System and method for customer data extraction in network

Abstract

A system and a method for customer data extraction in a network (106) is disclosed A network function (e.g., charging function CHF or provisioning gateway (PGW)) (302) receives data from one or more network elements. The CHF / PGW (302) stores the received data in a database (210) or a subscriber profile repository (SPR) (304). The system (108) extracts one or more network parameters (e.g., SUPIs, MAC ID lists) from the database (210) or the SPR (304) and generates a MAC ID list. The customer data corresponding to each MAC ID of the MAC ID lists are fetched. A main spreadsheet is generated to store the fetched customer data. The system (108) creates a plurality of sub-spreadsheets from the main spreadsheet based on one or more predefined conditions or requirements of network operators, service providers, network engineers, technicians, customer support teams, researchers, and security professionals.

Description

SYSTEM AND METHOD FOR CUSTOMER DATA EXTRACTION IN NETWORKRESERVATION OF RIGHTS

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

[0002] The present disclosure relates generally to the field of data extraction. More particularly, the present disclosure relates to systems and methods for customer data extraction in a network.BACKGROUND

[0003] The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

[0004] In communication systems, a large database refers to a collection of vast amounts of data that is stored, managed, and used for processing, querying, and analysis. This data includes customer information, communication logs, network performance data, and other relevant data for communication services. The large database allows for the central storage of critical data, making it easier to access, manage, and analyze communication information from one location.

[0005] With more data available, users such as network operators, engineers, technicians, customer support teams, researchers, and security professionals can make better-informed decisions based on customer usage patterns, service performance, or trends in network. Large databases help analyze customer behavior, enabling telecom companies to offer tailored services, promotions, and customized plans. Additionally, as communication services expand, large databases can be scaled to accommodate the growing volume of data, which is crucial for handling increasing customer bases and network usage. Network operators can use large databases to track performance, detect faults, and manage resources efficiently, ensuring a better quality of service (QoS) for users.

[0006] However, manually extracting customer data from large databases involves searching vast amounts of information, which can be time-consuming and prone to errors. This issue is compounded when data is spread across multiple tables or when complex queries are required, making the process even more challenging and inefficient. Managing large databases also requires specialized knowledge and resources. Regular updates, backups, and ensuring data security are essential but can be difficult to maintain. Further, querying and processing large amounts of data can lead to slower performance, especially if the database is not optimized, and complex queries can be resource-intensive, affecting response times.

[0007] Additionally, storing large amounts of customer data increases the risk of data breaches and unauthorized access, leading to privacy concerns and potential legal issues. Although large databases contain a lot of information, it can be difficult for the users to find useful insights without the proper tools or methods.OBJECTIVES

[0008] Some of the objectives of the present disclosure, which at least one embodiment herein satisfies, are as follows:

[0009] An objective of the present disclosure is to provide a system and a method for extracting customer data in a network.

[0010] Another objective of the present disclosure is to provide a database dump tool for fetching customer information from a database (repositoryj / subscriber profile repository (SPR).

[0011] Yet another objective of the present disclosure is to extract subscription permanent identifiers (SUPIs) and media access control (MAC) identifier (ID) lists from the database.

[0012] Yet another objective of the present disclosure is to fetch customer information for each MACID in the MACID lists from the database and save it in a main spreadsheet.

[0013] Yet another objective of the present disclosure is to automatically create a plurality of sub-spreadsheets based on requirements from the main spreadsheet.

[0014] Other objectives and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.SUMMARY

[0015] In an exemplary embodiment, a method for customer data extraction in a network is disclosed. The method comprises extracting, by an extraction unit, one or more network parameters from customer data present in a database or a subscriber profile repository (SPR). The one or more network parameters comprise subscription permanent identifiers (SUPIs) and media access control (MAC) identifiers (IDs). The method comprises generating, by a processing unit, a MAC ID list based on theSUPIs. The method comprises iteratively fetching, by the extraction unit, customer data corresponding to each MAC ID of the MAC ID list. The method comprises generating, by the processing unit, a main spreadsheet based on the fetched customer data.

[0016] In some embodiments, the method comprises creating, by the processing unit, a plurality of sub-spreadsheets from the main spreadsheet based on one or more conditions.

[0017] In some embodiments, the one or more conditions comprise data analysis, data verification, backup and disaster recovery, performance analysis, data retrieval, operation management, network management, service provisioning, mobility management, security management, and billing and charging management.

[0018] In some embodiments, the customer data comprises subscriber information, service and billing information, network usage and session data, authentication and security data, and location-related information.

[0019] In another exemplary embodiment, a system for customer data extraction in a network is disclosed. The system comprises an extraction unit configured to extract one or more network parameters from customer data present in a database or a subscriber profile repository (SPR). The one or more network parameters comprise subscription permanent identifiers (SUPIs) and media access control (MAC) identifiers (IDs). A processing unit is configured to generate a MAC ID list based on the SUPIs. The extraction unit is configured to iteratively fetch customer data corresponding to each MAC ID of the MAC ID list. The processing unit is configured to generate a main spreadsheet based on the fetched customer data.

[0020] In yet another exemplary embodiment, a computer program product comprising a non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to execute a method for customer data extraction in a network is disclosed. The method comprises extracting, by an extraction unit, one or more network parameters from customer data present in a database or a subscriber profile repository (SPR). The one or more network parameters comprise subscription permanent identifiers (SUPIs) and media access control (MAC) identifiers (IDs). The method comprises generating, by a processing unit, a MAC ID list based on the SUPIs. The method comprises iteratively fetching, by the extraction unit, customer data corresponding to each MAC ID of the MAC ID list. The method comprises generating, by the processing unit, a main spreadsheet based on the fetched customer data.BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWING

[0021] 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 toscale, 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.

[0022] FIG. 1 illustrates an exemplary network architecture for customer data extraction in a network, in accordance with an embodiment of the present disclosure.

[0023] FIG. 2 illustrates an exemplary block diagram of a system for customer data extraction in the network, in accordance with an embodiment of the present disclosure.

[0024] FIG. 3 illustrates an exemplary system architecture for customer data extraction in the network, in accordance with an embodiment of the present disclosure.

[0025] FIG. 4 illustrates an exemplary flow diagram for customer data extraction in the network, in accordance with an embodiment of the present disclosure.

[0026] FIG. 5 illustrates an exemplary flow diagram of a method for customer data extraction in the network, in accordance with an embodiment of the present disclosure.

[0027] FIG. 6 illustrates an exemplary block diagram of a computer system in which or with which embodiments of the present disclosure may be implemented.

[0028] The foregoing shall be more apparent from the following more detailed description of the disclosure.LIST OF REFERENCE NUMERALS100 Network Architecture102 Plurality of Users104 Plurality of User Equipments (UEs)106 Network108 System200 Block Diagram202 Processor(s)204 Memory206 Interface(s)208 Processing Engine210 Database300 System Architecture302 Charging Function (CHF) / Pro visioning Gateway (PGW)304 Subscriber Profile Repository (SPR)306 SPR Dump Tool400 Flow Diagram500 Method Flow Diagram600 Computer System610 External Storage Device620 Bus630 Main Memory640 Read-Only Memory650 Mass Storage Device660 Communication Ports670 ProcessorDETAILED DESCRIPTION

[0029] 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 embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address any of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein. Example embodiments of the present disclosure are described below, as illustrated in various drawings in which like reference numerals refer to the same parts throughout the different drawings.

[0030] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

[0031] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.

[0032] Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed inparallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

[0033] The word “exemplary” and / or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter 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 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 like the term “comprising” as an open transition word without precluding any additional or other elements.

[0034] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0035] The terminology used herein is to describe particular embodiments only and is not intended to be limiting the disclosure. As used herein, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. As used herein, the term “and / or” includes any combinations of one or more of the associated listed items. It should be noted that the terms “mobile device”, “user equipment”, “user device”, “communication device”, “device” and similar terms are used interchangeably for the purpose of describing the invention. These terms are not intended to limit the scope of the invention or imply any specific functionality or limitations on the described embodiments. The use of these terms is solely for convenience and clarity of description. The invention is not limited to any particular type of device orequipment, and it should be understood that other equivalent terms or variations thereof may be used interchangeably without departing from the scope of the invention as defined herein.

[0036] While considerable emphasis has been placed herein on the components and component parts of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment, as well as other embodiments of the disclosure, will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.

[0037] In communication systems, a large database is a repository that stores and manages extensive volumes of data, which are utilized for processing, querying, and analysis. This data comprises customer details, communication logs, network performance metrics, and other relevant information related to communication services. However, manually getting customer data from large databases can be difficult because it involves going through a lot of information, which takes a lot of time and can lead to mistakes. The problem worsens when the data is spread across different tables or when complicated searches are needed, making the process even slower and less accurate.

[0038] Accordingly, there is a need for systems and methods to efficiently and securely extract, segregate and organize large volumes of customer-related data from large databases.

[0039] The present disclosure aims to overcome the above-mentioned and other existing problems in this field of technology by providing a system and a method for customer data extraction in a network. A network function (charging function CHF or provisioning gateway (PGW)) receives data from one or more network elements. The network function stores the received data in a database or a subscriber profile repository (SPR). The system (also referred to as a SPR dump tool) then extracts one or more network parameters (e.g., Subscription Permanent Identifier (SUPI), Media Access Control Identifier (MAC ID) lists) from the database or the SPR. Thereafter, the system fetches customer data / details corresponding to each SUPI / MAC ID from the database / SPR and dumps the fetched customer data in a main spreadsheet. Further, the system creates a plurality of sub-spreadsheet from the main spreadsheet based on one or more conditions / requirements. The system efficiently accesses and retrieves the customer data stored in complex database structures. The retrieved data is then used for quick analysis and decision-making based on the requirements. This way, the system / SPR dump tool fetches customer information and solves several practical problems related to data management, security, and operational efficiency.Further, it enhances operational efficiency, supports strategic decision-making, and strengthens data governance practices.

[0040] Hereinafter, exemplary embodiments of the present disclosure will be described with reference to the accompanying drawings.

[0041] FIG. 1 illustrates an example of a network architecture (100) for customer data extraction in a network (106), in accordance with an embodiment of the present disclosure.

[0042] As illustrated in FIG. 1, the network architecture (100) may include one or more computing devices or UEs (104-1, 104-2... 104-N) associated with one or more users (102-1, 102-2... 102-N) in an environment. A person of ordinary skill in the art will understand that one or more users (102-1, 102-2... 102-N) may be individually referred to as the user (102) and collectively referred to as the users (102). Similarly, a person of ordinary skill in the art will understand that one or more UEs (104-1, 104-2... 104-N) may be individually referred to as the UE (104) and collectively referred to as the UEs (104). A person of ordinary skill in the art will appreciate that the terms “computing device(s)” and “user equipment” may be used interchangeably throughout the disclosure. Although three UEs (104) are depicted in FIG. 1, however, any number of the UEs (104) may be included without departing from the scope of the ongoing description.

[0043] In an embodiment, the UE (104) may include smart devices operating in a smart environment, for example, an Internet of Things (loT) system. In such an embodiment, the UE (104) may include, but is not limited to, smart phones, smart watches, smart sensors (e.g., a mechanical sensor, a thermal sensor, an electrical sensor, a magnetic sensor, etc.), networked appliances, networked peripheral devices, networked lighting system, communication devices, networked vehicle accessories, networked vehicular devices, smart accessories, tablets, smart televisions (TVs), computers, smart security systems, smart home systems, other devices for monitoring or interacting with or for the user (102) and / or entities, or any combination thereof. A person of ordinary skill in the art will appreciate that the UE (104) may include, but is not limited to, intelligent, multi-sensing, network-connected devices, that can integrate seamlessly with each other and / or with a central server or a cloudcomputing system or any other device that is network-connected.

[0044] In an embodiment, the UE (104) may include, but is not limited to, a handheld wireless communication device (e.g., a mobile phone, a smart phone, a phablet device, and so on), a wearable computer device (e.g., a head-mounted display computer device, a head-mounted camera device, a wristwatch computer device, and so on), a Global Positioning System (GPS) device, a laptop computer, a tablet computer, or another type of portable computer, a media playing device, a portable gaming system, and / or any other type of computer device with wireless communication capabilities, and the like. In an embodiment, the UE (104) mayinclude, but is not limited to, any electrical, electronic, electro-mechanical, or an equipment, or a combination of one or more of the above devices such as virtual reality (VR) devices, augmented reality (AR) devices, a laptop, a general-purpose computer, a desktop, a personal digital assistant, a tablet computer, a mainframe computer, or any other computing device. Further, the UE (104) may include one or more in-built or externally coupled accessories including, but not limited to, a visual aid device such as a camera, an audio aid, a microphone, a keyboard, and input devices for receiving input from the user (102) or an entity such as a touch pad, a touch enabled screen, an electronic pen, and the like. A person of ordinary skill in the art will appreciate that the UE (104) may not be restricted to the mentioned devices and various other devices may be used.

[0045] In an aspect, a network function (e.g., a charging function CHF or provisioning gateway (PGW)) receives data from one or more network elements (e.g., base station, policy control function (PCF), a policy and charging rules function (PCRF), a user plane function (UPF), a serving gateway (SGW), a home subscriber server (HSS), an application function (AF), etc.) in the network (106). The CHF / PGW stores the received data in a database or a subscriber profile repository (SPR). The system (108) then extracts one or more network parameters (e.g., Subscription Permanent Identifier (SUPIs), Media Access Control Identifier (MAC ID) lists) from the database or the SPR. Thereafter, the system (108) fetches the customer data corresponding to each MAC ID present in the MAC ID list from the database or the SPR. Further, the system (108) generates a main spreadsheet based on the fetched customer data. Finally, the system (108) creates a plurality of subspreadsheet from the main spreadsheet based on one or more predefined conditions or requirements of network operators, service providers, network engineers, technicians, customer support teams, researchers, and security professionals.

[0046] In an embodiment, the network (106) may include at least one of the 4G network, the 5G network, the 6G network, or the like. The network (106) may enable the UE (104) to communicate with other devices in the network architecture (100) and / or with the system (108). The network (106) may include a wireless card or some other transceiver connection to facilitate this communication. In another embodiment, the network (106) may be implemented as, or include any of a variety of different communication technologies such as a wide area network (WAN), a local area network (LAN), a wireless network, a mobile network, a Virtual Private Network (VPN), 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. In another embodiment, the network (106) includes, by way of example but not limitation, at least a portion of one or more networks having one or more nodes thattransmit, 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.

[0047] In FIG. 1, the UE (104) may communicate with the system (108) through the network (106). In particular, the UE (104) may be communicatively coupled with the network (106). The coupling includes steps of receiving, by the network (106), a connection request from the UE (104). Upon receiving the connection request, the coupling includes steps of sending, by the network (106), an acknowledgment of the connection request to the UE (104). Further, the coupling includes steps of transmitting a plurality of signals in response to the connection request. The plurality of signals is responsible for establishing communication of the UE (104) with the system (108). The system (108) is then configured to perform customer data extraction in the network (106).

[0048] Although FIG. 1 shows exemplary components of the network architecture (100), in other embodiments, the network architecture (100) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 1. Additionally, or alternatively, one or more components of the network architecture (100) may perform functions described as being performed by one or more other components of the network architecture (100).

[0049] FIG. 2 illustrates an exemplary block diagram (200) of the system (108) for customer data extraction in the network (106), in accordance with an embodiment of the present disclosure. FIG. 2 is explained in conjunction with the FIG. 1.

[0050] Referring to FIG. 2, in an embodiment, the system (108) may include one or more processor(s) (202). The one or more processor(s) (202) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and / or any devices that process data based on operational instructions. Among other capabilities, the one or more processor(s) (202) may be configured to fetch and execute computer-readable instructions stored in a memory (204) of the system (108). The memory (204) may be configured to store one or more computer-readable instructions or routines in a non-transitory computer readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory (204) may comprise any non-transitory storage device including, for example, volatile memory such as random-access memory (RAM), or non-volatile memory such as erasable programmable read only memory (EPROM), flash memory, and the like.

[0051] In an embodiment, the system (108) may include an interface(s) (206). The interface(s) (206) may comprise a variety of interfaces, for example, interfaces for data input and output devices (VO), storage devices, and the like. The interface(s) (206) may facilitate communication through the system (108). The interface(s) (206)may also provide a communication pathway for one or more components of the system (108). Examples of such components include, but are not limited to, processing engine(s) (208) and a database (210).

[0052] In an embodiment, the processing engine(s) (208) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) (208). In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) (208) may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) (208) may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) (208). In such examples, the system (108) may comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the system (108) and the processing resource. In other examples, the processing engine(s) (208) may be implemented by electronic circuitry.

[0053] In an embodiment, the database (210) includes data that may be either stored or generated as a result of functionalities implemented by any of the components of the processor (202) or the processing engines (208). In an embodiment, the database (210) may be separated from the system (108). In an embodiment, the database (210) may be indicative of including, but not limited to, a relational database, a distributed database, a cloud-based database, or the like.

[0054] In an aspect, the processing engine (208) comprises an extraction unit (212) and a processing unit (214).

[0055] In an aspect, the system (108) may function as database dump tool (also referred to as subscriber profile repository (SPR) dump tool) to access, extract, and organize large volume of the customer data stored in the database (210).

[0056] In an aspect, the processing engine (208) is configured to perform customer data extraction in the network (106).

[0057] In an aspect, the customer data present in the database (210) is obtained from one or more network elements. A charging function (CHF) / provisioning gateway (PGW) (shown in FIG. 3) receives data (e.g., customer data) from the one or more network elements. In an aspect, the one or more network elements comprise, but are not limited to, a base station, a policy control function (PCF), a policy and charging rules function (PCRF), a user plane function (UPF), a serving gateway (SGW), a home subscriber server (HSS), and an application function (AF), etc.

[0058] In an aspect, the charging function (CHF) refers to a network function responsible for managing and processing charging-related activities for the services provided to users. The CHF performs service usage monitoring, real-time monitoring, offline charging, balance management, etc.

[0059] In an aspect, the Packet Gateway (PGW) refers to a network element responsible for connecting a mobile network to an external internet protocol (Debased network (e.g., internet or private IP networks) and managing data traffic between the networks, such as the network (106) and user equipments (UEs), such as the UE (104).

[0060] In an aspect, the base station refers to a network element responsible for providing radio communication services between the user equipment (e.g., the UE 104) and the core network. The base station connects the UEs (104) in the radio access network (RAN) to the rest of the network infrastructure. The base station may be node B, an evolved node B (eNodeB) in 4G network or gNodeB in 5G network.

[0061] In an aspect, the PCF refers to a network function responsible for managing and enforcing policy rules for different types of network traffic. The PCF ensures that various network services are delivered according to the quality of service (QoS) requirements and network policies defined by the network operators.

[0062] In an aspect, the PCRF refers to a network function responsible for managing policy enforcement and charging rules for network traffic. The PCRF provides policy decisions that control data traffic based on subscriber profiles, service plans, and network conditions. The PCRF enforces charging rules by coordinating with the CHF to ensure users are billed correctly based on their usage.

[0063] In an aspect, the UPF refers to a network function responsible for handling user data traffic (the user plane) and for forwarding packets between the UE (104) and the core network (106). The UPF routes data from the UE (104) to the internet or other external networks.

[0064] In an aspect, the SGW refers to a network element that serves as an intermediary between the radio access network (RAN) and the core network. The SGW is responsible for handling user data and controlling the user plane (data plane), ensuring that traffic is routed effectively and efficiently. In an aspect, the core network can be the network (106).

[0065] In an aspect, the HSS is a central database in the network (106) and is responsible for user authentication, subscriber profile management, and service authorization.

[0066] In an aspect, the application function refers to a network function responsible for interacting with the network's core services to provide applicationlevel functions. The application function enables service-based communication between the core network and external applications (e.g., media services, gaming platforms, loT devices, etc.).

[0067] The data comprises one or more of network parameters, subscriber data, communication logs, network performance, security and authentication, billing and charging information, quality of service (QoS) information and the like.

[0068] In an aspect, the network parameters comprise one or more network identifier (e.g., subscription permanent identifiers (SUPIs) and media access control (MAC) identifiers (IDs)). In an aspect, the subscriber data comprises subscriber profile information (e.g., subscriber identifier (ID), subscriber plan, roaming data, services, device type). The network usage data comprises usage (e.g., daily, weekly, and monthly usage) and connection quality data (e.g., metrics such as signal strength, latency, jitter, and download / upload speed). The authentication and security information comprises authentication credentials (IMSI, keys for encryption, etc.). The security settings comprise details on encryption, privacy settings, and authentication tokens. The billing and charging information comprise account balance information (e.g., balance for voice, data, and text services) and payment history (e.g., records of past payments, billing cycles, and payment methods). Charging data comprises details on how services are billed, including data consumption, roaming charges, and special service usage. The QoS information comprises priority level requirements (e.g., some users may have priority access based on service type or subscription (e.g., enterprise customers, first responders)) and latency requirements (e.g., ultra-low latency for gaming or loT applications). Network slicing information comprises network slicing details. Location and mobility information comprises current location details, movement patterns and handover details. Application usage data comprises insights into what services or apps the customer uses most frequently (e.g., streaming, gaming, AR / VR applications). The communication logs in the network (106) are essential for tracking network operations, diagnosing issues, ensuring security, and providing insights into the network's performance. The communication logs capture various data points related to network activities, user interactions, and system performance. The communication logs comprise call detail records (CDR), session logs, network performance logs, and data traffic logs. In an aspect, the CDR comprises call type (e.g., voice or video), call start time, call end time, data usage, etc. The session logs comprise session identifier (ID), session duration, session start and end time. The network performance logs comprise throughput, latency, packet loss, reference signal received power (RSRP), reference signal received quality (RSRQ), and signal-to-interference plus noise ratio (SINR). The data traffic logs comprise information about the amount of data transferred, data duration, service, and quality.

[0069] The CHF / PGW then saves the received data in the database, such as the database (210) or the SPR (e.g., subscriber profile repository (SPR) (304), as shown in FIG. 3).

[0070] The extraction unit (212) is configured to extract one or more network parameters from customer data present in the database (210) or the subscriber profile repository (SPR) (304). The one or more network parameters comprise subscription permanent identifiers SUPIs) and media access control (MAC) identifiers (IDs). In an aspect, the SUPI refers to a unique identifier assigned to a subscriber to allow the network (106) to recognize and authenticate the subscriber across the network (106). The SUPI is responsible for managing subscriber-related services, such as network access, authentication, and charging.

[0071] In an aspect, the MAC ID refers to a unique identifier assigned to network interfaces for communications on the physical network segment. The MAC ID is used to identify devices on a local area network (LAN) or other network infrastructures.

[0072] The processing unit (214) is configured to generate a MAC ID list based on the SUPIs. In an aspect, the processing unit (214) may generate one or more MAC ID lists comprising MAC ID associated with each user (102) present in the network (106).

[0073] The extraction unit (212) is configured to iteratively fetch customer data corresponding to each MAC ID of the MAC ID list. In an aspect, the extraction unit (212) is configured to sequentially process the MAC IDs included in the generated MAC ID list and obtain customer-specific data corresponding to each respective MAC ID. The extraction unit (212) further repeats the retrieval operation in an iterative, one-by-one manner for individual MAC IDs until customer data associated with substantially all MAC IDs in the generated MAC ID list has been collected, thereby facilitating systematic and comprehensive extraction of customer data mapped to the respective device identifiers.

[0074] In an aspect, the customer data comprises, but are not limited to, subscriber information, service and billing information, network usage and session data, authentication and security data, and location-related information.

[0075] The processing unit (214) is configured to generate a main spreadsheet based on the fetched customer data. In an aspect, the main spreadsheet is a comprehensive or master file that contains a large amount of data. It contains all the information needed for the project, task, management, or business process.

[0076] In an aspect, the spreadsheet can be a data file, an Excel file, a Google sheet, a smart sheet, a workbook, a worksheet, etc. The spreadsheet may store data in a key-value format or any suitable data structure format, tabular format, Excel format or any other format.

[0077] Further, the processing unit (214) stores the fetched customer data into the main spreadsheet. In an example, the data (e.g., network data) corresponding to each MAC IDs is stored in the main spreadsheet.

[0078] Further, the processing unit (214) is configured to generate a plurality of sub-spreadsheets from the main spreadsheet based on one or more conditions. In an aspect, the sub-spreadsheets refer to individual spreadsheets generated from the main spreadsheet.

[0079] In an aspect, the one or more conditions comprise data analysis, data verification, backup and disaster recovery, performance analysis, data retrieval, operation management, network management, service provisioning, mobility management, security management, billing and charging management.

[0080] In an aspect, the processing unit (214) automatically divides the main spreadsheet into multiple sub-spreadsheets (e.g., smaller spreadsheets) according to conditions (e.g., specific purposes or rules). Each condition represents a criterion used to filter, group, or reorganize the data so that different operational needs may be addressed efficiently. The conditions may include data analysis for creating subspreadsheets containing selected datasets for statistical evaluation, trend identification, or reporting. Data verification for isolating records that require validation, error checking, or consistency review. Backup and disaster recovery for generating copies or segmented datasets to ensure data can be restored in case of failure or loss. Performance analysis for extracting performance-related parameters (e.g., system metrics, KPIs) for monitoring and optimization. Data retrieval for organizing data into searchable subsets for faster access to specific information. Operation management for grouping operational records to support monitoring and control of ongoing activities. Network management for segregating network-related information (e.g., nodes, traffic, faults) for administration. Service provisioning for extracting service configuration or deployment data for enabling or modifying services. Mobility management for separating mobility-related records (e.g., user movement, handovers, location updates). Security management for isolating security logs, access records, or threat-related data for protection measures. Billing and charging management for compiling usage and transaction data required for billing, invoicing, or charging processes.

[0081] In this way, the processing unit (214) applies the conditions as rules to automatically organize the main spreadsheet into purpose-specific sub-spreadsheets, enabling efficient handling, monitoring, and decision-making across different functional domains.

[0082] In an aspect, the one or more conditions are configured by network operators. The requirements can be real-time or pre-configured. The requirements may come from system administrators, such as network operators, service providers, network engineers, technicians, customer support teams, researchers, and security professionals. In an example, for the network data, the requirements may come from the network operators for usage analysis, from the security professionals for security data analysis and from the network engineers for performance monitoring.

[0083] In this way, the system (108) ensures efficient and systematic data retrieval, thereby enabling quick analysis and decision-making. The database dump tool for customers provides a comprehensive approach to extracting, organizing, securing, and utilizing customer data efficiently. By combining automation, customization, security, and scalability, the tool effectively addresses critical business needs related to data management, compliance, and decision-making processes. Collectively, such features contribute to the uniqueness and value proposition of the tool for modern data-driven enterprises.

[0084] Although FIG. 2 shows exemplary components of the system (108), in other embodiments, the system (108) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 2. Additionally, or alternatively, one or more components of the system (108) may perform functions described as being performed by one or more other components of the system (108).

[0085] FIG. 3 illustrates an example of a system architecture (300) for customer data extraction in the network (106), in accordance with an embodiment of the present disclosure. FIG. 3 is explained in conjunction with the FIGs. 1-2.

[0086] The system architecture (300) comprises a charging function (CHF) / provisioning gateway (PGW) (302), a subscriber profile repository (SPR) (304) / database (210), and an SPR dump tool (306).

[0087] In an aspect, the system (108) may function as the SPR dump tool (306). In an aspect, the system (108) may comprise the SPR dump tool (306). Further, in another aspect, the processing engine (208) may function as the SPR dump tool (306).

[0088] The charging function (CHF) / provisioning gateway (PGW) (302) receives data from one or more network elements.

[0089] Once the data is received, the CHF / PGW (302) saves the received data in the subscriber profile repository (SPR) (304) or the database (210).

[0090] In an aspect, the SPR (304) refers to a database responsible for managing subscriber-related data and profiles. The SPR (304) stores and manages data such as, but is not limited to, service-related data, connection data, registration data, authentication data, subscriber profile data and network data. In an aspect, the service-related data comprises, but is not limited to, call logs, usage data, messaging service-related data, voice over internet protocol (IP) session data, etc. In an aspect, the connection data comprises, but is not limited to, session initiation logs, radioresource connection data, handover, network quality, etc. In an aspect, the registration data comprises, but is not limited to, subscriber identity information (e.g., International Mobile Subscriber Identity (IMSI), Subscriber Identity Module (SIM)), device registration data (e.g., International Mobile Equipment Identity (IMEI), media access control identifier (MAC ID), MAC addresses, etc.). In an aspect, theauthentication data comprises, but is not limited to, login credentials, authentication tokens, access control lists, etc. In an aspect, the subscription data comprises, but is not limited to, subscriber name, contact details, subscription plan, billing information, usage preferences, service type, customer history data, etc. In an aspect, the network data comprises, but is not limited to, traffic data, bandwidth utilization, routing and switching information, network performance metrics, etc.

[0091] The SPR dump tool (306) analyzes (or scans) the data stored in the database (210) and the SPR (304). Based on the analysis, the SPR dump tool (306) extracts Subscription Permanent Identifiers (SUPIs) and Media Access Control Identifier (MAC ID) lists from the database (210) or the SPR (304). In an aspect, the SPR dump tool (306) extracts SUPIs from the data corresponding to authentication and registration. In an aspect, the SPR dump tool (306) extracts the MAC ID lists from data corresponding to radio resource connection (RRC) messages.

[0092] In an aspect, the SPR dump tool (306) (also referred to as database dump tool) refers to an automation tool / application designed to manage and utilize customer data effectively based on the needs of businesses and organizations.

[0093] In particular, the SPR dump tool (306) fetches the customer data for each MACID in the MAC ID lists from the database (210) or the SPR (304). Then, the SPR dump tool (306) dumps / stores the fetched customer data / details in a main spreadsheet (e.g., main Excel file). Further, based on the requirements / business needs, the SPR dump tool (306) creates a plurality of sub-spreadsheets (e.g., subExcel files) using the main spreadsheet. The requirements / business needs comprise data segmentation, data sharing, performance optimization, scalability, compliance and regulations. For example, in a telecommunication network (e.g., network (106)), the business needs and requirements comprise network planning, network traffic optimization, service management, customer segmentation, usage analysis and reporting, call data recording, network upgradation, etc.

[0094] In an operative aspect, the main spreadsheet includes data corresponding to the MAC ID. The data corresponding to the MAC ID comprise, but is not limited to, MAC address, subscription plan, device type, device manufacture, device model, connection history, network issues, etc.

[0095] The main spreadsheet serves as the central source from which different subsets of data (i.e., sub-spreadsheets) can be extracted and used for various purposes. The sub-spreadsheets are smaller sheets created from the main spreadsheet. The sub-spreadsheets are automatically generated to handle requirements / conditions. The conditions comprise specific tasks, business areas, departments, or user needs. The requirements may correspond to network operators, service providers, network engineers, technicians, customer support teams, researchers, and security professionals.

[0096] In an aspect, the dumped customer details / data are also referred to as database dumps. The database dumps provide insights into data distribution, query patterns, and overall database structure, helping optimize indexing, query execution plans, and resource allocation. The database dumps are used for performance tuning and analysis. Further, the SPR dump tool (306) automates the process of fetching the customer information, enabling quick and efficient extraction of customer-related data fields.

[0097] In an aspect, the SPR dump tool (306) incorporates a transactions per second (TPS) control feature. The TPS control feature involves managing and limiting the number of transactions (i.e., creation of sub-Excel files) per second performed by the SPR dump tool (306). This helps prevent system overloads, maintain performance, and ensure consistency in handling large volumes of data. Further, the SPR dump tool (306) helps regulate the data retrieval rate. In an example, the SPR dump tool (306) handles 50 TPS (i.e., 50 sub-Excel files per second). But if the current TPS exceeds 50 (e.g., 200 sub-Excel files per second), the SPR dump tool (306) queues incoming transactions (e.g., sub-Excel files) until the rate drops back to an acceptable level (i.e., 50 sub-Excel files per second).

[0098] The SPR dump tool (306) dumps customer information into a structured format. The structure format comprises table format or sheets. The SPR dump tool (306) automatically segregates / categorizes the data of the main spreadsheet. Based on the categorization, the plurality of sub-spreadsheets is created for different purposes. In an aspect, the plurality of sub-spreadsheets is created based on predefined conditions. This enables easy data analysis, reporting, deriving actionable insights and improving marketing strategies, customer service, and product offerings.

[0099] In an example, the main spreadsheet comprises the communication data. The communication data comprises records of calls made by different users, with details (e.g., customer ID, call ID, origin number, destination number, timestamp, call duration, region, call type). The communication data is segregated into multiple subspreadsheets based on predefined conditions. The predefined conditions comprise time-based, user-based (i.e., customer ID), region-based, call type. The plurality of sub-spreadsheets is created for the time-based data, the user-based data (i.e., customer ID), the region-based data, the call type data.

[0100] The SPR dump tool (306) is an essential component in backup and disaster recovery. Regularly dumping customer data into backup spreadsheets allows operations to be quickly restored while minimizing downtime in a data loss incident.

[0101] The SPR dump tool (306) supports scalability by efficiently handling large datasets and accommodating growing data volumes. It also ensures continued operational continuity by providing reliable access to customer information for ongoing business / network operations and growth initiatives.

[0102] The SPR dump tool (306) efficiently and securely extracts and organizes large volumes of customer-related data from the database. The tool accesses and organizes the customer data stored in complex database structures. The SPR dump tool (306) ensures that data retrieval is efficient and systematic, allowing for quick analysis and decision-making.

[0103] The SPR dump tool (306) efficiently extracts, organizes, secures, and utilizes customer data. By combining automation, customization, security, and scalability, the SPR dump tool (306) effectively addresses critical business needs in data management, compliance, and decision-making processes.

[0104] FIG. 4 illustrates an example flow diagram (400) for data extraction in the network (106), in accordance with an embodiment of the present disclosure. FIG. 4 is explained in conjunction with the FIGs. 1-3.

[0105] At step (402) of the flow diagram (400), the CHF / PGW (302) receives data from one or more network elements. The data comprises, but is not limited to, customer information, communication logs, network performance data, and other relevant data for communication services in the network (106). The customer information comprises, but is not limited to, subscriber profile information, network usage data, authentication and security information, billing and charging information, location and mobility information, services information, and application-specific information. The network performance comprises, but is not limited to, latency, throughput, packet loss, QoS information. The communication logs comprise, but are not limited to, call detail records (CDR), session logs, network performance logs, and data traffic logs.

[0106] In an aspect, the one or more network elements comprise, but are not limited to, the base station, the policy control function (PCF) or the policy and charging rules function (PCRF), the user plane function (UPF), the serving gateway (SGW), the home subscriber server (HSS), the application function, etc.

[0107] At step (404) of the flow diagram (400), the CHF / PGW (302) saves the received data in the database (210) or the SPR (304).

[0108] At step (406) of the flow diagram (400), the SPR dump tool (306) extracts SUPIs and MAC ID lists from the database (210) or the SPR (304).

[0109] At step (408) of the flow diagram (400), the customer data for each MAC ID in the MAC ID lists is fetched from the database (210) or the SPR (304).

[0110] At step (410) of the flow diagram (400), the fetched customer data is dumped in a main spreadsheet file (e.g., main Excel file).

[0111] At step (412) of the flow diagram (400), a plurality of sub-spreadsheet files (e.g., sub-Excel files) is created using the main spreadsheet file (e.g., main Excel file) based on the predefined conditions / requirements / business needs. The requirements / business needs may comprise, but are not limited to, billing and accounting, network optimization, regulatory compliance, security, services, dataanalysis, network maintenance and operation, and data protection and privacy. The customer data may be used by, but is not limited to, network operators, service providers, regularity authorities, network equipment vendors and suppliers, network maintenance and operation teams, and network performance analyzers.

[0112] FIG. 5 illustrates an exemplary flow diagram of a method (500) for customer data extraction in the network, in accordance with an embodiment of the present disclosure. FIG. 5 is explained in conjunction with FIGs 1-4.

[0113] At step (502), the method (500) includes extracting, by the extraction unit (212), one or more network parameters from customer data present in the database (210) or the subscriber profile repository (SPR) (304). In an aspect, a charging function (CHF) / provisioning gateway (PGW) receives the customer data from network elements (e.g., a base station, a policy control function (PCF), a policy and charging rules function (PCRF), a user plane function (UPF), a serving gateway (SGW), a home subscriber server (HSS), and an application function (AF)). The CHF / SPR stores the customer data in the database (210) or SPR (304). The extraction unit (212) extracts the one or more network parameters from the customer data stored in the database (210) / SPR (304). The one or more network parameters comprise subscription permanent identifiers (SUPIs) and media access control (MAC) identifiers (IDs).

[0114] At step (504), the method (500) includes generating, by the processing unit (214), a MAC ID list based on the SUPIs. The processing unit generates the MAC ID list comprising MAC ID associated with each user (102) present in the network (106).

[0115] At step (506), the method (500) includes iteratively fetching, by the extraction unit (212), customer data corresponding to each MAC ID of the MAC ID list. In an aspect, the extraction unit (212) sequentially accesses the MAC IDs contained in the generated MAC ID list and retrieves customer-specific data associated with each respective MAC ID. The extraction unit (212) performs the fetching operation repeatedly in a looped or stepwise manner for one MAC ID at a time until customer data corresponding to substantially all MAC IDs in the generated MAC ID list has been obtained, thereby ensuring systematic, complete, and organized extraction of customer information mapped to the listed device identifiers.

[0116] In an aspect, the customer data comprises subscriber information, service and billing information, network usage and session data, authentication and security data, and location-related information.

[0117] At step (508), the method (500) includes generating, by the processing unit (214), a main spreadsheet based on the fetched customer data. In an aspect, the processing unit generates the main spreadsheet and stores the fetched customer data in the generated main spreadsheet (e.g., Excel, worksheet, workbook, etc.).

[0118] In an aspect, the method (500) includes creating, by the processing unit (214), a plurality of sub-spreadsheets from the main spreadsheet based on one or more conditions. In an aspect, the processing unit (214) is configured to automatically partition the main spreadsheet into a plurality of sub-spreadsheets based on one or more predefined conditions (e.g., specified purposes or rules). Each condition defines a criterion for filtering, categorizing, or restructuring the data, thereby enabling the data to be organized into separate subsets to efficiently address different operational requirements.

[0119] The one or more conditions comprise data analysis, data verification, backup and disaster recovery, performance analysis, data retrieval, operation management, network management, service provisioning, mobility management, security management, billing and charging management.

[0120] FIG. 6 illustrates an exemplary computer system (600) in which or with which embodiments of the present disclosure may be implemented.

[0121] As shown in FIG. 6, the computer system (600) may include an external storage device (610), a bus (620), a main memory (630), a read-only memory (640), a mass storage device (650), communication port(s) (660), and a processor (670). A person skilled in the art will appreciate that the computer system may include more than one processor and communication ports. The processor (670) may include various modules associated with embodiments of the present disclosure. The communication port(s) (660) may be any of an RS-232 port for use with a modembased dialup connection, a 10 / 100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication port(s) (660) may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system connects.

[0122] The main memory (630) may be random access memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory (640) may be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information, e.g., start-up or Basic Input / Output System (BIOS) instructions for the processor (670). The mass storage device (650) may be any current or future mass storage solution, which can be used to store information and / or instructions. Exemplary mass storage device (650) includes, but is not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and / or Firewire interfaces), one or more optical discs, Redundant Array of Independent Disks (RAID) storage, e.g., an array of disks.

[0123] The bus (620) communicatively couples the processor (670) with the other memory, storage, and communication blocks. The bus (620) may be, e.g., aPeripheral Component Interconnect (PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), Universal Serial Bus (USB), or the like, for connecting expansion cards, drives, and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor (670) to the computer system.

[0124] Optionally, operator and administrative interfaces, e.g., a display, keyboard, joystick, and a cursor control device, may also be coupled to the bus (620) to support direct operator interaction with the computer system. Other operator and administrative interfaces can be provided through network connections connected through the communication port(s) (660). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system limit the scope of the present disclosure.

[0125] The present disclosure discloses a computer program product comprising a non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to execute a method for customer data extraction in a network is disclosed. The method comprises extracting, by an extraction unit, one or more network parameters from customer data present in a database or a subscriber profile repository (SPR). The one or more network parameters comprise subscription permanent identifiers (SUPIs) and media access control (MAC) identifiers (IDs). The method comprises generating, by a processing unit, a MAC ID list based on the SUPIs. The method comprises iteratively fetching, by the extraction unit, customer data corresponding to each MAC ID of the MAC ID list. The method comprises generating, by the processing unit, a main spreadsheet based on the fetched customer data.

[0126] The present disclosure provides significant technical enhancements by employing a database dump tool that efficiently and securely extracts and organizes large volumes of customer-related data from a database. Currently, manual extraction of the customer data from large databases is time-consuming and susceptible to human error, particularly when complex queries or multiple interconnected tables are involved. Such manual processes can lead to inconsistencies, delays, and reduced efficiency in data retrieval operations. The present disclosure provides the database dump tool that provides a streamlined mechanism for accessing and organizing customer data stored within complex database structures, thereby enabling efficient and systematic data retrieval for rapid analysis and informed decision-making. The tool offers a comprehensive approach to extracting, structuring, securing, and utilizing customer data through automation and customization features. By incorporating security, scalability, and compliance capabilities, the tool effectively addresses critical data management requirements in modern data-driven enterprises, thereby enhancing its operational value and distinctiveness.

[0127] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departingfrom the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.ADVANTAGES OF THE PRESENT DISCLOSURE

[0128] The present disclosure provides data inspection and verification. The spreadsheets allow users (e.g., network operators) to inspect the contents in a human-readable format. This further helps verify the integrity of data, ensure it matches expectations, and identify any anomalies or inconsistencies.

[0129] The present disclosure provides insights into data distribution, query patterns, and overall database structure, helping optimize indexing, query execution plans, and resource allocation through performance tuning and analysis.

[0130] The present disclosure provides efficient data retrieval with transactions per second (TPS) control. A dedicated database dump tool automates fetching customer information, enabling quick and efficient extraction of customer-related data fields. The TPS control helps to regulate the data retrieval rate, prevent system overload and maintain optimal performance.

[0131] The present disclosure provides easy data analysis and reporting by dumping customer information into a structured format. This enables businesses to derive actionable insights and improve marketing strategies, customer service, and product offerings.

[0132] The present disclosure provides backup and disaster recovery by regularly dumping customer data into backup files, restoring operations quickly and minimizing downtime during a data loss incident.

[0133] The present disclosure provides operational continuity and scalability by efficiently handling large datasets and accommodating growing data volumes. It ensures continued operational continuity by providing reliable access to customer information for ongoing business operations and growth initiatives.

Claims

CLAIMSWe claim:

1. A method (500) for customer data extraction in a network (106), the method (500) comprising:extracting (502), by an extraction unit (212), one or more network parameters from the customer data present in a database (210) or a subscriber profile repository (SPR) (304), wherein the one or more network parameters comprise subscription permanent identifiers (SUPIs) and media access control (MAC) identifiers (IDs);generating (504), by a processing unit (214), a MAC ID list based on the SUPIs;iteratively fetching (506), by the extraction unit (212), the customer data corresponding to each MAC ID of the MAC ID list; andgenerating (508), by the processing unit (214), a main spreadsheet based on the fetched customer data.

2. The method (500) as claimed in claim 1, further comprising:creating, by the processing unit (214), a plurality of sub-spreadsheets from the main spreadsheet based on one or more conditions.

3. The method (500) as claimed in claim 2, wherein the one or more conditions comprises data analysis, data verification, backup and disaster recovery, performance analysis, data retrieval, operation management, network management, service provisioning, mobility management, security management, and billing and charging management.

4. The method (500) as claimed in claim 1, wherein the customer data comprises subscriber information, service and billing information, network usage and session data, authentication and security data, and location-related information.

5. A system (108) for customer data extraction in a network (106), the system (108) comprising:an extraction unit (212) configured to extract one or more network parameters from the customer data present in a database (210) or a subscriber profile repository (SPR) (304), wherein the one or more network parameters comprise subscription permanent identifiers (SUPIs) and media access control (MAC) identifiers (IDs);a processing unit (214) configured to generate a MAC ID list based on the SUPIs;the extraction unit (212) configured to iteratively fetch the customer data corresponding to each MAC ID of the MAC ID list; andthe processing unit (214) configured to generate a main spreadsheet based on the fetched customer data.

6. The system (108) as claimed in claim 5, wherein the processing unit (214) is configured to generate a plurality of sub-spreadsheets from the main spreadsheet based on one or more conditions.

7. The system (108) as claimed in claim 6, wherein the one or more conditions comprise data analysis, data verification, backup and disaster recovery, performance analysis, data retrieval, operation management, network management, service provisioning, mobility management, security management, and billing and charging management.

8. The system (108) as claimed in claim 5, wherein the customer data comprises subscriber information, service and billing information, network usage and session data, authentication and security data, and location-related information.

9. A computer program product comprising a non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to execute a method (500) for customer data extraction in a network (106), the method (500) comprising:extracting (502), by an extraction unit (212), one or more network parameters from the customer data present in a database (210) or a subscriber profile repository (SPR) (304), wherein the one or more network parameters comprise subscription permanent identifiers (SUPIs) and media access control (MAC) identifiers (IDs);generating (504), by a processing unit (214), a MAC ID list based on the SUPIs;iteratively fetching (506), by the extraction unit (212), the customer data corresponding to each MAC ID of the MAC ID list; andgenerating (508), by the processing unit (214), a main spreadsheet based on the fetched customer data.

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