FORM 2
THE PATENTS ACT, 1970 (39 of 1970) THE PATENTS RULES, 2003
COMPLETE SPECIFICATION
OF DATABASES
APPLICANT
380006, Gujarat, India; Nationality : India
The following specification particularly describes
the invention and the manner in which
it is to be performed

SYSTEM AND PROCESSOR-IMPLEMENTED METHOD FOR AUTOMATED MAINTENANCE OF DATABASES
RESERVATION 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, belong¬ing to Jio Platforms Limited (JPL) or its affiliates (hereinafter referred as owner). The owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all rights whatsoever. All rights to such intellectual property are fully reserved by the owner.
FIELD OF THE INVENTION
[0002] The present disclosure relates to the field of database management systems. More particularly, the present disclosure relates to a system and a processor-imple-mented method for automated maintenance of databases.
BACKGROUND ART
[0003] The following description of related art is intended to provide background in-formation 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. How¬ever, it should be appreciated that this section be used only to enhance the understand¬ing of the reader with respect to the present disclosure, and not as admissions of prior art.
[0004] Typically, in big data storage solutions, databases are stored in one or more database nodes distributed over a plurality of geographical regions. Such solutions of-ten have several monitoring and maintenance systems that ensure databases continue
2

to run without degradation in performance or failure. Particularly in the context of tel-ecom data collection and storage, it is important to have resilient databases. Further, in the event of failure, issues with databases must be resolved with minimal downtime. Lack of auto-healing capabilities for database nodes can lead to prolonged downtime, delayed issue resolution, increased operational burden, lower system resilience, poten¬tial data loss, inefficient resource utilization, and scalability challenges.
[0005] To address these challenges, there is therefore a need in the art to provide a method and a system that can overcome the shortcomings of the existing prior arts.
OBJECT OF THE INVENTION [0006] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.
[0007] A primary object of the embodiments of the present invention is provide a sys-tem and a method for automated maintenance of databases.
[0008] Yet another object of the embodiments of the present invention is to provide a system and a method that reduces downtime with predictive maintenance. [0009] Yet another object of the embodiments of the present invention is to provide a system and a method that performs pre-emptive restarts to prevent failure. [0010] Yet another object of the embodiments of the present invention is to provide a system and a method that optimizes performance of the database.
[0011] Yet another object of the embodiments of the present invention is to improve service availability and enhance the overall reliability of the telecom infrastructure. [0012] These and other objectives and advantages of the embodiments of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings.
SUMMARY OF THE INVENTION

[0013] The following details present a simplified summary of the embodiments of the present invention to provide a basic understanding of the several aspects of the embod¬iments of the present invention. This summary is not an extensive overview of the em¬bodiments of the present invention. It is not intended to identify key/critical elements of the embodiments of the present invention or to delineate the scope of the embodi¬ments of the present invention. Its sole purpose is to present the concepts of the em¬bodiments of the present invention in a simplified form as a prelude to the more detailed description that is presented later.
[0014] The other objects and advantages of the embodiments of the present invention will become readily apparent from the following description taken in conjunction with the accompanying drawings. It should be understood, however, that the following de¬scriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and mod¬ifications may be made within the scope of the embodiments of the present invention without departing from the spirit thereof, and the embodiments of the present invention include all such modifications.
[0015] The embodiments of the present technology system and method for automated maintenance of databases. The system includes an artificial intelligence/machine learn-ing (AI/ML) module that continuously monitors the health and performance metrics of the database nodes, such as, but not limited to, CPU usage, memory utilization, disk I/O, network traffic, and response times and establishes baselines and uses ML algo¬rithms to detect anomalies or deviations from normal behavior. The AI/ML module analyzes the historical data and pattern to predict the failures or performance degrada¬tion in database nodes by identifying the alarms and warnings depending on the thresh¬old which is previously set by the AI/ML module. Once the failure and/or anomaly is detected, the system correlates multiple data points, system logs, and historical infor¬mation to identify the underlying cause of the problem. This helps in understanding the specific issues or failures that need to be addressed. Once the root cause is determined,
the system triggers one or more actions such as restarting a failed node, reallocating
4

resources, adjusting configuration parameters, or even scaling up or down the database nodes based on the workload. In the event of a node failure, the system ensures seam¬less failover by automatically promoting a standby node or initiating replication mech¬anisms to maintain data availability.
[0016] According to an aspect of the present technology, a processor-implemented method for automated maintenance of databases is provided. The method includes re-ceiving, by a monitoring engine associated with a processor, one or more health and performance metric values from a database node for identifying an abnormal behavior of the database nodes based on the one or more health and performance metric values. The method further includes forecasting, by the AI/ML engine one of a degradation or a failure, in the database nodes based on a set of historical data associated with the health and performance metric values, the received one or more health and performance metric values and correlating at least one of a plurality of data points, system logs, historical information and the received one or more health and performance metric val¬ues. The method further includes triggering, by a database (DB) manager engine asso¬ciated with the AI/ML engine, the database node to perform at least one of restarting, scaling, or restoring the database node based on the forecasting and initiating replica¬tion of the database nodes based on the degradation or the failure forecasted. [0017] According to one embodiment of the present technology, the database node stores data associated with at least one of a CPU usage, a memory utilization, a disk input/output (I/O), a network traffic, and a response time and an establish baseline. The data is an operational data associated with the database node (110).
[0018] According to one embodiment of the present technology, for scaling the da-
tabase nodes, the DB manager engine performs at least one of scale up or scale down
of one or more specifications or configurations associated with the database nodes.
[0019] According to one embodiment of the present technology, restoring the data-
base node includes restoring the database node to a previous back-up point.
[0020] According to one embodiment of the present technology, the method further
includes ensuring seamless failover by automatically promoting at least one of a
5

standby node or initiating replication mechanisms to maintain data availability in an event of a node failure.
[0021] The method further includes raising, by the DB manager engine, an alarm indi-cating the forecasted failure or the forecasted degradation in performance to the moni¬toring engine. The method further includes clearing, by the DB manager engine the alarm by pre-emptively triggering the database node to perform at least one of: restart, scale, or restore.
[0022] According to another aspect of the present technology, a system for au-
tomated maintenance of databases is provided. The system includes a processor to fetch and execute computer-readable instructions stored in a memory of the system. The sys¬tem further includes the memory to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium, fetched and exe¬cuted to create or share data packets over a network service. The system further in¬cludes an interface to provide a communication pathway for one or more components of the system. The system further includes a database comprising data either stored or generated as a result of functionalities implemented by the processor. The system fur¬ther includes a monitoring engine to receive one or more health and performance metric values from a database node including a distributed data lake, for determining whether one or more health and performance metric values exceed a corresponding predeter¬mined anomaly threshold for identifying an abnormal behavior of the database nodes based on the one or more health and performance metric values. The system further includes an artificial intelligence/machine learning (AI/ML) engine to forecast degra¬dations in one of performance or failures in the database nodes based on a set of his¬torical data associated with s the health and performance metric values, stored in the database along with the received health and performance metric values and to correlate at least one of a plurality of data points, system logs and historical information. The system further includes a database (DB) manager engine to pre-emptively trigger the database nodes to perform at least one of restart, scale or restore the database node

based on the forecasting and initiate replication of the database nodes based on the degradation or the failure forecasted.
[0023] According to one embodiment of the present technology, the database
node stores data associated with at least one of CPU usage, memory utilization, disk
input/output (I/O), network traffic, and response time, and an established baseline.
[0024] According to one embodiment of the present technology, for scaling the
database nodes, the DB manager engine performs scale-up or scale-down of one or
more specifications or configurations associated with the database nodes.
[0025] According to one embodiment of the present technology, restoring the
database node includes restoring the database node to a previous back-up point. [0026] According to yet another aspect of the present technology, a computer program product comprising a non-transitory computer-readable medium is provided. The non-transitory computer-readable medium comprises instructions that, when executed by one or more processors, cause one or more processors to perform a method. a method for automated maintenance of databases is provided. The method includes receiving, by a monitoring engine associated with a processor, one or more health and perfor¬mance metric values from a database node including a distributed data lake, for deter¬mining whether the one or more health and performance metric values exceed a corre¬sponding predetermined anomaly threshold for identifying an abnormal behavior of the database nodes based on the one or more health and performance metric values. The method further includes monitoring, by an artificial intelligence/machine learning (AI/ML) engine associated with a monitoring engine, the one or more health and per¬formance metric values of the database nodes. The method further includes forecasting, by the AI/ML engine one of a degradation or a failure, in the database nodes based on a set of historical data associated with the health and performance metric values, stored in the database along with the received health and performance metric values and cor¬relating at least one of a plurality of data points, system logs and historical information. The method further includes triggering, by a database (DB) manager engine associated
with the AI/ML engine, the database node to perform at least one of restarting, scaling,
7

or restoring the database node based on the forecasting and initiating replication of the
database nodes based on the degradation or the failure forecasted. The method further
includes raising, by the DB manager engine, an alarm indicating the forecasted failure
or the forecasted degradation in performance to the monitoring engine. The method
further includes clearing, by the DB manager engine the alarm by pre-emptively trig¬
gering the database node to perform at least one of: restart, scale, or restore.
[0027] The various embodiments of the present technology provide a system
and a method that reduces downtime with predictive maintenance. The present tech-nology provides a system and a method that forecasts degradation in database perfor-mance. The present technology provides a system and a method that performs pre-emptive restarts to prevent failure. The present technology provides a system and a method that optimizes performance of the database.
[0028] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments of the present invention that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adapta¬tions and modifications should and are intended to be comprehended within the mean¬ing and range of equivalents of the disclosed embodiments.
BRIEF DESCRIPTION OF THE DRAWINGS
[0029] The accompanying drawings, which are incorporated herein, and
constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed
methods and systems in which like reference numerals refer to the same parts
throughout the different drawings. Components in the drawings are not necessarily to
scale, emphasis instead being placed upon clearly illustrating the principles of the
present disclosure. Some drawings may indicate the components using block diagrams
and may not represent the internal circuitry of each component. It will be appreciated
by those skilled in the art that disclosure of such drawings includes the disclosure of
electrical components, electronic components or circuitry commonly used to
8

implement such components.
[0030] FIG. 1 illustrates an exemplary architecture of a system for automated
maintenance of database nodes, in accordance with embodiments of the present
disclosure.
[0031] FIG. 2 illustrates a block diagram of the system for automated
maintenance of databases, in accordance with embodiments of the present disclosure.
[0032] FIG. 3 illustrates a block diagram depicting the interaction between the
processing engine(s), in accordance with embodiments of the present disclosure.
[0033] FIG. 4 illustrates a sequence diagram for automated maintenance of
database nodes, in accordance with embodiments of the present disclosure.
[0034] FIG. 5 depicts a flowchart of a processor-implemented method for
automated maintenance of databases, in accordance with embodiments of the present
disclosure.
[0035] FIG. 6 illustrates an exemplary computer system in which or with which
embodiments of the present disclosure may be implemented. The foregoing shall be
more apparent from the following more detailed description of the disclosure.
LIST OF REFERENCE NUMERALS
100- Network Architecture
102- User
104 – User Equipment
106 – Network
108 – System
110- Database Nodes
202- Processor
204- Memory
206- Interface
208- Processing engines
210- Database

212- Monitoring Engine
214-Artificial intelligence (AI) engine
216- Database (DB) manager engine
218- Other engines
600- Computer system
610- External storage device
620- Bus
630- Main memory
640- Read only memory
650- Mass Storage Device
660- Communication Port
670- Computer System Processor
DETAILED DESCRIPTION OF THE EMBODIMENTS [0036] 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 all of the problems discussed above or might address only some of the problems discussed above. Some of the prob¬lems discussed above might not be fully addressed by any of the features described herein.
[0037] The ensuing description provides exemplary embodiments only, and is not in-tended 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 ele¬ments without departing from the spirit and scope of the disclosure as set forth.

[0038] 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.
[0039] 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 dia¬gram, or a block diagram. Although a flowchart may describe the operations as a se¬quential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can corre¬spond to a return of the function to the calling function or the main function. [0040] 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 de¬scribed herein as “exemplary” and/or “demonstrative” is not necessarily to be con¬strued as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising” as an open transition word without precluding any additional or other elements. [0041] Reference throughout this specification to “one embodiment” or “an embodi¬ment” or “an instance” or “one instance” means that a particular feature, structure, or
11

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.
[0042] The terminology used herein is for the purpose of describing particular embod-iments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.
[0043] The present disclosure relates to a system and a method for automated mainte-nance of database nodes. The system receives one or more health and performance metric values from each database node from a set of database nodes. The one or more health and performance metrics of a database node may refer to overall operational status, encompassing performance, reliability, integrity, and security. Some metrics that may be used to assess the health include but are not limited to performance metrics, resource utilization, storage and capacity, error and stability, data integrity, and secu¬rity. The performance metrics may include data throughput, input/output latency, speed of read/write operations, etc. Resource utilization may refer to a usage of re¬source allocated to the database node including central processing unit (CPU) usage, a memory utilization, a disk input/output (I/O), etc. Storage and capacity may refer to memory capacity and the amount of data stored. Error and stability may indicate errors

in database operations due to interruption, lack of resources, etc., and stability demon¬strated by the database node during constraints. Data integrity may refer to data accu¬racy. Security may refer to safety of the data from corruptions, etc.
[0044] The system determines whether the one or more health and performance metric values exceed a corresponding predetermined anomaly threshold. For example, the sys¬tem may determine that the database node response speed is slower than a desired threshold speed, leading to identification of abnormal behavior. The system identifies any abnormal behaviour of the database nodes and forecasts degradation in perfor-mance or failures in the database nodes based on a set of historical data of the health and performance metric values along with the received health and performance metric values. The system pre-emptively triggers the database nodes to restart, scale or restore the database node based on the forecasting. Additionally, present technology provides a solution for problem for lack of auto-healing capabilities for database nodes in the telecom domain that can lead to prolonged downtime, delayed issue resolution, in¬creased operational burden, lower system resilience, potential data loss, inefficient re¬source utilization, and scalability challenges. Moreover, present technology provides implementing auto healing mechanisms that can significantly mitigate these issues, im¬prove service availability, and enhance the overall reliability of the telecom infrastruc¬ture. Data Definition Language (DDL) have the capability to auto heal the database nodes using AI/ML techniques to automatically detect, diagnose, and resolve issues or failures in the database nodes without manual intervention. This approach helps ensure high availability and reliability of the database system. The “Data Definition Language (DDL)” used hereinafter in the specification refers to combining the data dictionary updates, storage engine operations, and binary log writes associated with a DDL oper¬ation into a single, operation.
[0045] The expression “health and performance metrics values” used hereinafter in the specification refers to metrics such as availability, DB response, indexing, capacity, sessions, and performance can be used to determine the health of a database system.

These parameters can be used to identify any shortcomings that may directly or indi-rectly impact your database infrastructure.
[0046] The various embodiments throughout the disclosure will be explained in more detail with reference to FIGs. 1-6.
[0047] Referring to FIG. 1, the network architecture (100) may include one or more computing devices or user equipments (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 user equipments (104-1, 104-2…104-N) may be individually referred to as the user equipment (104) and collectively referred to as the user equipment (104). A person of ordinary skill in the art will appreciate that the terms “computing device(s)” and “user equipment” may be used interchangeably throughout the disclosure. Although three user equipments (104) are depicted in FIG. 1, however any number of the user equipments (104) may be included without departing from the scope of the ongoing description. The architec¬ture (100) may include one or more database nodes (110), such as database node 1 (110-1) and database node 2 (110-2). The database nodes (110) may include, but not be limited to, any relational databases, NoSQL databases, distributed databases, cloud-based databases, and the like. Each of the database nodes (110) may have hardware and software specifications and configurations associated thereto.
[0048] In an embodiment, the user equipment (104) may include smart devices oper-ating in a smart environment, for example, an Internet of Things (IoT) system. In such an embodiment, the user equipment (104) may include, but is not limited to, smart phones, smart watches, smart sensors (e.g., mechanical, thermal, electrical, magnetic, etc.), networked appliances, networked peripheral devices, networked lighting system, communication devices, networked vehicle accessories, networked vehicular devices, smart accessories, tablets, smart television (TV), computers, smart security system,
smart home system, other devices for monitoring or interacting with or for the users
14

(102) and/or entities, or any combination thereof. A person of ordinary skill in the art
will appreciate that the user equipment (104) may include, but is not limited to, intelli¬
gent, multi-sensing, network-connected devices, that can integrate seamlessly with
each other and/or with a central server or a cloud-computing system or any other device
5 that is network-connected.
[0049] In an embodiment, the user equipment (104) may include, but is not limited to,
a handheld wireless communication device (e.g., a mobile phone, a smart phone, a pha-
blet device, and so on), a wearable computer device(e.g., a head-mounted display com¬
puter device, a head-mounted camera device, a wristwatch computer device, and so
10 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 sys¬
tem, and/or any other type of computer device with wireless communication capabili¬
ties, and the like. In an embodiment, the user equipment (104) may include, but is not
limited to, any electrical, electronic, electro-mechanical, or an equipment, or a combi-
15 nation of one or more of the above devices such as virtual reality (VR) devices, aug¬
mented reality (AR) devices, laptop, a general-purpose computer, desktop, personal
digital assistant, tablet computer, mainframe computer, or any other computing device,
wherein the user equipment (104) may include one or more in-built or externally cou¬
pled accessories including, but not limited to, a visual aid device such as a camera, an
20 audio aid, a microphone, a keyboard, and input devices for receiving input from the
user (102) or the entity such as touch pad, touch enabled screen, electronic pen, and the
like. A person of ordinary skill in the art will appreciate that the user equipment (104)
may not be restricted to the mentioned devices and various other devices may be used.
[0050] Referring to FIG. 1, the user equipment (104) may communicate with a system
25 (108), through a network (106). In an embodiment, the network (106) may include at
least one of a Fifth Generation (5G) network, 6G network, or the like. The network
(106) may enable the user equipment (104) to communicate with other devices in the
network architecture (100) and/or with the system (108). The network (106) may in-
15

clude a wireless card or some other transceiver connection to facilitate this communi¬
cation. 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
5 Private Network (VPN), the Internet, the Public Switched Telephone Network (PSTN),
or the like.
[0051] In another exemplary embodiment, the system (108) may include or comprise,
by way of example but not limitation, one or more of: a stand-alone server, a server
blade, a server rack, a bank of servers, a server farm, hardware supporting a part of a
10 cloud service or system, a home server, hardware running a virtualized server, one or
more processors executing code to function as a server, one or more machines perform¬ing server-side functionality as described herein, at least a portion of any of the above, some combination thereof.
[0052] In accordance with embodiments of the present disclosure, the system 108 may
15 be designed and configured for automatically maintain the database nodes. In an em-
bodiment, the system 108 may be configured to forecast degradation in performance or failures, and pre-emptively trigger a restart, scaling or restoration of the database nodes.
[0053] FIG. 2 illustrates a block diagram (200) of a system (108) for automated mainte-
20 nance of databases, in accordance with embodiments of the present disclosure.
[0054] In an aspect, 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, edge or fog microcontrollers, digital signal proces¬
sors, central processing units, logic circuitries, and/or any devices that process data
25 based on operational instructions. Among other capabilities, the one or more proces-
sor(s) (202) may be configured to fetch and execute computer-readable instructions stored in the 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 com¬puter-readable storage medium, which may be fetched and executed to create or share
16

data packets over a network service. The memory (204) may comprise any non-transi¬
tory 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.
5 [0055] Referring to FIG. 2, the system (108) may include an interface(s) (206). The
interface(s) (206) may include a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The interface(s) (206) may facilitate communication to/from the system (108). The inter-face(s) (206) may also provide a communication pathway for one or more components
10 of the system (108). Examples of such components include, but are not limited to, pro-
cessing unit/engine(s) (208) and a database (210).
[0056] In an embodiment, the processing unit/engine(s) (208) may be implemented as a combination of hardware and programming (for example, programmable instruc-tions) to implement one or more functionalities of the processing engine(s) (208). In
15 examples described herein, such combinations of hardware and programming may be
implemented in several different ways. For example, the programming for the pro-cessing engine(s) (208) may be processor-executable instructions stored on a non-tran-sitory 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
20 execute such instructions. In the present examples, the machine-readable storage me-
dium may store instructions that, when executed by the processing resource, implement the processing engine(s) (208). In such examples, the system (108) may include 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
25 but accessible to the system (108) and the processing resource. In other examples, the
processing engine(s) (208) may be implemented by electronic circuitry.
[0057] 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)
17

may be separate from the system (108). In an embodiment, the database (210) may be
indicative of including, but not limited to, a relational database, a distributed database,
a cloud-based database, or the like. The database node (110) stores data associated
with at least one of: a CPU usage, a memory utilization, a disk input/output (I/O), a
5 network traffic, and a response time and an establish baseline.
[0058] In an exemplary embodiment, the processing engine (208) may include one or
more engines selected from any of a monitoring engine (212), an artificial intelligence
(AI) engine (214), database (DB) manager engine (216) and other engines (218) having
functions that may include, but are not limited to, testing, storage, and peripheral func-
10 tions, such as wireless communication unit for remote operation, audio unit for alerts
and the like.
[0059] In an embodiment, the monitoring engine (212) may be configured to receive
one or more health and performance metric values from a database node (110) for iden¬
tifying an abnormal behavior of the database nodes (110) based on the one or more
15 health and performance metric values. The monitoring engine (212) determines
whether one or more health and performance metric values exceed a corresponding
predetermined anomaly threshold for identifying the abnormal behaviour. The abnor¬
mal behavior of a database refers to any deviation from the expected performance, sta¬
bility, or security of the database. In examples, the abnormal behavior includes perfor-
20 mance degradation, high resource utilization, errors and failures, data integrity issues,
security breaches, unexpected growth in data volume, and high availability issues, all
of which can be measured using health and performance metrics. In an embodiment,
the artificial intelligence (AI) engine (214) may forecast one of: performance degrada¬
tion or failures in the database nodes (110) based on a set of historical data associated
25 with the health and performance metric values, stored in the database (210) along with
the received health and performance metric values and correlation of at least one of: a
plurality of data points, system logs and historical information. The historical data may
be stored in the database (210).
18

[0060] The forecasting is described herein. In examples, the AI engine (214) may col¬
lect historical data associated with the health and performance metric values including
the CPU usage, memory usage, disk I/O, network I/O, query execution times, error
rates, and replication lag. In some examples, there may be historical system logs, cap-
5 tured events, warnings, and errors from the database nodes on abnormal operations.
The AI engine (214) may also store records of past incidents of performance degrada¬
tion or failures, including their resolution. Further, the AI engine (214) may continu¬
ously monitor and collect current health and performance metrics from the database
nodes. The AI engine (214) may store these real-time metrics in a database for real-
10 time analysis and forecasting. The AI engine (214) may process the historical data to
identify and handle missing or outliers in the data by inputting or removing missing
values to ensure data completeness. Further, the AI engine (214) may create new fea¬
tures such as moving averages, rolling windows, variances, and lagged values of key
metrics. The AI engine (214) may normalize or standardize the data for consistent scal-
15 ing across different metrics. To perform correlation, the AI engine (214) may perform
correlation analysis to identify relationships between various health and performance
metrics and degradation and failures. For example, the AI engine (214) may identify
that when the resources associated with database node are overused, the data may be
lost due to slowing operation. The AI engine (214) may identify and correlate the plu-
20 rality of data points, the plurality of system logs, historical information and the received
one or more health and performance metric values. For examples, the AI engine (214)
may process system logs to identify any events affecting the database node, similar
events in historical information, values indicative of events in the received one or more
health and performance and any indications or data points indicating degradation or
25 failure of the network node. In examples, the AI engine (214) may use statistical meth¬
ods to quantify strength of these relationships. The AI engine (214) may analyze sys¬
tem logs to extract patterns and events that precede performance degradation or failures
including from historical degradation and failure events, etc. Further, the AI engine
19

(214) may correlate log patterns with performance metrics to identify leading indicators of potential issues.
[0061] In examples, the system (108) may choose appropriate machine learning (ML) models for forecasting, such as time series models, regression models, or classification models. One or more ML modes are trained on historical data, using both health/per-formance metrics and system logs as input features. The data may be split into training and validation sets to evaluate model performance. In implementations, the system (108) may mark importance of different features in predicting performance degradation or failures. Further, real-time health and performance metrics are continuously fed to the trained models. A real-time data pipeline may be implemented to ensure timely data ingestion and processing. The system (108) may deploy anomaly detection mod¬els to identify deviations from normal behavior that could indicate imminent degrada¬tion or failure. The system (108) may generate forecasts for performance degradation or failure probabilities based on real-time data. In some aspects, the system (108) may set thresholds for alerting and notify administrators when the model predicts potential issues. The system (108) may continuously validate model predictions against actual outcomes to assess accuracy. The system (108) may update models periodically with new data to improve performance and adapt to changing patterns. The system (108) may implement a feedback loop where predictions and actual outcomes are logged for continuous improvement. The feedback is used to refine feature engineering, model parameters, and threshold settings. The system (108) may deploy one or more appro¬priate trained ML models as the AI engine (214).
[0062] In an embodiment, the database (DB) manager engine (216) may pre-emptively trigger the database nodes (110) to restart, scale or restore the database node based on the forecasting. In an embodiment, for scaling the database nodes (110), the database (DB) manager engine (216) may scale up or scale down the specifications or configu¬rations associated with the database nodes (110). In an embodiment, restoring the da¬tabase node may include restoring the database node to a previous back-up point. The

DB manager engine (216) performs scale-up or scale-down of one or more specifica-tions or configurations associated with the database nodes (110).
[0063] In an embodiment, the DB manager engine (216) triggers the database node (110) to perform at least one of: restart, scale or restore the database node based on the forecasting and initiating a replication of the database nodes (110) based on the degra¬dation or the failure forecasted. The DB manager engine (216) raises an alarm indicat¬ing the forecasted failure or the forecasted degradation in performance. The DB man¬ager engine (216) clears the alarm by pre-emptively triggering the database node (110) to perform at least one of: restart, scale, or re-store.
[0064] In an embodiment, the database (DB) manager engine (216) may initiate repli-cation of the database nodes based on the degradation or failure forecasted. In the event of a node failure, the database (DB) manager engine (216) may ensure seamless failo-ver by automatically promoting a standby node or initiating replication mechanisms to maintain data availability.
[0065] FIG. 3 illustrates a block diagram (300) for interaction between the processing engine(s) (208), in accordance with embodiments of the present disclosure. [0066] In an embodiment, the AI engine (214) may be configured to use one or more health and performance metric values as input features and output forecasts of degra¬dations in performance or failures in the database nodes (210). The AI engine (214) may be indicative of a pre-trained machine learning model, expert systems or the like, by not limited thereto, that has been trained with one or more health and performance metric values to forecast degradations in performance or failures of the database nodes (210). If the AI engine (214) predicts degradation in performance or failures in the database nodes (110), the AI engine (214) may cause the DB manager engine (216) to restart or restore the database nodes (210). The DB manager engine (216) may also transmit the forecasts to the monitoring engine (212). In an embodiment, the DB man¬ager engine (216) may be associated with a user interface where visualizations of one or more health and performance metric values may be presented. In an embodiment, the DB manager engine (216) may raise alarms, via transmission of a set of signals, in

the user interface to indicate need for manual or automated intervention for restarting, scaling or restoration of the database nodes (210). In examples, the DB manager engine (216) may raise alarms based on thresholds set by AI engine (214). For example, the AI engine (214) may have set threshold resource usage, for example of CPU threshold to be 80%. If the database node is consuming CPU resources amounting to 80% and above, indicating abnormal resource usage, the DB manager engine (216) may raise an alarm. This will help in auto intervention to pause the database node to prevent degra¬dation or failure.
[0067] FIG. 4 illustrates a sequence diagram (400) for automated maintenance of da-tabase nodes (110), in accordance with embodiments of the present disclosure. [0068] In an embodiment, the method may include receiving, by a processor such as the processor (202) of FIG. 2, one or more health and performance metric values from each database node from a set of database nodes, such as the set of database nodes of FIG. 1. In an embodiment, the one or more health and performance data may include, but not be limited to, CPU usage, memory utilization, disk I/O, network traffic, and response times and establishes baselines and the like.
[0069] The method includes determining, by the processor, whether the one or more health and performance metric values exceed a corresponding predetermined anomaly threshold. In an embodiment, method may identify any abnormal behaviour of the da-tabase nodes. The method includes monitoring (402) database for health and perfor-mance metrics values.
[0070] The method includes forecasting, by the processor, degradation or failures in the database nodes based on a set of historical data (404) of the health and performance metric values along with the received health and performance metric values. The method includes determining, by the processor, source of sub-optimal performance or failure of the database nodes. The method further includes correlating (406) multiple data points, system logs and historical information.
[0071] The method includes pre-emptively triggering (408), by the processor, the da-tabase nodes to restart, scale or restore the database node (414) comprising distributed

data lake (410) based on the forecasting. In an embodiment, for scaling the database node, the method includes scaling up or scaling down the specifications or configura-tions associated with the database nodes. In an embodiment, restoring the database node may include restoring the database node to a previous back-up point. [0072] The method may also include initiating, by the processor, replication of the da¬tabase nodes based on the degradation or failure forecasted. In the event of a node failure, the system ensures seamless failover by automatically promoting a standby node or initiating replication mechanisms to maintain data availability.
[0073] The method may include raising (412), by the processor, an alarm indicating the forecasted failure or degradation in performance via a UE, such as the UE (104) of FIG. 1. The method may include clearing (416), by the processor, the alarm on pre-emptively triggering the database node to restart, scale or restore (418).
[0074] FIG. 5 depicts a flowchart of a processor-implemented method (500) for auto-mated maintenance of databases, in accordance with embodiments of the present dis-closure. At step 502, one or more health and performance metric values is received, by a monitoring engine associated with a processor, from a database node for identifying an abnormal behavior of the database nodes based on the one or more health and per-formance metric values. The monitoring engine determines whether the one or more health and performance metric values exceed a corresponding predetermined anomaly threshold indicative of an abnormal behavior of the database nodes. At step 504, the one or more health and performance metric values of the database nodes is monitored, by an artificial intelligence/machine learning (AI/ML) engine associated with a moni-toring engine. At step 506, one of a degradation or a failure, is forecasted, by the AI/ML engine, in the database nodes based on a set of historical data associated with the health and performance metric values, stored in the database along with the received health and performance metric values and at least one of a plurality of data points, system logs and historical information are correlated. At step 508, the database node to perform at least one of: restart, scale or restore the database node is triggered, by a database (DB)

manager engine associated with the AI/ML engine, based on the forecasting and initi-ating a replication of the database nodes based on the degradation or the failure fore-casted. At step 510, an alarm indicating the forecasted failure or the forecasted degra-dation in performance is raised, by the DB manager engine, to the monitoring engine. At step 512, the alarm is cleared, by the DB manager engine by pre-emptively trigger-ing the database node to perform at least one of: restart, scale, or restore.
[0075] According to one embodiment of the present technology, the database node stores data associated with at least one of a CPU usage, a memory utilization, a disk input/output (I/O), a network traffic, and a response time and an establish baseline. [0076] According to one embodiment of the present technology, for scaling the data-base nodes, the DB manager engine performs at least one of scale-up or scale-down of one or more specifications or configurations associated with the database nodes. [0077] According to one embodiment of the present technology, restoring the database node includes restoring the database node to a previous back-up point.
[0078] According to one embodiment of the present technology, the method further includes ensuring seamless failover by automatically promoting at least one of a standby node or initiating replication mechanisms to maintain data availability in an event of a node failure.
[0079] FIG. 6 illustrates an exemplary computer system (600) in which or with which embodiments of the present disclosure may be implemented. As shown in FIG. 5, 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), a com-munication port (660), and a processor (670). A person skilled in the art will appreciate that the computer system (600) may include more than one processor (670) and com¬munication ports (660). Processor (670) may include various modules associated with embodiments of the present disclosure.
[0080] In an embodiment, the communication port (660) may be any of an RS-232 port for use with a modem-based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or

future ports. The communication port (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 (600) connects.
[0081] In an embodiment, the memory (630) may be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. Read-only memory (640) may be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or Basic In¬put/Output System (BIOS) instructions for the processor (670).
[0082] In an embodiment, the mass storage (650) may be any current or future mass storage solution, which may be used to store information and/or instructions. Exem-plary mass storage solutions include, but are not limited to, Parallel Advanced Tech-nology 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 In¬dependent Disks (RAID) storage, e.g., an array of disks (e.g., SATA arrays). [0083] In an embodiment, the bus (620) communicatively couples the processor(s) (670) with the other memory, storage and communication blocks. The bus (620) may be, e.g., a Peripheral Component Interconnect (PCI)/PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), Universal Serial Bus (USB) or the like, for con-necting 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 (600). [0084] 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 (600). Other operator and admin¬istrative interfaces may be provided through network connections connected through the communication port (660). Components described above are meant only to exem¬plify various possibilities. In no way should the aforementioned exemplary computer system (600) limit the scope of the present disclosure.

TECHNICAL ADVANCEMENTS
The present disclosure described herein above has several technical advantages includ¬ing, but not limited to, the realization of the system and the method that:
1. to provide a system and a method for automated maintenance of databases.
2. to provide a system and a method that reduces downtime with predictive mainte-nance.
3. to provide a system and a method that forecasts degradation in database performance.
4. to provide a system and a method that performs pre-emptive restarts to prevent fail-ure.
5. to provide a system and a method that optimizes performance of the database.
[0085] While considerable emphasis has been placed herein on the preferred embodi-ments, 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 princi-ples of the disclosure. These and other changes in the preferred embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the disclosure and not as limitation.

WE CLAIM:
1. A processor-implemented method (500) for automated maintenance of a data¬
base, the method comprising:
receiving (502), by a monitoring engine (212) associated with a processor (202), one or more health and performance metric values from a database node (110) for iden¬tifying an abnormal behavior of database nodes (110) based on the one or more health and performance metric values;
forecasting (506), by the AI/ML engine (214) one of: a degradation or a failure, in the database nodes (110) based on a set of historical data associated with the health and performance metric values, the received one or more health and performance met¬ric values and correlating at least one of: a plurality of data points, a plurality of system logs, historical information and the received one or more health and performance met¬ric values; and
triggering (508), by a database (DB) manager engine (216) associated with the AI/ML engine (214), the database node (110) to perform at least one of: restart, scale or restore the database node based on the forecasting and initiating a replication of the database nodes (110) based on the forecasted degradation or the failure.
2. The processor-implemented method (500) as claimed in claim 1, wherein the
database node (110) stores data associated with at least one of: a central processing unit
(CPU) usage, a memory utilization, a disk input/output (I/O), a network traffic, and a
response time and an establish baseline.

3. The processor-implemented method (500) as claimed in claim 2, wherein the data is an operational data associated with the database node (110).
4. The processor-implemented method (500) as claimed in claim 1, wherein for scaling the database nodes (110), the DB manager engine (216) performs at least one of: scale up or scale down of one or more specifications or configurations associated with the database nodes (110).
5. The processor-implemented method (500) as claimed in claim 1, wherein re-storing the database node (110) comprises:
restoring the database node (110) to a previous back-up point.
6. The processor-implemented method (500) as claimed in claim 1, further com¬
prising:
ensuring seamless failover by automatically promoting at least one of: a standby node or initiating replication mechanisms to maintain data availability in an event of a node failure.
7. The processor-implemented method (500) as claimed in claim 1, further com¬
prising:
raising (510), by the DB manager engine (216), an alarm indicating the fore-casted degradation or the failure in performance to the monitoring engine (212); and
clearing (512), by the DB manager engine (216), the alarm by pre-emptively triggering the database node (110) to perform at least one of: restart, scale, or restore.
8. A system (108) for automated maintenance of databases, the system compris¬
ing:

a processor (202) to fetch and execute computer-readable instructions stored in a memory (204) of the system;
a memory (204) to store one or more computer-readable instructions or routines in a non-transitory computer readable storage medium, fetched and executed to create or share data packets over a network service;
an interface (206) to provide a communication pathway for one or more com-ponents of the system;
a database (210) comprising data either stored or generated as a result of func-tionalities implemented by the processor (202);
a monitoring engine (212) to receive one or more health and performance met-ric values from a database node (110) for identifying an abnormal behavior of the da-tabase node (110) based on the one or more health and performance metric values;
an artificial intelligence/machine learning (AI/ML) engine (214) to forecast a degradation in one of: performance or failure in the database nodes (110) based on a set of historical data associated with the health and performance metric values, the re-ceived one or more health and performance metric values and to correlate at least one of: a plurality of data points, system logs historical information and the received one or more health and performance metric values; and
a database (DB) manager engine (216) to trigger the database nodes (110) to perform at least one of: restart, scale, or restore the database node (110) based on the forecasting and initiate replication of the database nodes (110) based on the degradation or the failure forecasted.
9. The system (108) as claimed in claim 8, wherein the database node (110) stores data associated with at least one of: a CPU usage, a memory utilization, a disk in¬put/output (I/O), a network traffic, and a response time and an establish baseline.
10. The system (108) as claimed in claim 9, wherein the data is an operational data associated with the database node (110).

11. The system (108) as claimed in claim 8, wherein for scaling the database nodes (110), the DB manager engine (216) performs scale up or scale down of one or more specifications or configurations associated with the database nodes (110).
12. The system (108) as claimed in claim 8, wherein restoring the database node (110) comprises restoring the database node (110) to a previous back-up point.
13. A user equipment (UE) (104) communicatively coupled with a network (106), the coupling comprises steps of:
receiving, by the network (106), a connection request from the UE (104);
sending, by the network (106), an acknowledgment of the connection request to the UE (104); and
transmitting a plurality of signals in response to the connection request, wherein the network (106) is configured to perform a processor-implemented method (500) for automated maintenance of a database as claimed in claim 1.