FORM 2
THE PATENTS ACT, 1970 (39 OF 1970) & THE PATENT RULES, 2003
COMPLETE SPECIFICATION
(See section 10 and rule 13)
“METHOD AND SYSTEM FOR REPROCESSING OF DATA FOR DATA
INTEGRITY”
We, Jio Platforms Limited, an Indian National, of Office - 101, Saffron, Nr. Centre Point, Panchwati 5 Rasta, Ambawadi, Ahmedabad - 380006, Gujarat, India.
The following specification particularly describes the invention and the manner in which it is to be performed.

METHOD AND SYSTEM FOR REPROCESSING OF DATA FOR DATA
INTEGRITY
FIELD OF THE DISCLOSURE
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[0001] The present disclosure generally relates to data processing systems. More particularly, the present disclosure relates to a method and system for reprocessing of data for data integrity.
10 BACKGROUND
[0002] The following description of the 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 15 present disclosure. However, it should be appreciated that this section is used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of the prior art.
[0003] Element management system (EMS) is a managing system which collects 20 and manages data from the network elements or nodes such as base stations of LTE, 5G or 6G network. The network elements or node or base stations send their performance statistics to the EMS. This data is stored in a database and used by various other entities for further analysis and taking actions for improving performance of the systems.
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[0004] Existing systems for managing and processing performance data in telecommunications networks face several challenges that can compromise data integrity and the accuracy of Key Performance Indicators (KPIs). One major issue is the connectivity problems between Northbound Interface (NBI) and Element 30 Management Systems (EMS), which can result in partial or incomplete data being transmitted to the EMS. Additionally, issues at the EMS end, such as system
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failures or processing errors, can further exacerbate the problem of data loss or corruption. Another significant challenge is the loss of connectivity between network nodes and the EMS. This can occur due to various network-related issues, leading to nodes being unable to send performance management (PM) data within 5 the stipulated timeline. As a result, the EMS may receive incomplete data, which affects the computation of KPIs and the overall reporting accuracy. Furthermore, the existing systems may lack robust mechanisms to automatically identify and address missing data from specific nodes. This can lead to gaps in the performance statistics, affecting the reliability and integrity of the KPIs computed from such 10 partial data. Consequently, the ability to monitor and ensure the performance and business continuity of all network nodes is compromised.
[0005] Thus, there exists an imperative need in the art to provide a method and a system for reprocessing of partial data received at different instances and 15 aggregating the results reflecting for data integrity, which the present disclosure aims to address.
OBJECTS OF THE INVENTION
20 [0006] Some of the objects of the present disclosure, which at least one embodiment disclosed herein satisfies are listed herein below.
[0007] It is an object of the present disclosure to provide a method and system for reprocessing of data for data integrity.
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[0008] It is another object of the present disclosure to provide a method and system for reprocessing of data for data integrity that can automatically identify missing base stations in a periodic interval and compute KPIs for those nodes in both time and space domains.
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[0009] It is yet another object of the present disclosure to provide a method and system for reprocessing of data for data integrity that can ensure the availability of performance statistics for 99% of the radiating nodes in a telecommunications network.
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[0010] It is yet another object of the present disclosure to provide a method and system for reprocessing of data for data integrity that can overcome issues related to connectivity between Network-to-Business Interface (NBI) interfaces and Element Management Systems (EMS), ensuring complete and accurate data 10 transmission.
[0011] It is yet another object of the present disclosure to provide a method and system for reprocessing of data for data integrity that can address system failures or processing errors at the EMS end, ensuring the integrity of the data and the 15 accuracy of the computed KPIs.
[0012] It is yet another object of the present disclosure to provide a method and system for reprocessing of data for data integrity that can handle network-related issues leading to loss of connectivity between network nodes and the EMS, ensuring 20 continuous monitoring and performance assessment of all network nodes.
[0013] It is yet another object of the present disclosure to provide a method and system for reprocessing of data for data integrity that can implement a sliding window mechanism for receiving and processing performance data, ensuring timely 25 and accurate computation of KPIs.
[0014] It is yet another object of the present disclosure to provide a method and system for reprocessing of data for data integrity that can store and manage performance data and KPIs in a distributed file system, enhancing data accessibility 30 and scalability.
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[0015] It is yet another object of the present disclosure to provide a method and system for reprocessing of data for data integrity that can compress performance data for efficient storage and processing, reducing the system's storage and computational requirements.
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SUMMARY OF THE DISCLOSURE
[0016] This section is provided to introduce certain aspects of the present disclosure in a simplified form that are further described below in the detailed description. 10 This summary is not intended to identify the key features or the scope of the claimed subject matter.
[0017] According to an aspect of the present disclosure, a method for reprocessing of data for data integrity is disclosed. The method includes receiving, at a receiving
15 unit, a first performance data for one more base stations from an Element management system (EMS) server for a first pre-defined time interval. Next, the method includes computing, by a determination unit, one or more first key performance indicators (KPIs) based on the first performance data received for the first pre-defined time interval. Next, the method includes receiving, at the receiving
20 unit, a second performance data for the one or more base stations from the Element management system (EMS) servers for a second pre-defined time interval. Next, the method includes computing, by the determination unit, one or more second KPIs based on the second performance data received for the second pre-defined time interval. Thereafter, the method includes performing, by a processing unit,
25 aggregation of the one or more second KPIs and the one or more first KPIs to generate one or more overall KPIs.
[0018] In an exemplary aspect of the present disclosure, the method further comprises storing, by a storage unit, the one or more first KPIs in a raw counter 30 aggregation table.
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[0019] In an exemplary aspect of the present disclosure, the method for reprocessing of the data facilitates in identification of at least one missing base station in a periodic interval.
5 [0020] In an exemplary aspect of the present disclosure, a sliding window mechanism is used to receive and process the second performance data over the second pre-defined time interval for computation of the one or more second KPIs.
[0021] In an exemplary aspect of the present disclosure, the sliding window 10 mechanism is implemented in a third pre-defined time interval.
[0022] In an exemplary aspect of the present disclosure, the method further comprises compressing, by the processing unit, the first performance data; storing, by a storage unit, the compressed first performance data on a distributed file system; 15 and reading, by a data recognition unit, the compressed first performance data of the first pre-defined period of time.
[0023] In an exemplary aspect of the present disclosure, the method further comprises storing, by a storage unit, the one or more second KPIs in a raw counter 20 aggregation table.
[0024] In an exemplary aspect of the present disclosure, the performance data for the second pre-defined time interval comprises performance data for the first pre-defined time interval.
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[0025] In an exemplary aspect of the present disclosure, the method further comprises storing, by a storage unit, the one or more overall KPIs in a raw counter aggregation table.
30 [0026] According to another aspect of the present disclosure, a system for reprocessing of data for data integrity is disclosed. The system comprising a
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receiving unit, configured to receive a first performance data for one more base stations from an Element management system (EMS) server for a first pre-defined time interval; a determination unit, configured to compute one or more first key performance indicators (KPIs) based on the first performance data received for the 5 first pre-defined time interval; the receiving unit, configured to receive a second performance data for the one or more base stations from the Element management system (EMS) servers for a second pre-defined time interval; the determination unit, configured to compute one or more second KPIs based on the second performance data received for the second pre-defined time interval; and a 10 processing unit, configured to perform aggregation of the one or more second KPIs and the one or more first KPIs to generate one or more overall KPIs.
[0027] Yet another aspect of the present disclosure may relate to a non-transitory computer-readable storage medium storing instructions for reprocessing of data for
15 data integrity system, the instructions include executable code which, when executed by one or more units of a system, causes: a receiving unit of the system to receive a first performance data for one more base stations from an Element management system (EMS) server for a first pre-defined time interval; a determination unit of the system to compute one or more first key performance
20 indicators (KPIs) based on the first performance data received for the first pre¬defined time interval; the receiving unit of the system to receive a second performance data for the one or more base stations from the Element management system (EMS) server for a second pre-defined time interval; the determination unit of the system to compute one or more second KPIs based on the second
25 performance data received for the second pre-defined time interval; and a processing unit of the system to perform aggregation of the one or more second KPIs and the one or more first KPIs to generate one or more overall KPIs.
BRIEF DESCRIPTION OF THE DRAWINGS
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[0028] 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, 5 emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Also, the embodiments shown in the figures are not to be construed as limiting the disclosure, but the possible variants of the method and system according to the disclosure are illustrated herein to highlight the advantages of the disclosure. It will be appreciated by those skilled in the art that disclosure of such 10 drawings includes disclosure of electrical components or circuitry commonly used to implement such components.
[0029] FIG. 1 illustrates an exemplary block diagram of an architecture of implementation of a system for reprocessing of data for data integrity with Element 15 Management System (EMS) server, in accordance with exemplary embodiments of the present disclosure.
[0030] FIG. 2 illustrates an exemplary block diagram of a system for reprocessing of data for data integrity, in accordance with exemplary embodiments of the present 20 disclosure.
[0031] FIG. 3 illustrates an exemplary sequence diagram indicating the process for reprocessing of data for data integrity, in accordance with exemplary embodiments of the present disclosure.
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[0032] FIG. 4 illustrates an exemplary method flow diagram indicating the process for reprocessing of data for data integrity, in accordance with exemplary embodiments of the present disclosure.
30 [0033] FIG. 5 illustrates an exemplary block diagram of a computing device upon which an embodiment of the present disclosure may be implemented.
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[0034] The foregoing shall be more apparent from the following more detailed description of the disclosure.
5 DETAILED DESCRIPTION
[0035] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that 10 embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter may each be used independently of one another or with any combination of other features. An individual feature may not address any of the problems discussed above or might address only some of the problems discussed above.
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[0036] 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. 20 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.
[0037] Specific details are given in the following description to provide a thorough 25 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, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail.
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[0038] 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 may be performed in parallel or 5 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.
[0039] The word “exemplary” and/or “demonstrative” is used herein to mean 10 serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein 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 15 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.
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[0040] As used herein, a “processing unit” or “processor” or “operating processor” includes one or more processors, wherein processor refers to any logic circuitry for processing instructions. A processor may be a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a plurality
25 of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits, Field Programmable Gate Array circuits, any other type of integrated circuits, etc. The processor may perform signal coding data processing, input/output processing, and/or any other functionality that enables the working of the system according to
30 the present disclosure. More specifically, the processor or processing unit is a hardware processor.
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[0041] As used herein, “a user equipment”, “a user device”, “a smart-user-device”, “a smart-device”, “an electronic device”, “a mobile device”, “a handheld device”, “a wireless communication device”, “a mobile communication device”, “a 5 communication device” may be any electrical, electronic and/or computing device or equipment, capable of implementing the features of the present disclosure. The user equipment/device may include, but is not limited to, a mobile phone, smart phone, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, wearable device or any other computing device which is capable 10 of implementing the features of the present disclosure. Also, the user device may contain at least one input means configured to receive an input from at least one of a transceiver unit, a processing unit, a storage unit, a detection unit and any other such unit(s) which are required to implement the features of the present disclosure.
15 [0042] As used herein, “storage unit” or “memory unit” refers to a machine or computer-readable medium including any mechanism for storing information in a form readable by a computer or similar machine. For example, a computer-readable medium includes read-only memory (“ROM”), random access memory (“RAM”), magnetic disk storage media, optical storage media, flash memory devices or other
20 types of machine-accessible storage media. The storage unit stores at least the data that may be required by one or more units of the system to perform their respective functions.
[0043] As discussed in the background section, Existing systems for managing and 25 processing performance data in telecommunications networks face several challenges that can compromise data integrity and the accuracy of Key Performance Indicators (KPIs). One major issue is the connectivity problems between Northbound Interface (NBI) and Element Management Systems (EMS), which can result in partial or incomplete data being transmitted to the EMS. Additionally, 30 issues at the EMS end, such as system failures or processing errors, can further exacerbate the problem of data loss or corruption. Another significant challenge is
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the loss of connectivity between network nodes and the EMS. This can occur due to various network-related issues, leading to nodes being unable to send performance management (PM) data within the stipulated timeline. As a result, the EMS may receive incomplete data, which affects the computation of KPIs and the 5 overall reporting accuracy. Furthermore, the existing systems may lack robust mechanisms to automatically identify and address missing data from specific nodes. This can lead to gaps in the performance statistics, affecting the reliability and integrity of the KPIs computed from such partial data. Consequently, the ability to monitor and ensure the performance and business continuity of all network nodes 10 is compromised.
[0044] To overcome these and other inherent problems in the art, the present disclosure proposes a solution of a reprocessing mechanism for data integrity that aims to ensure the completeness and accuracy of performance data and Key
15 Performance Indicators (KPIs) in telecommunications networks. This is achieved through several innovative approaches outlined in the claims of the invention. Firstly, the invention introduces a method and system for receiving and processing performance data from base stations in predefined time intervals. This approach allows for the continuous monitoring of network performance and the computation
20 of KPIs in real-time, addressing the issue of data loss due to connectivity problems between network nodes and the Element Management System (EMS). Secondly, the disclosure proposes the use of a sliding window mechanism for processing the performance data. This method enables the system to handle variations in data availability and ensures that KPIs are computed based on the most recent and
25 relevant data, thereby improving the accuracy of the performance metrics. Thirdly, the invention provides a means for identifying missing base stations in periodic intervals. By automatically detecting gaps in the data, the system can initiate reprocessing for the affected nodes, ensuring that the performance statistics and KPIs remain comprehensive and reliable. Furthermore, the invention includes
30 mechanisms for compressing and storing performance data on a distributed file system, which enhances the efficiency of data management and supports scalability.
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This feature addresses the problem of system failures or processing errors at the EMS end by providing a robust and resilient data storage solution.
[0045] It would be appreciated by the person skilled in the art that the present 5 disclosure addresses the problems in the prior art by providing a comprehensive solution for reprocessing data for data integrity. This solution ensures the continuous availability of accurate and reliable performance statistics and KPIs, thereby supporting effective network management and business continuity in telecommunications networks.
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[0046] Hereinafter, exemplary embodiments of the present disclosure will be described with reference to the accompanying drawings.
[0047] Referring to FIG. 1, an exemplary block diagram of an architecture [100] 15 for reprocessing of data for data integrity with Element Management System (EMS) server is shown, in accordance with the exemplary embodiments of the present disclosure. As shown in FIG. 1, the architecture [100] comprises at least one EMS server [104] and at least one edge node [102]. Also, in FIG. 1 only a few units are shown, however, the architecture [100] may comprise multiple such units or the 20 architecture [100] may comprise any such numbers of said units, as required to implement the features of the present disclosure.
[0048] The EMS server [104] is a managing system which collects and manages data from one or more network elements or nodes such as base stations. The 25 network elements or node or base stations send their performance data/statistics/counters to the EMS server [104].
[0049] The Edge Node [102] fetches the performance data from the EMS server
[104] and processes to determine or identify cell performance data and missing cell
30 performance data for further analysis and taking actions for improving performance
of the network. After every pre-configured time period (for example 15 minutes)
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raw data (such as performance data) is received from EMS server [104]. The receipt of the raw data (such as performance data) facilitates in identifying missing cell data. Raw data may include but not limited to at least raw performance statistics from the EMS server [104]. Due to some issue such as connectivity failure with the 5 EMS server, backhaul issues or some other reasons, it may be possible performance data of cells or base stations or network nodes could not be captured. The Edge node [102] computes the KPIs data or counter based on performance data and perform reprocessing for identifying the missing cells periodically and compute the KPIs for those nodes in both time and space domain. In an exemplary aspect, the 10 edge node [102] performs the reprocessing periodically and automatically based on for the pre-configured user/network operator’s time interval.
[0050] Referring to FIG. 2, an exemplary block diagram of a system [200] for reprocessing of data for data integrity is shown, in accordance with the exemplary
15 embodiments of the present disclosure. As shown in FIG. 2, the system [200] comprises a processing unit [202], a receiving unit [204], a determination unit [206], a data recognition unit [208] and a storage unit [210]. Also, all of the components/ units of the system [200] are assumed to be connected to each other unless otherwise indicated below, however, due to clarity purpose interconnections
20 are not shown. In an exemplary aspect, the components/units may be present inside the edge node [102]. Also, in FIG. 2 only a few units are shown, however, the system [200] may comprise multiple such units or the system [200] may comprise any such numbers of said units, as required to implement the features of the present disclosure. In an implementation, the system [200] may reside in a server. In another
25 implementation, the system [200] may reside partly in the server.
[0051] The receiving unit [204] is configured to receive first performance data for one or more base stations from an Element Management System (EMS) server [104] for a first predefined time interval. The predefined time interval specifies the 30 regularity and the duration for which this data is captured, ensuring a systematic and consistent approach to data collection. The performance data collected can
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include a set of metrics for evaluating the operation of the base stations, such as but not limited only to signal quality, traffic load, and error rates. In an exemplary aspect, the first pre-defined period of time may be such as, but not limited to, in hours, minutes, days and the like. In an exemplary aspect, the first pre-defined 5 interval/period of time may be such as, but no limited to, 4 hours.
[0052] The determination unit [206] is communicatively coupled to the receiving unit [204]. The determination unit [206] is configured to compute one or more first key performance indicators (KPIs) based on the first performance data received for 10 the first predefined time interval. Examples of the one or more KPIs includes, but not limited only to call drop, congestion, data speed utilization, traffic throughput, and utilization. Upon receiving the initial batch of the performance data from the receiving unit [204], the determination unit [206] begins to compute one or more first key performance indicators (KPIs).
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[0053] The receiving unit [204] is further configured to receive a second performance data for the one or more base stations from the EMS server [104] for a second predefined time interval. After the first performance data and the one or more first KPIs are collected and computed for the first interval. Thereafter the
20 receiving unit [204] is configured to receive subsequently the second performance data for the second interval. The collection of performance data (such as the first performance data and the second performance data) in separate predefined time intervals (the first predefined time interval and the second time interval) allows for dynamic tracking of network performance and responsiveness to changes or
25 anomalies that may occur in the network's operation.
[0054] The second performance data is received and processed using a sliding
window mechanism over the second predefined time interval for the computation
of one or more second key performance indicators (KPIs). The sliding window
30 mechanism is implemented in a third predefined time interval (such as four hours).
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The system not only updates the data set for analysis dynamically within the second time interval but also applies this mechanism over an additional, third time interval.
[0055] For example, the system might collect performance data every minute and 5 initially compute KPIs based on the first five minutes of data (the first predefined time interval). As the system continues to collect data, it uses a sliding window mechanism to compute the second set of KPIs based on the most recent 15 minutes of data (the second predefined time interval). This means that every minute, the oldest minute of data is dropped from the analysis, and the newest minute is added,
10 ensuring that the KPIs always reflect the latest 15-minute period. Furthermore, the sliding window mechanism is implemented over a larger 30-minute period (the third predefined time interval). This allows the system to compare the current 15-minute performance with the previous 15-minute period within this 30-minute window. By continuously updating the data set and KPIs, the system provides a
15 real-time and comprehensive view of the network's performance, enabling timely identification and resolution of any issues that may arise.
[0056] The sliding window mechanism is a technique used in data processing where a "window" of fixed size moves over the data set in steps. At each step, the
20 mechanism processes only the data within the window, which represents a subset of the entire data set. This approach is particularly useful for analysing streaming data or large data sets in real-time or near-real-time. The sliding window mechanism allows the system to continuously update the set of data being analysed. For example, if the window size is set to cover a four-hour interval, the system
25 would process the latest (such as for a period of four hours) data at each step. As time progresses, the window "slides" forward, and the system discards the oldest data and includes new data, ensuring that the analysis always covers the most recent four -hours period.
30 [0057] The second performance data is received and processed using a sliding window mechanism over the second predefined time interval for the computation
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of one or more second key performance indicators (KPIs). This means that the system uses a method where the data set for analysis is continuously updated, allowing for the most recent and relevant information to be considered when calculating the second set of KPIs. The sliding window mechanism ensures that the 5 performance evaluation remains current and accurate, reflecting the latest changes in the network's performance.
[0058] The determination unit [206] is further configured to compute one or more second KPIs based on the second performance data received for the second
10 predefined time interval. After the receiving unit [204] has received the second set of performance data, the determination unit [206] analyses the new data to extract the one or more second KPIs. In an embodiment, the system [200] may compare the one or more first KPIs and the one or more second KPIs. By comparing the one or more first KPIs and the one or more second KPIs, the system can identify patterns
15 or anomalies that could signal issues requiring attention. Moreover, if discrepancies or gaps are identified, this triggers the reprocessing protocol to ensure that reliable data feeds into the overall performance analysis, enabling network operators to make informed decisions.
20 [0059] The processing unit [202] communicatively coupled to the determination unit [206]. The processing unit [202] is configured to perform aggregation of the one or more second KPIs and the one or more first KPIs to generate one or more overall KPIs. The one or more overall KPIs are derived from both the one or more second KPIs and the one or more first KPIs, enabling the system [200] to track
25 changes, identify trends, and make more informed decisions about the network's management and optimization thereby ensuring data integrity and supporting the reliable operation of the telecommunications network.
[0060] The first performance data and the second performance data comprise, but
30 not limited to, at least one of call drop rate, mute call rate, handover success rate
and IP/cell throughput and the like. The one or more first KPIs and the one or more
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second KPIs comprises such as, but not limited to, at least one of a latency, a throughput, a packet loss rate, and a signal strength of one or more base stations/network nodes or pre-configured/ pre-monitored cell’s performance data parameters.
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[0061] In an embodiment, the system [200] for reprocessing of the data facilitates in identification of at least one missing base station in a periodic interval. For example, the system [200] has the capability to detect when data from a base station is not received as expected during a specific time period. By identifying missing 10 base stations, the system [200] can address gaps in the data collection process, ensuring that the performance statistics remain complete and accurate.
[0062] The processing unit [202] is further configured to compress the first performance data. The compression reduces the amount of storage space required
15 and makes the data easier to manage and transmit. The storage unit [210] is configured to store the compressed first performance data on a distributed file system such that the data is securely saved and can be accessed from multiple locations if needed. Additionally, the storage unit [210] is further configured to store the one or more second KPIs in a raw counter aggregation table. In an
20 exemplary aspect, the raw counter aggregation table includes raw performance statistics. The raw performance statistics may include such as but not limited to, KPI metrics, network traffic data etc. The one or more second KPIs are computed based on the second performance data and storing them in a raw counter aggregation table allows for efficient organization and retrieval. The data
25 recognition unit [208] is further configured to read the compressed first performance data of the first predefined period of time. This unit retrieves the data for analysis and further processing, ensuring that the system can utilize the compressed data effectively.
30 [0063] Furthermore, the storage unit [210] is also configured to store the first set of KPIs in a raw counter aggregation table. Similar to the second KPIs, storing the first
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KPIs in this manner allows for systematic recording and easy access to the performance indicators, facilitating ongoing monitoring and analysis of the network's performance. The storage unit [210] is further configured to store the one or more overall key performance indicators (KPIs) in a raw counter aggregation 5 table. This means that in addition to storing the first and second sets of KPIs, the storage unit is also equipped to store the aggregated KPIs that provide a comprehensive view of the network's performance. The use of a raw counter aggregation table allows for the systematic organization and easy retrieval of these overall KPIs, facilitating efficient analysis and decision-making based on the 10 overall performance of the network.
[0064] In an embodiment, the performance data for the second pre-defined time interval comprises performance data for the first pre-defined time interval. For example, a telecommunications network where performance data is collected every
15 minute. The system is set up to analyse performance data over two predefined time intervals: the first interval covers 10 minutes, and the second interval covers 20 minutes. For the first predefined time interval, the system collects and analyses performance data from 1:00 PM to 1:10 PM. This data is used to compute the first set of key performance indicators (KPIs), providing insights into the network's
20 performance during this 10-minute period. For the second predefined time interval, the system collects and analyses performance data from 1:00 PM to 1:20 PM. This 20-minute interval includes the data from the first interval (1:00 PM to 1:10 PM) as well as additional data from 1:10 PM to 1:20 PM. The system then computes the second set of KPIs based on this extended dataset, offering a broader view of the
25 network's performance over the 20-minute period.
[0065] Referring to FIG. 3 an exemplary sequence diagram [300], for reprocessing of data for data integrity, in accordance with exemplary embodiments of the present invention is shown. In an implementation the sequence is performed by the system 30 [200].
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[0066] At S1, the system [200] executes one or more shell script(s), instruction sets or commands every pre-defined time interval (for example, not limited to, 15 mins) to pull the performance data from one or more EMS server(s) [104]. The System [200] may have one or more processing unit [202], which may execute shell 5 script(s)/instructions/commands for fetching performance data of network elements/ nodes such as base stations from EMS server [104].
[0067] At S2, fetched performance data is compressed by the processing unit [202] and then stored in a distributed file system, referred to as ‘storage unit 1’ [302A]. 10 The performance data is grouped together depending on the nature of the raw counters and then each of the file is compressed and received from source (such as EMS server [104]). The ‘storage unit 1’ [302A] may be for example include but not limited to Hadoop Distributed file system (HDFS) database. The ‘storage unit 1’ [302A] such as HDFS may act as a distributed file system.
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[0068] At S3, the data analysis Unit [302B] reads the compressed files in predefined time interval (for example, not limited to, quarterly). In an exemplary aspect, the data analysis unit [302B] may be such as, but not limited to, a Spark engine or the like analysis/processing engine.
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[0069] At S4, the data analysis unit [302B] may store or update computed counters or KPIs data based on the performance data of network elements/ base stations into ‘Storage unit 2’ [302C]. The ‘Storage unit 2’ [302C] may be for example include but not limited only to, HBase database or other database/s. The ‘Storage unit 2’ 25 [302C] such as, HBase database, may store at least a fact table, KPIs, computed counters.
[0070] In an exemplary aspect, ‘Storage unit 2’ [302C] such as HBase and ‘storage unit 1’ [302A] such as HDFS database may store similar data. The network 30 administrator may configure to store KPI or counter data in ‘Storage unit 2’ [302C] HBase and ‘storage unit 1’ [302A] based on usage and network traffic.
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[0071] At S5, the data analysis unit [302B] may read KPI configurations from ‘Storage Unit 3’ [302D]. The data analysis unit [302B] hourly processes the computing job to compute counters including the late or missing data (such as 5 missed at first time interval and received at second time interval) and writes to ‘Storage Unit 2’ [302C] tables. The ‘Storage unit 3’ [302D] may be for example include but not limited only to, Oracle database or other database The ‘storage unit 3’ may store at least a dimension table. The dimension table includes definition of KPIs, counters, KPI configurations etc.
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[0072] Referring to FIG. 4 an exemplary method flow diagram [400], for reprocessing of data for data integrity, in accordance with exemplary embodiments of the present invention is shown. In an implementation the method [400] is performed by the system [200]. As shown in FIG. 4, the method [400] starts at step 15 [402].
[0073] At step [404], the method [400] as disclosed by the present disclosure comprises receiving, at a receiving unit [204], a first performance data for one more base station from an Element management system (EMS) server [104] for a first 20 pre-defined time interval. The predefined time interval specifies the regularity and the duration for which this data is captured, ensuring a systematic and consistent approach to data collection. The performance data collected can include a set of metrics for evaluating the operation of the base stations, such as but not limited only to signal quality, traffic load, and error rates. In an exemplary aspect, the first pre-25 defined period of time may be such as, but not limited to, in hours, minutes, days and the like. In an exemplary aspect, the first pre-defined interval/period of time may be such as, but no limited to, 4 hours.
[0074] Next, at step [406], the method [400] as disclosed by the present disclosure
30 comprises computing, by a determination unit [206], one or more first key
performance indicators (KPIs) based on the first performance data received for the
21

first pre-defined time interval. Upon receiving the initial batch of the performance data from the receiving unit [204], the determination unit [206] begins to compute one or more first key performance indicators (KPIs).
5 [0075] Next, at step [408], the method [400] as disclosed by the present disclosure comprises receiving, at the receiving unit [204], a second performance data for the one or more base stations from the Element management system (EMS) server [104] for a second pre-defined time interval. After the first performance data and the one or more first KPIs are collected and computed for the first interval.
10 Thereafter the receiving unit [204] is configured to receive subsequently the second performance data for the second interval. The collection of performance data (such as the first performance data and the second performance data) in separate predefined time intervals (the first predefined time interval and the second time interval) allows for dynamic tracking of network performance and responsiveness
15 to changes or anomalies that may occur in the network's operation. The second performance data is received and processed using a sliding window mechanism over the second predefined time interval for the computation of one or more second key performance indicators (KPIs). The sliding window mechanism is implemented in a third predefined time interval. The system not only updates the data set for
20 analysis dynamically within the second time interval but also applies this mechanism over an additional, third time interval.
[0076] Next, at step [410], the method [400] as disclosed by the present disclosure comprises computing, by the determination unit [206], one or more second KPIs 25 based on the second performance data received for the second pre-defined time interval. Upon receiving the initial batch of the performance data from the receiving unit [204], the determination unit [206] begins to compute one or more first key performance indicators (KPIs).
30 [0077] Next, at step [412], the method [400] as disclosed by the present disclosure comprises performing, by a processing unit [202], aggregation of the one or more
22

second KPIs and the one or more first KPIs to generate one or more overall KPIs. The one or more overall KPIs are derived from both the one or more second KPIs and the one or more first KPIs, enabling the system [200] to track changes, identify trends, and make more informed decisions about the network's management and 5 optimization thereby ensuring data integrity and supporting the reliable operation of the telecommunications network.
[0078] The processing unit [202] is further configured to compress the first performance data. The compression reduces the amount of storage space required
10 and makes the data easier to manage and transmit. The storage unit [210] is configured to store the compressed first performance data on a distributed file system such that the data is securely saved and can be accessed from multiple locations if needed. Additionally, the storage unit [210] is further configured to store the one or more second KPIs in a raw counter aggregation table. The one or
15 more second KPIs are computed based on the second performance data and storing them in a raw counter aggregation table allows for efficient organization and retrieval. The data recognition unit [208] is further configured to read the compressed first performance data of the first predefined period of time. This unit retrieves the data for analysis and further processing, ensuring that the system can
20 utilize the compressed data effectively.
[0079] Thereafter, the method [400] terminates at step [414].
[0080] It should be noted that the terms "first", "second", and the like, herein do not 25 denote any order, ranking, quantity, or importance, but rather are used to distinguish one element from another.
[0081] Further, the terms "a" and "an" herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
30
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[0082] In an exemplary embodiment, the present disclosure provides reprocessing mechanism automatically identifies missing cells/nodes/network elements data periodically due to loss network connectivity or any reasons in the intended time frame, computes the KPIs for those cells/nodes in both time and space domain. In 5 an example, the present disclosure may implement reprocessing mechanism in sliding window fashion, with a time lapse of 4 hrs. Every hour reprocessing mechanism job may initiate and process for the last 4 hours missing cells/nodes/network elements data ensuring that hierarchical aggregation is done including the missing cells/nodes/network elements.
10
[0083] FIG. 5 illustrates an exemplary block diagram of a computing device [500] (also referred to herein as a computer system [500]) upon which an embodiment of the present disclosure may be implemented. In an implementation, the computing device implements the method for reprocessing of data for data integrity using the 15 system [200]. In another implementation, the computing device [500] itself implements the method for reprocessing of data for data integrity by using one or more units configured within the computing device, wherein said one or more units are capable of implementing the features as disclosed in the present disclosure.
20 [0084] The computing device [500] may include a bus [502] or other communication mechanism for communicating information, and a processor [504] coupled with the bus [502] for processing information. The processor [504] may be, for example, a general-purpose microprocessor. The computing device [500] may also include a main memory [506], such as a random-access memory (RAM),
25 or other dynamic storage device, coupled to the bus [502] for storing information and instructions to be executed by the processor [504]. The main memory [506] also may be used for storing temporary variables or other intermediate information during execution of the instructions to be executed by the processor [504]. Such instructions, when stored in non-transitory storage media accessible to the processor
30 [504], render the computing device [500] into a special-purpose machine that is customized to perform the operations specified in the instructions. The computing
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device [500] further includes a read only memory (ROM) [508] or other static storage device coupled to the bus [502] for storing static information and instructions for the processor [504].
5 [0085] A storage device [510], such as a magnetic disk, optical disk, or solid-state drive is provided and coupled to the bus [502] for storing information and instructions. The computing device [500] may be coupled via the bus [502] to a display [512], such as a cathode ray tube (CRT), for displaying information to a computer user. An input device [514], including alphanumeric and other keys, may
10 be coupled to the bus [502] for communicating information and command selections to the processor [504]. Another type of user input device may be a cursor controller [516], such as a mouse, a trackball, or cursor direction keys, for communicating direction information and command selections to the processor [504], and for controlling cursor movement on the display [512]. This input device
15 typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allow the device to specify positions in a plane.
[0086] The computing device [500] may implement the techniques described herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware
20 and/or program logic which in combination with the computing device [500] causes or programs the computing device [500] to be a special-purpose machine. According to one embodiment, the techniques herein are performed by the computing device [500] in response to the processor [504] executing one or more sequences of one or more instructions contained in the main memory [506]. Such
25 instructions may be read into the main memory [506] from another storage medium, such as the storage device [510]. Execution of the sequences of instructions contained in the main memory [506] causes the processor [504] to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions.
30
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[0087] The computing device [500] also may include a communication interface [518] coupled to the bus [502]. The communication interface [518] provides a two-way data communication coupling to a network link [520] that is connected to a local network [522]. For example, the communication interface [518] may be an 5 integrated services digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, the communication interface [518] may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be implemented. In any such 10 implementation, the communication interface [518] sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.
[0088] The computing device [500] can send messages and receive data, including 15 program code, through the network(s), the network link [520] and the communication interface [518]. In the Internet example, a server [530] might transmit a requested code for an application program through the Internet [528], the Internet Service Provider (ISP) [526], the host [524] the local network [522] and the communication interface [518]. The received code may be executed by the 20 processor [504] as it is received, and/or stored in the storage device [510], or other non-volatile storage for later execution.
[0089] The computing device [500] encompasses a wide range of electronic devices capable of processing data and performing computations. Examples of computing
25 device [500] include, but are not limited only to, personal computers, laptops, tablets, smartphones, servers, and embedded systems. The devices may operate independently or as part of a network and can perform a variety of tasks such as data storage, retrieval, and analysis. Additionally, computing device [500] may include peripheral devices, such as monitors, keyboards, and printers, as well as
30 integrated components within larger electronic systems, showcasing their versatility in various technological applications.
26

[0090] The present disclosure further discloses a non-transitory computer-readable storage medium storing instructions for reprocessing of data for data integrity system, the instructions include executable code which, when executed by one or 5 more units of a system, causes: a receiving unit [204] of the system to receive a first performance data for one more base stations from an Element management system (EMS) server [104] for a first pre-defined time interval; a determination unit [206] of the system to compute one or more first key performance indicators (KPIs) based on the first performance data received for the first pre-defined time interval; the
10 receiving unit [204] of the system to receive a second performance data for the one or more base stations from the Element management system (EMS) server [104] for a second pre-defined time interval; the determination unit [206] of the system to compute one or more second KPIs based on the second performance data received for the second pre-defined time interval; and a processing unit [202] of the
15 system to perform aggregation of the one or more second KPIs and the one or more first KPIs to generate one or more overall KPIs.
[0091] To overcome inherent problems in the art, the present disclosure proposes a solution of a reprocessing mechanism for data integrity that aims to ensure the
20 completeness and accuracy of performance data and Key Performance Indicators (KPIs) in telecommunications networks. Firstly, the disclosure introduces a technique that involves receiving and processing performance data from base stations in predefined time intervals. The approach allows for the continuous monitoring of network performance and the computation of KPIs in real-time,
25 addressing the issue of data loss due to connectivity problems between network nodes and the Element Management System (EMS). Secondly, the disclosure proposes the use of a sliding window mechanism for processing the performance data. The technique enables the system to handle variations in data availability and ensures that KPIs are computed based on the most recent and relevant data, thereby
30 improving the accuracy of the performance metrics. Thirdly, the invention provides a means for identifying missing base stations in periodic intervals. By automatically
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detecting gaps in the data, the system can initiate reprocessing for the affected nodes, ensuring that the performance statistics and KPIs remain comprehensive and reliable. Furthermore, the invention includes mechanisms for compressing and storing performance data on a distributed file system, which enhances the efficiency 5 of data management and supports scalability. This feature addresses the problem of system failures or processing errors at the EMS end by providing a robust and resilient data storage solution.
[0092] Further, in accordance with the present disclosure, it is to be acknowledged 10 that the functionality described for the various the components/units can be implemented interchangeably. While specific embodiments may disclose a particular functionality of these units for clarity, it is recognized that various configurations and combinations thereof are within the scope of the disclosure. The functionality of specific units as disclosed in the disclosure should not be construed 15 as limiting the scope of the present disclosure. Consequently, alternative arrangements and substitutions of units, provided they achieve the intended functionality described herein, are considered to be encompassed within the scope of the present disclosure.
20 [0093] While considerable emphasis has been placed herein on the disclosed embodiments, it will be appreciated that many embodiments can be made and that many changes can be made to the embodiments without departing from the principles of the present disclosure. These and other changes in the embodiments of the present disclosure will be apparent to those skilled in the art, whereby it is to
25 be understood that the foregoing descriptive matter to be implemented is illustrative and non-limiting.
28

We Claim:
1. A method for reprocessing of data for data integrity, the method comprising:
receiving, at a receiving unit [204], a first performance data for one
5 more base stations from an Element management system (EMS) server [104]
for a first pre-defined time interval;
computing, by a determination unit [206], one or more first key
performance indicators (KPIs) based on the first performance data received
for the first pre-defined time interval;
10 receiving, at the receiving unit [204], a second performance data for the
one or more base stations from the Element management system (EMS) server [104] for a second pre-defined time interval;
computing, by the determination unit [206], one or more second KPIs
based on the second performance data received for the second pre-defined
15 time interval; and
performing, by a processing unit [202], aggregation of the one or more second KPIs and the one or more first KPIs to generate one or more overall KPIs.
20 2. The method as claimed in claim 1, the method further comprising storing, by a storage unit [210], the one or more first KPIs in a raw counter aggregation table.
3. The method as claimed in claim 1, wherein the method for reprocessing of
25 the data facilitates in identification of at least one missing base station in a
periodic interval.
4. The method as claimed in claim 1, wherein a sliding window mechanism is
used to receive and process the second performance data over the second pre-
30 defined time interval for computation of the one or more second KPIs.

5. The method as claimed in claim 4, wherein the sliding window mechanism is
implemented in a third pre-defined time interval.
6. The method as claimed in claim 1, wherein the method further comprising:
compressing, by the processing unit [202], the first performance data;
storing, by a storage unit [210], the compressed first performance data
on a distributed file system; and
reading, by a data recognition unit [208], the compressed first performance data of the first pre-defined time interval.
7. The method as claimed in claim 6, the method further comprising storing, by
the storage unit [210], the one or more second KPIs in a raw counter
aggregation table.
15 8. The method as claimed in claim 1, wherein the performance data for the second pre-defined time interval comprises performance data for the first pre-defined time interval.
9. The method as claimed in claim 6, the method further comprising storing, by
20 the storage unit [210], the one or more overall KPIs in a raw counter
aggregation table.
10. A system for reprocessing of data for data integrity, the system comprising:
a receiving unit [204], configured to receive a first performance data for
25 one more base stations from an Element management system (EMS) server
[104] for a first pre-defined time interval;
a determination unit [206], configured to compute one or more first key performance indicators (KPIs) based on the first performance data received for the first pre-defined time interval;

the receiving unit [204], configured to receive a second performance data for the one or more base stations from the Element management system (EMS) server [104] for a second pre-defined time interval;
the determination unit [206], configured to compute one or more second KPIs based on the second performance data received for the second pre¬defined time interval; and
a processing unit [202], configured to perform aggregation of the one or more second KPIs and the one or more first KPIs to generate one or more overall KPIs.
11. The system as claimed in claim 10, further comprises a storage unit [210] configured to store the one or more first KPIs in a raw counter aggregation table.
15 12. The system as claimed in claim 10, wherein the system for reprocessing of the data facilitates in identification of at least one missing base station in a periodic interval.
13. The system as claimed in claim 10, wherein the second performance data is
20 received and processed using a sliding window mechanism over the second
pre-defined time interval for computation of the one or more second KPIs.
14. The system as claimed in claim 13, wherein the sliding window mechanism
is implemented in a third pre-defined time interval.
25
15. The system as claimed in claim 10, further comprises:
the processing unit [202] to compress the first performance data;
a storage unit [210], configured to store the compressed first
performance data on a distributed file system; and
30 a data recognition unit [208], configured to read the compressed first
performance data of the first pre-defined time interval.

16. The system as claimed in claim 15, wherein the storage unit [210] is further
configured to store the one or more second KPIs in a raw counter aggregation
table.
5
17. The system as claimed in claim 10, wherein the performance data for the
second pre-defined time interval comprises performance data for the first pre¬
defined time interval.
18. The system as claimed in claim 15, wherein the storage unit [210] is further configured to store the one or more overall KPIs in a raw counter aggregation table.