DESC:
FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003

COMPLETE SPECIFICATION
(See section 10 and rule 13)
1. TITLE OF THE INVENTION
METHOD AND SYSTEM FOR DATA MANAGEMENT IN A NETWORK
2. APPLICANT(S)
NAME NATIONALITY ADDRESS
JIO PLATFORMS LIMITED INDIAN OFFICE-101, SAFFRON, NR. CENTRE POINT, PANCHWATI 5 RASTA, AMBAWADI, AHMEDABAD 380006, GUJARAT, INDIA
3.PREAMBLE TO THE DESCRIPTION

THE FOLLOWING SPECIFICATION PARTICULARLY DESCRIBES THE NATURE OF THIS INVENTION AND THE MANNER IN WHICH IT IS TO BE PERFORMED.

FIELD OF THE INVENTION
[0001] The present invention relates to telecommunication networks, more particularly relates to a method and a system for data management in a network.
BACKGROUND OF THE INVENTION
[0002] The modern communication systems, especially cellular networks, have experienced exponential growth in recent years. With the proliferation of smartphones, Internet of Things (IoT) devices, and other connected devices, enormous amounts of data are being generated and transmitted over these networks. This data encompasses various types, including text, images, videos, sensor readings, and more, which are generated by users, devices, and applications.
[0003] Traditionally, the data generated by users and devices in cellular networks is transmitted to centralized systems for processing and analysis. This approach entails significant challenges and limitations. Firstly, the sheer volume of data generated can overload the network infrastructure, leading to congestion and increased latency. Secondly, the centralized computation of all data requires substantial computational resources and may result in processing delays. Thirdly, in the event of failures or outages at the centralized location, data loss and service disruptions can occur. Overall, the existing systems, which ships the data generated at the edges directly to the centralized location for further computation, leads to delay, excess bandwidth utilization, heavy network infrastructure and the entire process is prone to multiple failures at network or interface level.
[0004] Some techniques, such as distributed computing and cloud-based architectures aim to distribute data processing computation across multiple nodes or utilize cloud resources for scalable processing. However, these solutions still involve significant network traffic and rely on centralized systems for processing, thus limiting their effectiveness in mitigating latency, bandwidth, and reliability challenges.
[0005] Therefore, there is a clear need of a solution which may not require to ship the data generated at the devices and/or edges to the central location for direct computation that may result in processing delay, excess bandwidth consumption, and being prone to multiple failures at network or interface level. Further, there is a need of a solution having intelligence to determine distribution of the computing tasks across multiple nodes and the central location that may result into effectiveness with mitigated latency, optimized bandwidth usage, and reliability.
SUMMARY OF THE INVENTION
[0006] One or more embodiments of the present disclosure provide a method and system for data management in a network.
[0007] In one aspect of the present invention, the system for data management in the network is disclosed. The system includes an aggregation unit configured to aggregate data at an edge in the network based on a request received from a user. The system further includes a generation unit configured to generate a summarized record of the data at the edge. The system further includes a transceiver, configured to, transmit, the generated summarized record from the edge to a computation engine to perform at least one of, data computation and analysis.
[0008] In an embodiment, the data include at least one of, cell level data, subscriber level data, r4gstate data, maintenance zone data, and center-wise data.
[0009] In an embodiment, the data computation is performed using a customized computation layer in the computation engine. The computation is performed on the summarized record for one or more geographies at predefined time periods.
[0010] In another aspect of the present invention, the method for data management in the network is disclosed. The method includes the step of aggregating data at an edge in the network based on a request received from a user. The method further includes the step of generating a summarized record of the data at the edge. The method further includes the step of transmitting, by the one or more processors, the generated summarized record from the edge to a computation engine to perform at least one of, data computation and analysis.
[0011] In another aspect of the invention, a non-transitory computer-readable medium having stored thereon computer-readable instructions is disclosed. The computer-readable instructions are executed by a processor. The processor is configured to aggregate data at an edge in a network based on a request received from a user. The processor is configured to generate a summarized record of the data at the edge. The processor is configured to transmit the generated summarized record from the edge to a computation engine to perform at least one of, data computation and analysis.
[0012] In another aspect of invention, User Equipment (UE) is disclosed. The UE includes one or more primary processors communicatively coupled to one or more processors, the one or more primary processors coupled with a memory. The processor causes the UE to transmit a request from the user to the one or more processors related to a specific requirement of the data.
[0013] Other features and aspects of this invention will be apparent from the following description and the accompanying drawings. The features and advantages described in this summary and in the following detailed description are not all-inclusive, and particularly, many additional features and advantages will be apparent to one of ordinary skill in the relevant art, in view of the drawings, specification, and claims hereof. Moreover, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes and may not have been selected to delineate or circumscribe the inventive subject matter, resort to the claims being necessary to determine such inventive subject matter.
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] 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 disclosure of electrical components, electronic components or circuitry commonly used to implement such components.
[0015] FIG. 1 is an exemplary block diagram of an environment for data management in a network, according to one or more embodiments of the present invention;
[0016] FIG. 2 is an exemplary block diagram of a system for data management in the network, according to one or more embodiments of the present invention;
[0017] FIG. 3 is a schematic representation of a workflow of the system of FIG. 1, according to the one or more embodiments of the present invention;
[0018] FIG. 4 is an exemplary block diagram of an architecture implemented in the system of the FIG. 2, according to one or more embodiments of the present invention;
[0019] FIG. 5 is a signal flow diagram for data management in the network, according to one or more embodiments of the present invention; and
[0020] FIG. 6 is a schematic representation of a method for data management in the network, according to one or more embodiments of the present invention.
[0021] The foregoing shall be more apparent from the following detailed description of the invention.
DETAILED DESCRIPTION OF THE INVENTION
[0022] Some embodiments of the present disclosure, illustrating all its features, will now be discussed in detail. It must also be noted that as used herein and in the appended claims, the singular forms "a", "an" and "the" include plural references unless the context clearly dictates otherwise.
[0023] Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure including the definitions listed here below are not intended to be limited to the embodiments illustrated but is to be accorded the widest scope consistent with the principles and features described herein.
[0024] A person of ordinary skill in the art will readily ascertain that the illustrated steps detailed in the figures and here below are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[0025] The present invention addresses the challenges associated with data aggregation and computation in legacy systems, where data is traditionally sent to centralized locations for processing. The above-mentioned approach often requires high network infrastructure, leading to increased latency and bandwidth consumption. In contrast, the present invention discloses a novel solution called data aggregation at edge and centralized computation, which optimizes data processing in cellular networks. The data aggregation at edge and centralized computation enables the aggregation of cell level data and subscriber level data at the edge of the network, near the data sources. At the edge, the data is summarized, transforming it into condensed information or records. These summarized records are then transmitted to a centralized location for further computation and analysis.
[0026] FIG. 1 illustrates an exemplary block diagram of an environment 100 for data management in a network, according to one or more embodiments of the present disclosure. In this regard, the environment 100 includes a User Equipment (UE) 102, a server 104, a network 106 and a system 108 communicably coupled to each other for data management in the network 106.
[0027] As per the illustrated embodiment and for the purpose of description and illustration, the UE 102 includes, but not limited to, a first UE 102a, a second UE 102b, and a third UE 102c, and should nowhere be construed as limiting the scope of the present disclosure. In alternate embodiments, the UE 102 may include a plurality of UEs as per the requirement. For ease of reference, each of the first UE 102a, the second UE 102b, and the third UE 102c, will hereinafter be collectively and individually referred to as the “User Equipment (UE) 102”.
[0028] In an embodiment, the UE 102 is one of, but not limited to, any electrical, electronic, electro-mechanical or an equipment and a combination of one or more of the above devices such as a smartphone, 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.
[0029] The environment 100 includes the server 104 accessible via the network 106. The server 104 may include, by way of example but not limitation, one or more of a standalone server, a server blade, a server rack, a bank of servers, a server farm, hardware supporting a part of a cloud service or system, a home server, hardware running a virtualized server, one or more processors executing code to function as a server, one or more machines performing server-side functionality as described herein, at least a portion of any of the above, some combination thereof. In an embodiment, the entity may include, but is not limited to, a vendor, a network operator, a company, an organization, a university, a lab facility, a business enterprise side, a defense facility side, or any other facility that provides service.
[0030] The network 106 includes, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, or some combination thereof. The network 106 may include, but is not limited to, a Third Generation (3G), a Fourth Generation (4G), a Fifth Generation (5G), a Sixth Generation (6G), a New Radio (NR), a Narrow Band Internet of Things (NB-IoT), an Open Radio Access Network (O-RAN), and the like.
[0031] The network 106 may also include, by way of example but not limitation, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth. The network 106 may also include, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, a VOIP or some combination thereof.
[0032] The environment 100 further includes the system 108 communicably coupled to the server 104 and the UE 102 via the network 106. The system 108 is configured to manage the data in the network 106. As per one or more embodiments, the system 108 is adapted to be embedded within the server 104 or embedded as an individual entity.
[0033] Operational and construction features of the system 108 will be explained in detail with respect to the following figures.
[0034] FIG. 2 is an exemplary block diagram of the system 108 for data management in the network 106, according to one or more embodiments of the present invention.
[0035] As per the illustrated embodiment, the system 108 includes one or more processors 202, a memory 204, a user interface 206, and a database 208. For the purpose of description and explanation, the description will be explained with respect to one processor 202 and should nowhere be construed as limiting the scope of the present disclosure. In alternate embodiments, the system 108 may include more than one processor 202 as per the requirement of the network 106. The one or more processors 202, hereinafter referred to as the processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, single board computers, and/or any devices that manipulate signals based on operational instructions.
[0036] As per the illustrated embodiment, the processor 202 is configured to fetch and execute computer-readable instructions stored in the memory 204. 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 RAM, or non-volatile memory such as disk memory, EPROMs, FLASH memory, unalterable memory, and the like.
[0037] In an embodiment, the user interface 206 includes a variety of interfaces, for example, interfaces for a graphical user interface, a web user interface, a Command Line Interface (CLI), and the like. The user interface 206 facilitates communication of the system 108. In one embodiment, the user interface 206 provides a communication pathway for one or more components of the system 108. Examples of such components include, but are not limited to, the UE 102 and the database 208.
[0038] The database 208 is one of, but not limited to, a centralized database, a cloud-based database, a commercial database, an open-source database, a distributed database, an end-user database, a graphical database, a No-Structured Query Language (NoSQL) database, an object-oriented database, a personal database, an in-memory database, a document-based database, a time series database, a wide column database, a key value database, a search database, a cache databases, and so forth. The foregoing examples of database 208 types are non-limiting and may not be mutually exclusive e.g., a database can be both commercial and cloud-based, or both relational and open-source, etc.
[0039] In order for the system 108 to manage data in the network 106, the processor 202 includes one or more modules. In one embodiment, the one or more modules include, but not limited to, an aggregation unit 210, a generation unit 212, and a transceiver 214, communicably coupled to each other for data management in the network 106.
[0040] In one embodiment, each of the aggregation unit 210, the generation unit 212, and the transceiver 214 can be used in combination or interchangeably for data management in the network 106.
[0041] The aggregation unit 210, the generation unit 212, and the transceiver 214 in an embodiment, may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processor 202. In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processor 202 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processor may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the memory 204 may store instructions that, when executed by the processing resource, implement the processor. In such examples, the system 108 may comprise the memory 204 storing the instructions and the processing resource to execute the instructions, or the memory 204 may be separate but accessible to the system 108 and the processing resource. In other examples, the processor 202 may be implemented by electronic circuitry.
[0042] In one embodiment, the aggregation unit 210 is configured to aggregate data at an edge in the network 106 based on a request received from a user. In an embodiment, the edge in the network 106 refers to the boundary where data is collected and processed closer to the source of data generation, rather than being sent to a centralized data center or cloud for processing. The request refers to an instruction or command initiated by the user to perform a specific action or to retrieve specific data. The specific action refers to a particular operation or task initiated by the user. The specific data refers to distinct categories of information that the system can collect, process, summarize, and transmit based on the user requests. The request from the user includes, but not limited to, data aggregation request, data summarization request, data transmission request. The user is at least one of, but not limited to, network operator, subscriber, and administrator.
[0043] In an embodiment, the data include at least one of, cell level data, subscriber level data, r4gstate data, maintenance zone data, and center-wise data. The cell level data refers to information collected from individual cell towers or base stations in a cellular network. The cell level data includes, but is not limited to, signal strength, interference levels, and handover statistics. The subscriber level data includes information related to individual users or UE 102 connected to the network 106. The subscriber level data includes, but is not limited to, usage patterns, location information, service quality, subscription details. The r4gstate data refers to state information of the 4th Generation (4G) Radio Access Network (RAN) elements, such as base stations and network controllers. The r4gstate data includes, but is not limited to, connection states, resource allocation, network events, performance metrics such as throughput, latency and error rates. The maintenance zone data pertains to information related to specific maintenance zones within the network 106, which are designated areas for operational and maintenance purposes. The maintenance zone data includes, but is not limited to, maintenance activities, faults and repairs, equipment status, work orders. The center-wise data refers to information aggregated at higher-level operational centers or regions within the network 106. The center-wise data includes, but is not limited to, regional performance, aggregated usage, service levels, and incident reports.
[0044] In an embodiment, the data is aggregated at the edge in the network 106 based on the aggregation techniques. The aggregation techniques include, at least one of, but not limited to, summarization, windowing, spatial aggregation, hierarchical aggregation, etc. The summarization refers to creating summary statistics such as averages, counts, totals etc., for example, the summarization technique calculates the average signal strength and data usage for each cell tower every hour. The windowing refers to aggregating data over specific time windows to create time-based summaries, for example, the windowing technique uses a sliding window to aggregate network performance data every 5 minutes, updating every minute, to detect real-time issues. The spatial aggregation refers to aggregating data based on geographic locations or regions. The hierarchical aggregation refers to aggregating data at different levels of hierarchy, such as cell level, zone level, and center-wise level.
[0045] Upon aggregating the data at the edge in the network 106, the generation unit 212 is configured to generate a summarized record of the data at the edge. In particular, the generation unit 212 generates the summarized record of the aggregated data at the edge. The generation unit 212 generates the summarized record of the aggregated data at the edge by statistical summarization i.e., by calculating the statistical metrics such as average, sum, median etc., to condense the data and organizing the data into groups based on attributes like cell level, subscriber level or geographic zones.
[0046] For example, if the user requests cell level data summarization for the previous day, the aggregation unit 210 aggregates the data by collecting the data from cell towers and integrates the collected data. Thereafter, the generation unit 212 generates the summarized record of the aggregated data by calculating average signal strength, total data usage and number of active users of each cell tower and grouping the data by cell tower ID and time intervals (for example, hourly).
[0047] Upon generating the summarized record of the aggregated data, the transceiver 214 is configured to transmit the generated summarized record of the aggregated data from the edge to a computation engine. The generated summarized record of the aggregated data from the edge is transmitted to the computation engine to perform at least one of data computation and analysis.
[0048] In an embodiment, the computation engine is a component responsible for processing and analyzing data received from the network edge. The computation engine performs various computational tasks on the received summarized record of the aggregated data. The various tasks include, but are not limited to, complex calculations, statistical analyses, data modeling, and other forms of data processing to extract meaningful insights or derive actionable information. The computation engine is also responsible for analyzing the data. The analyzing of data includes, but is not limited to, pattern recognition, trend analysis, and anomaly detection. The pattern recognition involves identifying recurring sequences or regularities in data. The trend analysis is the process of evaluating data over time to identify consistent upward or downward movements, revealing long-term directions or tendencies within the dataset. The anomaly detection involves identifying data points that deviate significantly from the norm or expected patterns, which could indicate errors, rare events, or potential issues.
[0049] The data computation is performed using a customized computation layer in the computation engine. The computation is performed on the summarized record for one or more geographies at predefined time periods.
[0050] In an embodiment, the data computation involves processing and manipulating data to derive meaningful insights or results. The data computation process includes, but is not limited to, data ingestion, data segmentation, algorithm application, resource optimization, analysis and reporting. For example, to compute the summarized records of the data, the summarized records (such as network usage statistics, call drop rates etc.) are ingested from various regions. Upon gathering the summarized records of the data, the data is segmented based on geographic regions. Thereafter, the data is organized according to predefined time periods (such as hourly, daily, weekly). Subsequently, depending on the data type and analysis goals, the computation engine applies specialized algorithms. The specialized algorithms incudes, but are not limited to statistical analysis, machine learning algorithm, data mining, time series analysis. The statistical analysis includes calculating mean, median, mode, standard deviation, and other summary statistics to understand data distribution and variability. The machine learning includes applying models like linear regression, decision trees, or neural networks for predictive analysis, classification, and anomaly detection. The data mining includes using of techniques such as clustering, association rule learning, and anomaly detection to uncover hidden patterns and relationships within the data. The time series analysis includes analyzing trends and make forecasts based on historical data. Upon applying the specialized algorithms, the computational resources are allocated to handle high data volumes efficiently, ensuring timely analysis. Thereafter, the computation results are processed and formatted into reports and visualizations by the reporting and visualization module. The generated reports provide actionable insights that can be used for network optimization, predictive maintenance, capacity planning, and improving user experience.
[0051] The customized computation layer refers to a specialized layer within a computation engine designed to perform customized processing and analysis on data according to specific requirements or rules defined by the user or application. The data computation process includes, but not limited to, data ingestion, geographic segmentation, time-based processing, algorithm execution, resource optimization, and analysis and reporting. More specifically, the summarized record of the aggregated data is ingested into the customized computation layer. Upon ingesting the summarized record of the aggregated data, the customized computation layer contains customized algorithms and processing rules that are specific to the type of data and the desired outcomes. The customized algorithms include, but are not limited to, statistical analysis, machine learning models, or other data processing techniques. Further, the computation is performed on the summarized record of the aggregated data for specified geographies at predefined time periods. In particular, the customized computation layer filters and processes the data according to geographic regions and time intervals. The predefined time periods refer to specific intervals or durations of time that have been set in advance for performing data computation and analysis, for example, hourly, daily, weekly, monthly etc. Thereafter, the customized computation layer generates the output in the form of analyzed data or reports.
[0052] In an exemplary embodiment, a telecom network wants to optimize its performance during peak hours. Therefore, the system 108 deployed at the edge of the network 106 collects data from various cell towers and subscriber devices. The data includes but is not limited to, cell level data, subscriber level data, R4G state data, maintenance zone data, and center-wise data. Upon collecting the data, the data is aggregated based on at least one of signal strength, bandwidth usage and network traffic. Upon aggregating the data, the summarized record of the aggregated data is generated. The summarized record of the aggregated data includes, but is not limited to, average signal strength per cell tower, total data usage per subscriber, average call drop rate and maintenance alerts summary. Thereafter, the summarized record of the aggregated data is transmitted to the computation engine to perform at least one of, data computation and analysis. The computation engine includes the customized computation layer to process the summarized records. Further, the computation engine performs data computation to analyze network performance across different geographies at different time periods. For example, the computation engine calculates the overall network performance metrics, identifies patterns in network usage, and predicts future maintenance needs.
[0053] Therefore, by leveraging data aggregation at the edge and subsequent centralized computation, the system 108 optimizes network performance, reduces latency and bandwidth requirements, and ensures reliable data processing and analysis even in the face of failure scenarios.
[0054] FIG. 3 describes a preferred embodiment of the system 108 of FIG. 2, according to various embodiments of the present invention. It is to be noted that the embodiment with respect to FIG. 3 will be explained with respect to the first UE 102a and the system 108 for the purpose of description and illustration and should nowhere be construed as limited to the scope of the present disclosure.
[0055] As mentioned earlier in FIG. 1, each of the first UE 102a, the second UE 102b, and the third UE 102c may include an external storage device, a bus, a main memory, a read-only memory, a mass storage device, communication port(s), and a processor. The exemplary embodiment as illustrated in FIG. 3 will be explained with respect to the first UE 102a without deviating from the scope of the present disclosure and the limiting the scope of the present disclosure. The first UE 102a includes one or more primary processors 302 communicably coupled to the one or more processors 202 of the system 108.
[0056] The one or more primary processors 302 are coupled with a memory 304 storing instructions which are executed by the one or more primary processors 302. Execution of the stored instructions by the one or more primary processors 302 enables the first UE 102a to transmit the request from the user to the one or more processors 202 related to a specific requirement of data.
[0057] As mentioned earlier in FIG. 2, the one or more processors 202 of the system 108 is configured for processing data in the network 106. As per the illustrated embodiment, the system 108 includes the one or more processors 202, the memory 204, the user interface 206, and the database 208. The operations and functions of the one or more processors 202, the memory 204, the user interface 206, and the database 208 are already explained in FIG. 2. For the sake of brevity, a similar description related to the working and operation of the system 108 as illustrated in FIG. 2 has been omitted to avoid repetition.
[0058] Further, the processor 202 includes the aggregation unit 210, the generation unit 212, and the transceiver 214. The operations and functions of the aggregation unit 210, the generation unit 212, and the transceiver 214 are already explained in FIG. 2. Hence, for the sake of brevity, a similar description related to the working and operation of the system 108 as illustrated in FIG. 2 has been omitted to avoid repetition. The limited description provided for the system 108 in FIG. 3, should be read with the description as provided for the system 108 in the FIG. 2 above, and should not be construed as limiting the scope of the present disclosure.
[0059] FIG. 4 is an exemplary block diagram of an architecture 400 of the system 108 for processing data in the network 106, according to one or more embodiments of the present invention.
[0060] The architecture 400 includes a computation engine 402, file system 404 and the database 208.
[0061] In an embodiment, the data is aggregated at the edge in the network 106 based on the user requests. Each edge unit aggregates data relevant to its specific location. Upon aggregating the data at the edge in the network 106, the summarized record of the aggregated data is generated. The generating of the summarized record of the aggregated data reduces the size of the data.
[0062] Thereafter, the summarized record of the aggregated data is transmitted to computation engine 402. In specific, the aggregated and summarized data is transmitted to a customized computation layer of the computation engine 402. The customized computation layer performs computations on the summarized record of the aggregated data. The computation includes, but is not limited to, statistical analysis, algorithmic processing and machine learning based predictions.
[0063] In an embodiment, the computations are performed at predefined time periods such as hourly, weekly daily etc., to ensure regular and timely analysis of the data. In an embodiment, the computation is also performed on geographical segmented data for detailed analysis of data specific to various regions.
[0064] The results of the computation of the summarized record of the aggregated data are stored in the file system 404 and the database 208. The stored data is available for future analysis, reporting, and decision-making. The file system 404 handles unstructured or semi-structured data (like logs and reports). The database 208 manages structured data (like records and tables).
[0065] FIG. 5 is a signal flow diagram for processing data in the network 106, according to one or more embodiments of the present invention.
[0066] At step 502, the edge in the network 106 receives the request from the user.
[0067] At step 504, upon receiving the request from the user, the data is aggregated at the edge in the network 106. The data include at least one of, cell level data, subscriber level data, r4gstate data, maintenance zone data, and center-wise data.
[0068] At step 506, upon aggregating the data, the summarized record of the data is generated at the edge of the network 106.
[0069] At step 508, subsequently, the generated summarized record of the data is transmitted to the computation engine to perform at least one of, data computation and analysis. The data computation is performed using the customized computation layer in the computation engine. The computation is performed on the summarized record for one or more geographies at predefined time periods.
[0070] At step 510, the computed results of the summarized record are stored in the file system 404 and the database 208.
[0071] FIG. 6 is a flow diagram of a method 600 for data management in the network 106, according to one or more embodiments of the present invention. For the purpose of description, the method 600 is described with the embodiments as illustrated in FIG. 2 and should nowhere be construed as limiting the scope of the present disclosure.
[0072] At step 602, the method 600 includes the step of aggregating data at an edge in the network 106 based on a request received from a user by the aggregation unit 210. The data include at least one of, cell level data, subscriber level data, r4gstate data, maintenance zone data, and center-wise data.
[0073] At step 604, the method 600 includes the step of generating a summarized record of the data at the edge by the generation unit 212.
[0074] At step 606, the method 600 includes the step of transmitting the generated summarized record from the edge to a computation engine to perform at least one of, data computation and analysis by the transceiver 214. The data computation is performed using a customized computation layer in the computation engine. The computation is performed on the summarized record for one or more geographies at predefined time periods.
[0075] The present invention further discloses a non-transitory computer-readable medium having stored thereon computer-readable instructions. The computer-readable instructions are executed by the processor 202. The processor 202 is configured to aggregate data at the edge in the network 106 based on the request received from the user. The processor 202 is further configured to generate the summarized record of the data at the edge. The processor 202 is further configured to transmit the generated summarized record from the edge to the computation engine to perform at least one of, data computation and analysis.
[0076] A person of ordinary skill in the art will readily ascertain that the illustrated embodiments and steps in description and drawings (FIG.1-6) are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[0077] The present disclosure incorporates technical advancement of reduction in network congestion and prevents data loss in case of failure scenarios at centralized location. Further, the present disclosure overcomes the challenges of bandwidth requirement and latency issues.
[0078] The present invention offers multiple advantages over the prior art and the above listed are a few examples to emphasize on some of the advantageous features. The listed advantages are to be read in a non-limiting manner.

REFERENCE NUMERALS

[0079] Environment- 100
[0080] User Equipment (UE)- 102
[0081] Server- 104
[0082] Network- 106
[0083] System -108
[0084] Processor- 202
[0085] Memory- 204
[0086] User Interface- 206
[0087] Database- 208
[0088] Aggregation Unit- 210
[0089] Generation Unit- 212
[0090] Transceiver- 214
[0091] Computation Engine- 402
[0092] Filesystem- 404

,CLAIMS:CLAIMS:
We Claim:
1. A method (600) for data management in a network (106), the method (600) comprising the steps of:
aggregating, by one or more processors (202), data at an edge in the network (106) based on a request received from a user;
generating, by the one or more processors (202), a summarized record of the data at the edge; and
transmitting, by the one or more processors (202), the generated summarized record from the edge to a computation engine (402) to perform at least one of, data computation and analysis.

2. The method (600) as claimed in claim 1, wherein the data include at least one of, cell level data, subscriber level data, r4gstate data, maintenance zone data, and center-wise data.

3. The method (600) as claimed in claim 1, wherein the data computation is performed using a customized computation layer in the computation engine, and wherein the data computation is performed on the summarized record for one or more geographies at predefined time periods.

4. A system (108) for data management in a network (106), the system (108) comprises:
an aggregation unit (210) configured to aggregate, data at an edge in the network based on a request received from a user;
a generation unit (212) configured to generate, a summarized record of the data at the edge; and
a transceiver (214), configured to, transmit, the generated summarized record from the edge to a computation engine to perform at least one of, data computation and analysis.

5. The system (108) as claimed in claim 4, wherein the data include at least one of, cell level data, subscriber level data, r4gstate data, maintenance zone data, and center-wise data.

6. The system (108) claimed in claim 4, wherein the data computation is performed using a customized computation layer in the computation engine, and wherein the computation is performed on the summarized record for one or more geographies at predefined time periods.

7. A User Equipment (UE) (102), comprising:
one or more primary processors (302) communicatively coupled to one or more processors (202), the one or more primary processors (302) coupled with a memory (304), wherein said memory (304) stores instructions which when executed by the one or more primary processors (302) causes the UE (102) to:
transmit, a request from the user to the one or more processors (202) related to a specific requirement of data; and
wherein the one or more processors is configured to perform the steps as claimed in claim 1.