Enhanced Real-Time Monitoring and Visualization System for Efﬁcient

Data Analysis: A Case Study on Public Distribution System Data in

Assam, India

Narottam Bhattacharjee

1 a

, Abhijit Gogoi

2 b

and Sanghamitra Nath

1 c

Department of Computer Science and Engineering, Tezpur University, Napaam, Assam, India

Department of Computer Science and Engineering, Dibrugarh University, Assam, India

Keywords:

RDBMS, Big Data, Real-Time, Visualization, Elasticsearch, Kibana, Flask, PostgreSQL.

Abstract:

Modern software produces large amounts of log ﬁles or data. The exponential growth of data in modern soft-

ware poses signiﬁcant challenges for organizations in efﬁciently managing and extracting insights from this

vast amount of information. This paper presents a user-friendly monitoring system that integrates Elastic-

search’s search engine with Kibana’s visualization tools, aimed at simplifying real-time data management. By

streamlining processes for retrieval, monitoring, searching, analysis, and visualization, the system addresses

the challenges associated with handling large volumes of data. Through testing with data from Assam’s Public

Distribution System (PDS), the research demonstrates the system’s efﬁcacy in creating an efﬁcient solution

for monitoring and analyzing PDS data. The strategic alignment of this system underscores its potential to

signiﬁcantly enhance the effectiveness of data management in critical domains such as public distribution.

This study contributes to advancing the understanding and implementation of streamlined data management

solutions in complex operational environments.

1 INTRODUCTION

Managing the rapid increase in log ﬁles from mod-

ern applications poses difﬁculties in real-time data ac-

cess and search, with high usage leading to potential

system failures. Handling substantial real-time data

is challenging for Relational Database Management

System (RDBMS). Addressing these issues requires

complex infrastructures, relying on specialized mon-

itoring systems like DevOps and big data manage-

ment tools. However, continuous human monitoring

of these systems proves to be a challenging task. In

their study, Konig et al.(K

onig and Steffens, 2018) ex-

plored how integrating machine learning techniques

can signiﬁcantly enhance monitoring systems. De-

spite the complexity involved in building such sys-

tems, they opted for Elasticsearch as the foundational

big data technology for their proposed DevOps frame-

work.

Elasticsearch (Shukla and Kumar, 2019) serves

as an open-source distributed analytics engine, capa-

https://orcid.org/0009-0004-7167-9804

https://orcid.org/0009-0000-4849-8264

https://orcid.org/0000-0001-7516-5510

ble of handling a wide range of data types, including

text, numerical, geospatial, unstructured, and struc-

tured data. Some examples of big data are transporta-

tion, government, media, entertainment, and advertis-

ing and marketing data. Ration Card Data

in Public

Distribution System (PDS) is one of them.

In June 1997, India’s government introduced the

Targeted Public Distribution System (TPDS) with a

primary emphasis on supporting the economically

disadvantaged. Under the TPDS, countries are re-

quired to develop and implement a comprehensive ev-

idence based system for grain delivery through the

identiﬁcation of the poor and transparent and ac-

countable distribution system through the Fair Price

Shop (FPS). The Targeted Public Distribution Sys-

tem (TPDS) operates collaboratively with both the

central and state governments sharing responsibilities.

The state is tasked with identifying families below the

poverty line, issuing ration cards, overseeing and reg-

ulating the operation of the FPS, and managing inter-

actions with the state government.

Since December 2015, Assam has implemented

The ration card data set. (2022) [Online]. Available:

https://pds.assam.gov.in/ , 2022

Bhattacharjee, N., Gogoi, A. and Nath, S.

Enhanced Real-Time Monitoring and Visualization System for Efﬁcient Data Analysis: A Case Study on Public Distribution System Data in Assam, India.

DOI: 10.5220/0013304500004646

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Cognitive & Cloud Computing (IC3Com 2024), pages 5-12

ISBN: 978-989-758-739-9

the National Food Security Act (NFSA’13), offering

highly subsidized food grains to 2.52 crore individu-

als in accordance with Article 13 of the NFSA. Efﬁ-

cient monitoring and quick searching of ration card-

related information have become essential, necessitat-

ing the adoption of new technology for real-time data

retrieval. Elasticsearch, coupled with Kibana

as its

monitoring and visualization tool, emerges as a suit-

able solution, enabling faster and optimized perfor-

mance compared to traditional RDBMS systems.

The study aims to evaluate and analyze the real-

time performance of Elasticsearch compared to an

RDBMS system. The development of a web-based

application facilitates the easy retrieval of PDS-

related ration cards through Elasticsearch indexing.

Additionally, a dynamic dashboard incorporating Key

Performance Indicator (KPI)-based data visualization

using Kibana has been created. The proposed sys-

tem’s objectives include systematically recording and

maintaining ration cardholder details for Assam on

a uniﬁed website. Furthermore, the system aims to

expedite the generation of detailed reports for ration

cardholders whenever required.

2 RELATED WORK

Big data analysis serves various objectives, includ-

ing the classiﬁcation of conversation topics to iden-

tify trending discussions. Given that the main aim of

this research is to devise a text search and big data

analysis system within the DevOps framework, it is

crucial to establish a clear deﬁnition. Wahaballa et

al.(Wahaballa et al., 2015) described this approach

as a software development methodology designed to

enhance communication, collaboration, and integra-

tion between development and operations teams, aim-

ing to address critical challenges such as the fear of

change and the risks associated with development.

Historically, the separation between teams dedi-

cated to swift feature delivery and those responsible

for ensuring system stability has led to discord and

decreased productivity. DevOps emerged to address

conﬂicts, offering swift and continuous assessment

through the facilitation and integration of collabora-

tion among development, operations, and teams re-

sponsible for quality assurance. But building, log-

ging, and monitoring tools are essential to automating

the DevOps process. Akshaya et al.(Akshaya et al.,

2015) introduces several monitoring tools commonly

Getting Started with the elastic stack

technology (2022) [Online]. Available

: https://www.elastic.co/guide/en/elastic-

stack/7.17/installing-elastic-stack.html

employed in DevOps practices. Some of them are

brieﬂy described as follows.

• Splunk serves as a tool for analyzing and storing

big data, providing support for numerous related

applications and plugins.

• Loggly offers logging and log data management

through its cloud-based software-as-a-service

(SaaS) solutions. Unlike other similar tools fo-

cused on identifying system issues, Loggly em-

phasizes pinpointing the source of the problem.

• Graphite proves to be a scalable solution for stor-

ing data, generating graphs from time-lapse data,

and real-time monitoring of performance parame-

ters.

• Sumo Logic stands out as a cloud-based SaaS

security platform designed to process logs and

derive real-time insights, proving valuable for

promptly analyzing and addressing challenges

within the infrastructure.

• Cacti is designed as an application capable of stor-

ing system data and presenting real-time graphics

derived from it.

• Logstash, a component of the Elastic Stack, excels

in processing extensive volumes of log ﬁles and

extracting valuable insights from them.

• Kibana, an integral component within the Elas-

tic ecosystem, provides interactive visualization

of data on dashboards that users can customize ac-

cording to their needs.

Bangnasco et al.(Bagnasco et al., 2015) in their

work, describes ELK stack, comprised of Elastic-

search, Logstash, and Kibana, as a monitoring system

utilized for Infrastructure as a Service (IaaS) and sci-

entiﬁc applications in the cloud, leveraging the Elas-

ticsearch ecosystem. The monitoring system is used

to display data from a cloud-based system and store

it in a SQL database, then feed it to an Elasticsearch

node via the Logstash plugin. Each application pos-

sesses its unique Kibana dashboard for monitoring

and visualization. Nevertheless, the success of the

elastic stack-based solution prompted the team to con-

template the elimination of the SQL middleware layer

from the system. Zamﬁr et al.(Zamﬁr et al., 2019) in

their paper described the development of an Elastic-

search monitoring system that uses machine learning

techniques to effectively identify and stop errors at

scale. Takase et al. (Takase et al., 2017) in their work,

present a solution based on the strategy used by the

CERN cloud group. This solution comprises devel-

oping an Elasticsearch plugin to control data access

based on user identity, allowing individual users to

see personalized Kibana dashboards for monitoring

IC3Com 2024 - International Conference on Cognitive & Cloud Computing

cloud usage. Hamilton et al.(Hamilton et al., 2018)

in their paper, elaborate on a SCADA (Supervisory

Control and Data Acquisition) statistics monitoring

system developed at CERN, employing the Elastic

Stack. Shah et al.(Shah et al., 2022) in their paper,

present a solution based on Elasticsearch, which in-

cludes a customized search engine, distributed struc-

ture, pre-deﬁned indexing, and a standardized frame-

work designed for broad text mining. Modern in-

dustrial gear generates large amounts of log data, but

manually examining it takes a long time. Nezhmetdi-

nov et al.(Nezhmetdinov et al., 2023) provide an in-

formation system that uses the ELK software stack

to automate log ﬁle analysis for industrial equipment.

Calderon et al. (Calderon et al., 2023) in their study,

demonstrate the implementation and performance as-

sessment of an Internet of Things (IoT) platform

that integrates Apache Kafka with Elastic Stack tools

(Elasticsearch, Kibana, and Beats). The Elasticsearch

cluster for data storage and search, the Edge Nodes

for communication with IoT devices and networks,

the Data Streaming module with Apache Kafka, the

Cloud Server for device interaction and data storage

in Elasticsearch, and the Visualization module with

Kibana comprise the architecture of the IoT platform.

Karmalkar et al.(Karmalkar et al., 2021) in their work

developed a web application that serves as a data an-

alytics based search engine for Twitter data based on

Elastic search. Tscharf et al.(Walter-Tscharf, 2022)

in their work describe a system design for indexing,

clustering, and searching scientiﬁc text using Elastic-

search and Kibana. Bhosale et al.(Bhopale and Ti-

wari, 2024) in their paper mentioned the Elasticsearch

engine as an efﬁcient document matching and re-

trieval mechanism for their transformation-based in-

formation retrieval system. Famous companies like

Facebook, Uber, Udemy and Netﬂix have used Elas-

tic Stack as a monitoring tool. CERN also uses Elastic

Stack as a control solution for more than 210 control

applications.

This study explores challenges associated with

handling and analyzing vast real-time data, highlight-

ing the expertise required in speciﬁc database and vi-

sualization tools. We introduce a simple monitoring

system that integrates Elasticsearch and Kibana fea-

tures. Our method demonstrates superior text search

performance compared to current approaches. The re-

maining sections of this document are organised as

follows: Section 3 provides a brief review of Elastic-

search and Kibana concepts. In Section 4, we intro-

duce our new proposed architecture methodology for

real time text searching and monitoring, along with a

brief explanation of the experimental setup . Finally,

Section 5 discusses the conclusions drawn from our

ﬁndings.

3 STATE OF THE ART

This section highlights the present status of Elastic

Stack technology utilized in this study.

3.1 ELASTICSEARCH

Elasticsearch, an openly accessible distributed and

open-source search tool, furnishes an analytical en-

gine that facilitates text, numeric, geospatial, struc-

tured, and unstructured search capabilities. Elas-

ticsearch, formerly known as Elastic Research N.V.

and now referred to as Elastic, is constructed on the

foundation of Apache Lucene. Its popularity stems

from the simplicity of its REST APIs, distributed na-

ture, speed, and scalability. Elastic search(Kathare

et al., 2020) is the central component of the Elastic

Stack. Other notable examples of websites and appli-

cations leveraging Elasticsearch include Wikipedia,

which utilizes it for text search and recommendations,

Stack Overﬂow for location-based queries and sim-

ilar searches, and GitHub for code searches, show-

casing the versatility of Elasticsearch across different

domains.

The system architecture is built on the following

concepts:

• Documents - In a basic Elasticsearch repository,

a document comprises ﬁelds of a speciﬁed type,

each having a unique identiﬁer, and it needs to be

included in an index.

• Index - Documents’ logical storage location. It

uses the inverse index model. It is distributed

across numerous points and subdivided into one

or more spheres.

• Node - a single Elasticsearch instance that is ac-

tive.

• Cluster - cooperative grouping of interconnected

nodes.

• Shards - Splitting one index into multiple sub-

shards allows for parallelization, which boosts ef-

ﬁciency. It is possible to store the shards on vari-

ous servers.

• Replicas - replicas of the broken pieces. Replicas

are used to boost throughput, obtain data redun-

dancy, and achieve high availability.

3.2 KIBANA

The Elastic search community created Kibana, a vi-

sualization tool, in 2013. With its intuitive and user-

Enhanced Real-Time Monitoring and Visualization System for Efﬁcient Data Analysis: A Case Study on Public Distribution System Data in

Assam, India

friendly interface, it offers real-time capabilities for

analysis, summarization, charting, and debugging.

Sharing of log snapshots is also permitted. With

Kibana (Srivastava, 2019), we can manage several

dashboards with an easy-to-use browser-based inter-

face and save the dashboard.

4 PROPOSED ARCHITECTURE

This section outlines the methodology and dataset

employed in the experimental setup.

4.1 METHODOLOGY

The system design architecture employed in this re-

search is depicted in Fig. 1. There are two servers

server1 and server2. The speciﬁcations of both

servers are exactly the same. Server 1 installs Elastic-

search with a PostgreSQL input, while Server 2 uses

the PostgreSQL input directly. The client can access

the servers through the localhost network connection.

Figure 1: System Design Architecture

A server is conﬁgured as a node. There is “Elas-

tic Node-1” for server 1 and “PostgreSQL database”

for server 2. Elastic Node is conﬁgured in a cluster

named “elasticsearch”. Each node will have indices

inside them for storing data. The software system ar-

chitecture is shown in Fig. 2(a).

The collected data is ration card data that is taken

from the PDS Assam website and is stored into Post-

greSQL database. This data is stored into Elastic-

search index using python code and Kibana is used

to visualize Elasticsearch data using graphs, tables or

charts and analyze this data easily from a browser.

The system data ﬂow design is illustrated in Fig. 2(b).

(a) System Design Architecture

(b) Software Data Flow Design.

Figure 2: Software Architecture and Design.

4.2 DATASET

A dataset comprising 58,437 ration card holders has

been chosen for import into Elasticsearch from Post-

greSQL using Python code. The dataset includes

various ﬁelds such as FPS (Fair Price Shop), ration

card number, holder name, member serial number,

member name, member detector, age, relationship,

mother’s name, father’s name, gender, and district.

This dataset is the result of aggregating individual

FPS dealer details from different districts.

To validate the accuracy of the data import, multi-

ple search queries were executed on the Elasticsearch

node using a comprehensive Query Domain Speciﬁc

Language (DSL). All these queries returned accurate

and expected responses. Fig. 3 is an illustration show-

casing a sample of the dataset.

4.3 EXPERIMENTAL SETUP

To become acquainted with the Elastic Stack technol-

ogy and examine its potential, a simple experimental

IC3Com 2024 - International Conference on Cognitive & Cloud Computing

Figure 3: Raw Data.

Elasticsearch with Kibana infrastructure was built up.

To begin, Elasticsearch version 7.17.1 was in-

stalled on a local PC with the following speciﬁcations:

2 cores CPU, 4 threads, 4 GB RAM, 500 GB hard

disk, 256 GB SSD, and Microsoft Windows 10 Pro-

fessional. Installation instructions from the ofﬁcial

manual

resulted in a simple and rapid setup. Sub-

sequently, the Elasticsearch node was initiated in the

background using its default settings automatically. A

sample request was submitted to elastic search to as-

sess the node’s health, and the results showed proper

execution. In a similar manner, the Elastic node op-

erating in the system was located and setup using

Kibana version 7.17.1.

5 RESULTS AND DISCUSSION

Once Kibana is launched, the Kibana server identiﬁes

the Elasticsearch node and establishes a connection to

it. Utilizing the web interface, it presents a summary

of the system’s infrastructure, as illustrated in Fig. 4.

For visualization purposes, pie charts, graphs, and

tables were generated using Kibana, utilizing the

stored data to explore the range of functionalities pro-

vided by the Kibana tool. For instance, Fig. 5(a) dis-

plays two charts and total number of records available

in elastic search. First chart represent all the FPS deal-

ers with number of ration card holder enrolled under

them with the percentage of occurrences and second

chart displays the gender ratio between male, female

and trans-gender. Fig. 5(b) shows a searchable data-

table representing all the ﬁelds present in the given

data in an efﬁcient organized manner. Kibana data-

table supports three types of searching mechanism as:

Lucene Search (Full Text Normal Searching), KQL

Getting Started with Elastic Stack Technol-

ogy (2022) [Online]. Following the instruc-

tions at https://www.elastic.co/guide/en/elastic-

stack/7.17/installing-elastic-stack.html

Figure 4: Elasticsearch Cluster.

(Kibana Query Language) Search and Filter Search. It

has been noted that Kibana provides a wide range of

visualization options, with graphs refreshing rapidly

upon any parameter change.

(a) Kibana Charts.

(b) Kibana Searchable data table.

Figure 5: Data display using Kibana.

Using Flask, and Kibana’s short URLs we have

created a monitoring dashboard or a website as shown

in Fig. 6(a) for easy monitoring and quick searching

of big data (PDS Data). The dashboard has been de-

signed with a special page for API handler that sup-

ports more than 15 various APIs as shown it Fig. 6(b)

Enhanced Real-Time Monitoring and Visualization System for Efﬁcient Data Analysis: A Case Study on Public Distribution System Data in

Assam, India

(a) Dashboard for PDS Data.

(b) Public APIs handler page.

Figure 6: Website Overview.

and Table 1. It can be utilized for additional research

endeavors and integrated with any system, as depicted

in Fig. 6(b). It can also be used for easy retrieval of

ration card-related information in real-time.

The developed web application software fulﬁlls the

following speciﬁc requirements:

i) The system displays the basic details of each

Card Holder details including name, Card Num-

ber, Family Member Name, Age, District, and

FPS dealer name.

ii) The system allows faster access, and searching of

PDS data in real-time, allowing downloading of

results in CSV ﬁle format as shown in Fig. 7.

iii) The system allows the generation of dynamic

Dashboards containing KPI-based data visualiza-

tion using Kibana in real-time and allows down-

loading of results in CSV ﬁle format.

iv) The system allows district-wise monitoring of the

PDS records via interactive charts and graphs.

Based on the data presented in Table 2, it is ev-

ident that the proposed method outperforms Post-

greSQL in terms of data rendering time across dif-

Figure 7: Result of a single API request.

ferent numbers of records. For instance, when han-

dling 58,437 records, the proposed method demon-

strates a rendering time of 2-4 minutes compared

to PostgreSQL’s 5-7 minutes. Moreover, for larger

datasets containing 269,548 records, the proposed

method shows signiﬁcant improvement with a render-

ing time of 5-7 minutes compared to PostgreSQL’s

greater than 10 minutes. These results highlight the

effectiveness of the proposed approach in achieving

faster data rendering, making it a promising solution

for real-time data analysis and management tasks. In

conclusion, we’ve incorporated an ELK panel into the

system to assist in dataset analysis. This feature al-

lows for the creation of aggregations and basic visu-

alizations for each data ﬁeld. This addition enhances

the system’s ability to thoroughly explore and inter-

pret data, contributing to better insights and decision-

making.

6 CONCLUSION

The research culminated in developing a robust PDS

data analysis monitoring system for the National In-

formatics Centre, Government of Assam. Utilizing

Elasticsearch and Kibana, integrated with Flask and

PostgreSQL, the objective was to establish a DevOps

monitoring framework capable of handling extensive

volumes of big data and real-time events. The ini-

tial phase involved immersing into the technological

landscape to explore possibilities and establish Elas-

ticsearch as the cornerstone technology, chosen for

its scalability, speed, and simplicity tailored to the

provided dataset. This strategic choice aligned with

the overarching goal of creating an effective solution

for PDS data analysis and monitoring system. Future

work could focus on exploring opportunities to inte-

grate emerging technologies such as blockchain for

enhanced data security and transparency could further

bolster the robustness of the monitoring system.

IC3Com 2024 - International Conference on Cognitive & Cloud Computing

Table 1: API Endpoints and Descriptions

API Endpoints Description

http://localhost:5000/#home Used to display the dashboard

of the proposed system.

http://localhost:5000/API Used to display the entire avail-

able API to the proposed sys-

tem.

http://localhost:5000/fpsName/<fpsName> Used to fetch 10 records of the

ration cards holders that come

under the particular FPS dealer.

http://localhost:5000/fpsName/<fpsName>/<records> Used to fetch required amount

of the ration cards holder

records that come under the

particular FPS dealer.

http://localhost:5000/memberName/<memberName> Used to fetch 10 records of the

ration cards holders that come

under the given member name.

http://localhost:5000/memberName/<memberName>/<records> Used to fetch required amount

of the ration cards holder

records that come under the

given member name.

http://localhost:5000/fatherName/<fatherName> Used to fetch 10 records of the

ration cards holders that come

under the given father name.

http://localhost:5000/fatherName/<fatherName>/<records> Used to fetch required amount

of the ration cards holder

records that come under the

given father name.

http://localhost:5000/motherName/<motherName> Used to fetch 10 records of the

ration cards holders that come

under the given mother name.

http://localhost:5000/motherName/<motherName>/<records> Used to fetch required amount

of the ration cards holder

records that come under the

given mother name.

http://localhost:5000/districtName/<districtName> Used to fetch 10 records of the

ration cards holders that come

under the given district.

http://localhost:5000/districtName/<districtName>/<records> Used to fetch required amount

of the ration cards holder

records that come under the

given district.

http://localhost:5000/RationCardNo/<rationCardNumber> Used to fetch at max 20 records

of the ration cards holders that

come under the given ration

card number.

http://localhost:5000/familyHead/<rationCardOwnerName> Used to fetch at max 20 records

of the ration cards holders that

come under the given ration

card owner name.

Enhanced Real-Time Monitoring and Visualization System for Efﬁcient Data Analysis: A Case Study on Public Distribution System Data in

Assam, India

Table 2: Time required for data rendering (in min)

Number of

Records

PostgreSQL Proposed

Method

58437 5-7 min 2-4 min

269548 >10 min 5-7 min

ACKNOWLEDGMENT

This research is done for National Informatics Cen-

tre, Assam State Unit. The authors are grateful to

Pranab Chakravarty, Scientist E (Technical Director)

of NIC, Department of Computer Science and Infor-

mation Technology Cotton University, and Depart-

ment of Computer Science and Engineering, Tezpur

University for giving us the opportunity to carry out

this work.

REFERENCES

Akshaya, H., Vidya, J., and Veena, K. (2015). A ba-

sic introduction to devops tools. International Jour-

nal of Computer Science & Information Technologies,

6(3):05–06.

Bagnasco, S., Berzano, D., Guarise, A., Lusso, S., Masera,

M., and Vallero, S. (2015). Monitoring of iaas and

scientiﬁc applications on the cloud using the elastic-

search ecosystem. In Journal of physics: Conference

series, volume 608, page 012016. IOP Publishing.

Bhopale, A. P. and Tiwari, A. (2024). Transformer based

contextual text representation framework for intelli-

gent information retrieval. Expert Systems with Ap-

plications, 238:121629.

Calderon, G., del Campo, G., Saavedra, E., and Santamar

ıa,

A. (2023). Monitoring framework for the performance

evaluation of an iot platform with elasticsearch and

apache kafka. Information Systems Frontiers, pages

1–17.

Hamilton, J., Gonzalez Berges, M., Tournier, J.-C., and

Schoﬁeld, B. (2018). Jacow: Scada statistics moni-

toring using the elastic stack (elasticsearch, logstash,

kibana).

Karmalkar, V. et al. (2021). Twego trending: Data ana-

lytics based search engine using elasticsearch. Turk-

ish Journal of Computer and Mathematics Education

(TURCOMAT), 12(1S):246–251.

Kathare, N., Reddy, O. V., and Prabhu, V. (2020). A com-

prehensive study of elasticsearch. International Jour-

nal of Science and Research (IJSR).

onig, L. and Steffens, A. (2018). Towards a quality model

for devops. Continuous Software Engineering & Full-

scale Software Engineering, 37:37–42.

Nezhmetdinov, R., Kotyrova, S., Nezhmetdinova, R., and

Kovalev, I. (2023). Automating log analysis for in-

dustrial equipment maintenance using elastic stack. In

E3S Web of Conferences, volume 389, page 01064.

EDP Sciences.

Shah, N., Willick, D., and Mago, V. (2022). A framework

for social media data analytics using elasticsearch and

kibana. Wireless networks, pages 1–9.

Shukla, P. and Kumar, S. (2019). Learning Elastic Stack

7.0: Distributed Search, Analytics, and Visualization

Using Elasticsearch, Logstash, Beats, and Kibana.

Packt Publishing Ltd.

Srivastava, A. (2019). Kibana 7 quick start guide: visualize

your elasticsearch data with ease. Packt Publishing

Ltd.

Takase, W., Nakamura, T., Watase, Y., and Sasaki, T.

(2017). A solution for secure use of kibana and elas-

ticsearch in multi-user environment. arXiv preprint

arXiv:1706.10040.

Wahaballa, A., Wahballa, O., Abdellatief, M., Xiong, H.,

and Qin, Z. (2015). Toward uniﬁed devops model.

In 2015 6th IEEE international conference on soft-

ware engineering and service science (ICSESS), pages

211–214. IEEE.

Walter-Tscharf, F. F. W. V. (2022). Indexing, clustering, and

search engine for documents utilizing elasticsearch

and kibana. In Mobile Computing and Sustainable In-

formatics: Proceedings of ICMCSI 2022, pages 897–

910. Springer.

Zamﬁr, V.-A., Carabas, M., Carabas, C., and Tapus, N.

(2019). Systems monitoring and big data analysis us-

ing the elasticsearch system. In 2019 22nd Interna-

tional Conference on Control Systems and Computer

Science (CSCS), pages 188–193. IEEE.

IC3Com 2024 - International Conference on Cognitive & Cloud Computing