data as per geography and provide insights gleaned from other frameworks like Supercore, R4G State (readiness state for network transition from 4G to 5G), Centre, and Point.

[0052] Although FIG. 1 shows exemplary components of the network architecture (100), in other embodiments, the network architecture (100) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 1. Additionally, or alternatively, 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).

[0053] FIG. 2A illustrates a block diagram of the system (108) for mapping and analyzing data for performing data visualization and extraction operations using pre-defined network geographies, in accordance with embodiments of the present disclosure. In an aspect, the system (108) may include one or more processor(s) (202) and a memory (204). The one or more processor(s) (202) may be implemented as one or more microprocessors, microcomputers, microcontrollers, edge or fog microcontrollers, digital signal processors, central processing units, logic circuit-ries, and / or any devices that process data based on operational instructions. Among other capabilities, the one or more processor(s) (202) may be configured to fetch and execute computer- readable instructions stored in a memory (204) of the system (108). The memory (204) may be configured to store one or more computer-readable instructions or routines in a non-transitory computer readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory (204) may include any non-transitory storage device including, for example, volatile memory such as random-access memory (RAM), or nonvolatile memory such as erasable programmable read-only memory (EPROM), flash memory, and the like.

[0054] The memory (204) may include, for example, a hard disk drive and / or a removable storage drive, representing a floppy disk drive, a magnetic tape drive, a compact disk drive, a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as EPROM or PROM), and the like, which is read by and written to by removable storage unit. As will be appreciated, the removable storage unit includes a computer usable storage medium having stored therein computer software and / or data. The removable storage drive reads from and / or writes to a removable storage unit in a well-known manner. The removable storage unit, also called a program storage device or a computer program product, represents a floppy disk, magnetic tape, compact disk, etc. The computer programs (also called computer control logic) are stored in main memory (204). Such computer programs, when executed, enable the system (108) to perform the functions of the present disclosure as discussed herein. In particular, the computer programs, when executed, enable the one or more processor (102) to perform the functions of the present disclosure. Accordingly, such computer programs represent controllers of the system (108).

[0055] Referring to FIG. 2A, the system (108) may also include an interface(s) (206). The in- terface(s) (206) may include a variety of interfaces, for example, interfaces for data input and output devices, referred to as I / O devices, storage devices, and the like. The interface(s) (206) may facilitate communication to / from the system (108). The interface(s) (206) may also providea communication pathway for one or more components of the system (108). Examples of such components include, but are not limited to, a database (210).

[0056] In an embodiment, the system (108) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities. In examples described herein, such combinations of hardware and programming may be implemented in several different ways.

[0057] In an embodiment, the system (108) may include one or more databases such as databases (210). In an embodiment, the database (210) includes data that may be either stored or generated because of functionalities implemented by any of the components of the processor (202). In an embodiment, the database (210) may be separate from the system (108). In an embodiment, the database (210) may be indicative of including, but not limited to, a relational database, a distributed database, distributed file sharing system, a cloud-based database, or the like.

[0058] In an embodiment, the system (108) includes a user interface (206), a load balancer unit (208), a workflow engine (212), a distributed computing engine (214), and a dashboard (216). The user interface (206) comprises one or more geographies selection options and one or more filtering options comprising at least one of a normal call release reason or an abnormal call release reason (CRR), one or more geographies, and the computation of data for data analysis. The one or more filtering options may refer to user interface element that provide options on the user interface for the user to make choices. In examples, the one or more filtering options may be in a form of a dropdown element, choice radio buttons, or as list of options provided on the user interface.

[0059] In embodiments, the load balancer unit (208) is configured to monitor network traffic and automatically redirecting traffic to available workflow engines based on load of each workflow engines. The load balancer unit (208) also routes traffic through a group of network firewalls for security. In current context, the load balancer unit (208) is configured for forwardinga user request to a workflow engine (212) and for forwarding the computed data comprising geography -based clear code to the UI (206). The workflow engine (212) is configured for sending computation data comprising at least one of one or more filters, a geography, and one or more error codes, to a distributed computing engine (214), based on the user request. According to one embodiment of the present technology, the workflow engine (212) performs at least one of generating or processing a forward request based on the geography selected. The distributed computing engine (214) is configured for computing data based on at least one of the geographies and one or more error codes from the computation data by using at least one of raw error code data or pre-aggregated error code data, from a distributed file system (232) associated with a database (210). The dashboard (216) and a report integration mechanism are configured for integrating one or more geographies support into a plurality of xProbe dashboards and reports.

[0060] FIG. 2B illustrates a flowchart (218) showing the mapping and analysis of data using predefined network geographies, according to an embodiment of the present disclosure.

[0061] As illustrated, at step (220), the process begins.

[0062] At step (222), a UI (206) is provided to a user. The UI (206) may provide options for the user to select one or more network, one or more geographies and / or use predefined filters.

[0063] At step (224), a load balancer may identify one or more workflow engine (212) based onload of the workflows.

[0064] At step (226), the workflow engine (212) based on the geography / time may be selected. In examples, in step (228) the workflow engine (212) may process the request, and results of the processing may be stored in a distributed data lake (234). If the workflow engine (212) does not have geographical data, then the request is forwarded to the distributed computing engine.

[0065] In step (230), the distributed computing engine computes data for selected network geography. For computing the data for the selected network geography, raw or pre-aggregatednetwork data may be fetched and used from a distributed file system (232). The results from both the processed request and the computing engine are collected at a distributed data lake (234).

[0066] FIG. 3 illustrates a process flow (300) showing mapping and analysis of data using predefined network geographies, according to an embodiment of the present disclosure.

[0067] As illustrated, a user request with more geography selection options is passed to a load balancer unit (208).

[0068] At step (304), a user (302) sends a request with one or more geography selection options to a user interface server (301).

[0069] At step (306), the request is forwarded to load balancer unit (208). The load balancer unit (208) is configured to communicate the request to an appropriate available workflow engine (212).

[0070] At step (308), the load balancer unit (208) forwards the request to the xProbe manager unit that is the workflow engine (212).

[0071] At step (314), the workflow engine (212) sends filters, geography, and error codes to a distributed computing engine (328).

[0072] At step (310), at the workflow engine (212), geographical mapping, dashboard, report integration, CRR management, and data formatting are performed. For example, the workflow engine (212) may obtain the user-selected one or more geographies and identifies regions or areas relevant to the network (e.g., cities, regions, countries). In aspects, the workflow engine (212) may gather data related to network performance, coverage, or infrastructure associated with the one or more geographies. The workflow engine (212) may ensure data points have geographic attributes (e.g., GPS coordinates). The workflow engine (212) may map data points to the geographies to generate geographical mapping. The geographical mapping may beprocessed for the dashboard and report integration. For example, the workflow engine (212) may also gather call release reasons and abnormal call release reasons as a part of gathering data related to network performance.

[0073] At step (312), based on the geography selected, the workflow engine (212) may decide to perform compute information for the geographical mapping or generate forward request. In examples, for some select geographies, the workflow engine (212) may forward the request to the distributed computing engine (214) at a computation layer (328).

[0074] At step (316), at the computation layer (328), based on the geography selected, data collection and computation may be done for the selected geography and the error code, while receiving raw error code data (318) from a distributed file system (232). The raw error code data consists of numerical codes generated by network elements to indicate specific errors or issues. The raw error codes may help indicate in the dashboard or the computed data, the error and related analytics. For example, the raw error codes captured in the network are generally stored in the distributed file system, although they can be stored in another database as well.

[0075] At step (320), computed data is sent from the distributed computing engine (328) via a distributed data lake (234) to the workflow engine (212) in one embodiment. The computation layer (328) may include the distributed computing engine (214). In some embodiments, the computed data may be communicated directly to the workflow engine (212), directly.

[0076] At step (322), geography -based clear code data along with notification is sent to the load balancer unit (208) and is forwarded to the UI (206) (at step (324)) and to the user (302) at step (326). At the UI (206), the user may be enabled to view and perform geography related analytics using filters, one or more geography options, etc. For example, the user may be able to obtain insights and information at various network levels and frameworks such as Supercore, R4G State, Centre, Point, etc.

[0077] FIG. 4 illustrates a flowchart (400) of a method for mapping and analyzing data for performing data visualization and extraction operations using pre-defined network geographies,according to an embodiment of the present invention.

[0078] At step (402), a request through a user interface (UI) of a user device may be received by a workflow engine (212), the request comprising at least one of a user selection of one or more geography and one or more filtering options. The one or more filtering options comprising: a normal call release reason or an abnormal call release reason (CRR), one or more geographies, and computation of data for data analysis.

[0079] At step (404), the request may be processed to map data associated with the one or more selected geographies with network data and communicate the processed request to a distributed computing engine (214), may be performed by the workflow engine (212).

[0080] At step (406), the mapped data for the selected one or more geographies based on the request may be aggregated and computed, by the distributed computing engine (214) distributed computing engine.

[0081] At step (408), the computed data may be communicated by the distributed computing engine (214) to the workflow engine (212) distributed computing engine.

[0082] At step (410), the computed data comprising geography-based network information on the UI (206) may be rendered by the workflow engine (212) distributed computing engine.

[0083] According to one embodiment of the present technology, sending the computation data from the workflow engine comprises performing, by the workflow engine, at least one of: a geographical mapping, a dashboard and report integration, a call release reason (CRR) management, and a data formatting, performing one of: generating or processing, by the workflow engine, a forward request based on the geography selected and generating the computation data based on the forward request.

[0084] According to one embodiment of the present technology, the method further comprises enhancing xProbe user interface to support network geographies -based analytics and providingthe user with valuable insight for decision-making and analysis. The UI (206) may be configured to include various frameworks such as Supercore, R4G State, Centre, and Point. The user may be able to use any of the frameworks, change geographies, and use different filters to anaylze the network information associated with the geographies.

[0085] In an exemplary embodiment, the present disclosure discloses the user equipment (UE) (104) configured for tracking a network performance summary and subscriber activity within a network. The user equipment (104) includes the processor (202) and a computer-readable storage medium storing programming for execution by the processor (202). The programming includes instructions to the UE configured for receiving a request to map network data, including call release reasons, abnormal call release reasons and error codes, to specific geographies. This step involves linking network performance data to geographic regions, providing essential context for further analysis. The workflow engine (212) ensures that data points (e.g., network information, etc.), such as CRR and error codes, are accurately associated with their respective geographies, enabling a comprehensive understanding of network behavior across different areas. Once geographies are mapped, the workflow engine (212) communicates this information, including raw error code data and CRR, to a distributed computing engine. The workflow engine (212) is equipped to handle large-scale data processing tasks by leveraging multiple nodes for efficient computation. By distributing the workload, the distributed computing engine (214) aggregates raw network data and pre-aggregated error codes, processing them to derive insights into network performance and stability across various regions.

[0086] The distributed computing engine (214) aggregates data related to mapped geographies, performing computations on CRR, raw error codes, and other network metrics. This involves identifying patterns in call releases and error occurrences and analyzing them to pinpoint issues affecting network reliability and user experience. The distributed computing engine (214) capability to process large volumes of data ensures timely and accurate insights, highlighting areas needing improvement and optimizing network operations. Following computation, thedistributed computing engine (214) sends the processed data back to the workflow engine (212). This data, enriched with geography-based insights and analysis of CRR and error codes, is communicated to the UI (206). The workflow engine (212) renders this information on the UI, transforming complex datasets into intuitive visualizations and reports that facilitate user understanding and decision-making.

[0087] The UI (206) serves as a critical platform for exploring and visualizing geography -based network information. Through interactive dashboards, users can analyze CRR patterns, error code distributions, and raw network data, gaining insights into how different regions perform. This visual representation enables stakeholders to identify problem areas, optimize network resources, and enhance service quality by leveraging geographic and performance data effectively. The integration of CRR and error data into the analysis provides a detailed view of network challenges, guiding strategies for improvement, and ensuring robust network management.

[0088] FIG. 5 illustrates an exemplary computer system (500) in which or with which embodiments of the present disclosure may be implemented. As shown in FIG. 5, the computer system (500) may include an external storage device (510), a bus (520), a main memory (530), a readonly memory (540), a mass storage device (550), a communication port (560), and a processor (570). A person skilled in the art will appreciate that the computer system (500) may include more than one processor (570) and communication ports (560). The processor (570) may include various modules associated with embodiments of the present disclosure.

[0089] In an embodiment, the communication port (560) may be any of an RS -232 port for use with a modem-based dialup connection, a 10 / 100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fibre, a serial port, a parallel port, or other existing or future ports. The communication port (560) may be chosen depending on the network (106), such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system (500) connects.

[0090] In an embodiment, the memory (530) may be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. Read-only memory (540) may be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or Basic Input / Output System (BIOS) instructions for the processor (570).

[0091] In an embodiment, the mass storage (550) may be any current or future mass storage solution, which may be used to store information and / or instructions. Exemplary mass 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., having Universal Serial Bus (USB) and / or Firewire interfaces), one or more optical discs, Redundant Array of Independent Disks (RAID) storage, e.g., an array of disks (e.g., SATA arrays).

[0092] In an embodiment, the bus (520) communicatively couples the processor(s) (570) with the other memory, storage, and communication blocks. The bus (520) may be, e.g., a Peripheral Component Interconnect (PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), Universal Serial Bus (USB) or the like, for connecting expansion cards, drives and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor (570) to the computer system (500).

[0093] Optionally, operator and administrative interfaces, e.g., a display, keyboard, joystick, and cursor control device, may also be coupled to the bus (520) to support direct operator interaction with the computer system (500). Other operator and administrative interfaces may be provided through network connections connected through the communication port (560). The components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system (500) limit the scope of the present disclosure.TECHNICAL ADVANCEMENT OF THE PRESENT DISCLOSURE

[0094] The present disclosure provides technical advancement compared to existing art by extending data extraction capabilities to extract specific data subsets based on chosen geographies. The present disclosure performs implementation of enhancing the user interface with more geography’s selection and filtering options (normal / abnormal CRR, geographies, etc) and computation of huge data for data analysis. The present disclosure implements visualizations like bar chart, line chart and region-specific charts to display data based on selected geographies.

[0095] Following the process flow steps effectively enhances xProbe user interface(s) to support network geographies-based analytics, providing the users with valuable insights for better decision-making and analysis. The disclosed system and method provide an enhanced data analysis by enabling the inclusion of different network geographies for data visualization and extraction. This analysis provides a more comprehensive and granular analysis of network data that helps the users to analyze the performance, trends, and patterns specific to geography, enabling better decision-making and troubleshooting.

[0096] In addition, by having access to network geographies-based analytics, the users may have the capability to make more informed decisions regarding network optimization, resource allocation, and infrastructure planning. They may identify specific areas or regions where the network performance is lacking or exceeding expectations, allowing for targeted interventions and improvements.

[0097] Also, an ability to extract data based on different network geographies may allow for customized reporting tailored to specific areas of interest. Stakeholders may generate reports specific to the geography, providing localized insights and facilitating better communication and collaboration among teams.

[0098] Further, with the inclusion of network geographies-based analytics, it may become easier to monitor the performance of different regions or specific network segments. This may enable proactive identification of the network issues, such as congestion or service outages, and facilitate timely resolution to minimize customer impact. Furthermore, the availability ofnetwork geographies-based analytics may assist in optimizing resource allocation. By analyzing the performance metrics and user behavior within specific geographies, network operators may efficiently allocate resources and capacity, ensuring a better user experience and reduction in operational costs. It may be appreciated that the proposed enhancement may allow for the inclusion of additional network geographies as per requirements. This flexibility may ensure the analytics solution to be scaled and adapted to changing network configurations, expansion into new regions, or introduction of new service areas. Overall, the disclosed system and method provide a comprehensive, localized, and targeted analysis of the network data, leading to improved decision-making, better resource utilization, and enhanced customer experience.

[0099] While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the disclosure and not as limitation.ADVANTAGES OF THE PRESENT DISCLOSURE

[0100] The present disclosure provides a system and a method for mapping and analyzing data for performing data visualization and extraction operations using pre-defined network geographies.

[0101] The present disclosure develops a mapping mechanism to link network enriched data with network geographies from raw network data for analysis and visualization.

[0102] The present disclosure extends data extraction capabilities to extract specific data subsets based on chosen geographies.

[0103] The present disclosure provides an enhanced User Interface (UI) for selection and filtering of options related to additional geographies (such as normal Call Release Reason (CRR) / abnormal CRR, geographies, etc.,), and computation of huge data for data analysis.

[0104] The present disclosure implements visual representations like bar chart, line chart, and region-specific charts to display data based on the selected geographies.

[0105] The present disclosure provides a comprehensive, localized, and targeted analysis of the network data, leading to improved decision-making, better resource utilization, and enhanced customer experience. The inclusion of different network geographies for data visualization and extraction in xProbe dashboards provides a more comprehensive and granular analysis of network data.

[0106] The present disclosure enables the users to analyse the performance, trends, and patterns specific to Supercore, Circle, R4G State, Centre, and Point, enabling better decisionmaking and troubleshooting. By having access to network geographies-based analytics, users can make more informed decisions regarding network optimization, resource allocation, and infrastructure planning. They can identify specific areas or regions where network performance is lacking or exceeding expectations, allowing for targeted interventions and improvements.

[0107] The present disclosure extracts data based on different network geographies allows for customized reporting tailored to specific areas of interest. With the inclusion of network geographies-based analytics, it becomes easier to monitor the performance of different regions or specific network segments.

[0108] The present disclosure enables proactive identification of network issues, such as congestion or service outages, and facilitates timely resolution to minimize customer impact. The availability of network geographies-based analytics assists in optimizing resource allocation. By analyzing the performance metrics and user behaviour within specific geographies, network operators can allocate resources and capacity more efficiently, ensuring a better user experience and reducing operational costs.

[0109] The present disclosure provides an enhancement to xProbe dashboards which allows for the inclusion of additional network geographies as per requirements. This flexibility ensures that the analytics solution can scale and adapt to changing network configurations, expansion into new regions, or the introduction of new service areas.

[0110] The present disclosure has the ability to provide a comprehensive, localized, and targeted analysis of network data, leading to improved decision-making, better resource utilization, and enhanced customer experience.

Claims

We Claim:

1. A method (400) for performing geography -based analytics, the method comprising: receiving (402), by a workflow engine (212), a request through a user interface (UI) of a user device, the request comprising at least one of a user selection of one or more geography and one or more filtering options, wherein the one or more filtering options comprising: a normal call release reason or an abnormal call release reason (CRR), one or more geographies, and computation of data for data analysis; processing (404), by the workflow engine (212), the request, to map information associated with the one or more selected geographies with network data and communicate the processed request, to a distributed computing engine (214); aggregating and computing (406), by the distributed computing engine (214), data associated with the mapped information for the selected one or more geographies based on the request; communicating (408), by the distributed computing engine (214), the computed data to the workflow engine (212), the computed data comprising geography-based network information to the UI (206); and rendering (410), by the workflow engine (212), the computed data comprising geography-based network information on the UI (206).

2. The method of claim 1, wherein the computed data comprises enriched network data comprising user-select geographies mapped with data points.

3. The method (400) of claim 1, further comprising:performing, by the workflow engine (212), at least one of: a geographical mapping, a dashboard (110) and report integration, a call release reason (CRR) management, and a data formatting; performing one of: generating or processing, by the workflow engine (212), a forward request based on the geography selected; and generating the computation data based on the forward request.

4. The method (400) of claim 1, further comprising enhancing user interface to support network geographies -based analytics, and providing the user with an insight based on for decision-making and analysis.

5. A system (108) for performing geography-based analytics, the system (108) comprising: a user interface (UI) (206) comprising at least one of one or more geographies selection options and one or more filtering options, wherein the one or more filtering options comprising at least one of: a normal call release reason or an abnormal call release reason (CRR), one or more geographies, and computation of data for data analysis; a workflow engine (212) configured to: receive a request through the user interface, the request comprising at least one of a user selection of at least one of the one or more geographies and the one or more filtering options; and process the request, to map information associated with the one or more selected geographies with network data and communicate the processed request, to a distributed computing engine (214); the distributed computing engine (214) configured to: aggregate and compute associated with the mapped information for the selected one or more geographies based on the request; and communicate the computed data to the workflow engine (212), the computed data comprising geography -based network information to the UI (206); and the workflow engine (212) is further configured to:render the computed data comprising geography-based network information on the UI (206).

6. The system (108) of claim 4, wherein the workflow engine (212) performs at least one of: generating or processing a forward request based on the geography selected.

7. The system (108) of claim 4, further comprising a dashboard (216) and a report integration mechanism, for integrating one or more geographies support into a plurality of xProbe dashboards and reports.

8. A computer program product comprising a non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of: receiving (402), by a workflow engine (212), a request through a user interface (UI) of a user device, the request comprising at least one of a user selection of one or more geography and one or more filtering options, wherein the one or more filtering options comprising: a normal call release reason or an abnormal call release reason (CRR), one or more geographies, and computation of data for data analysis; processing (404), by the workflow engine (212), the request, to map information associated with the one or more selected geographies with network data and communicate the processed request, to a distributed computing engine (214); aggregating and computing (406), by the distributed computing engine (214), data associated with the mapped information for the selected one or more geographies based on the request; communicating (410), by the distributed computing engine (214), the computed data to the workflow engine (212), the computed data comprising geography-based network information to the UI (206); and rendering (412), by the workflow engine (212), the computed data comprising geography-based network information on the UI (206).

9. A user equipment (UE) ( 104) configured for mapping and analyzing data for performing data visualization and extraction operations using pre-defined network geographies, the user equipment (104) comprising: a processor (202); and a computer readable storage medium storing programming for execution by the processor (202), the programming including instructions to: receiving (402), by a workflow engine (212), a request through a user interface of the UE, the request comprising at least one of a user selection of one or more geography and one or more filtering options, wherein the one or more filtering options comprising: a normal call release reason or an abnormal call release reason (CRR), one or more geographies, and computation of data for data analysis; responsive to the request, receiving and rendering (412), on the UI, the computed data comprising geography-based network information on the UI (206) based on processing the re- quest using the method (400) for performing geography -based analytics as claimed in the claim1.

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