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Journal articles on the topic 'Big data analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 October 2024

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1

Sharma, Mansi. "Predictive Analysis: Rolein Big Data." Indian Journal of Science and Technology 12, no. 38 (October 20, 2019): 1–8. http://dx.doi.org/10.17485/ijst/2019/v12i38/145564.

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2

Azizović, Melisa. "Big Data cluster analysis." Ekonomski izazovi 13, no. 25 (2024): 21–33. http://dx.doi.org/10.5937/ekoizazov2425020a.

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In this paper, we focused on cluster analysis as the most commonly used technique for grouping different objects. By clustering data, we can extract groups of similar objects from different collections. First, we defined Big Data and clustering to follow the rest of the paper. We presented the most popular techniques for clustering data, including partitioning, hierarchical clustering, density-based clustering, and network-based clustering. Big Data describes large amounts of data. High precision of big data can contribute to decision-making confidence, and better estimates can help increase efficiency, reduce costs, and risks. Various methods and approaches are used for data processing, including clustering, classification, regression, artificial intelligence, neural networks, association rules, decision trees, genetic algorithms, and the nearest neighbor method. A cluster represents a set of objects from the same class, which means that similar objects are grouped together, and different objects are grouped separately. We described the K-means algorithm, hierarchical clustering, density-based clustering -DBSCAN algorithm, and the STING algorithm for network-based clustering.
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Binney, W. "Big Data Analysis." European Data Protection Law Review 3, no. 1 (2017): 13–15. http://dx.doi.org/10.21552/edpl/2017/1/4.

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4

Lee, Hae-Young. "Big Challenge in Big Data Research: Continual Dispute on Big Data Analysis." Korean Circulation Journal 50, no. 1 (2020): 69. http://dx.doi.org/10.4070/kcj.2019.0349.

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Shaytura, S. V., and D. A. Galkin. "Geomarketing Big Data Analysis." INFORMACIONNYE TEHNOLOGII 27, no. 4 (April 10, 2021): 180–87. http://dx.doi.org/10.17587/it.27.180-187.

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The accumulation of a large amount of geospatial data requires new approaches to their processing and visualization. One of these approaches is the creation of a geomarketing system with a fundamentally new toolkit based on data clustering. The capabilities of such a system are shown using examples of housing cost assessment, determining the location of a new shopping center, a bank branch and a clinic.
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Otsuki, Akira. "Big Data Landscape Analysis." Joho Chishiki Gakkaishi 23, no. 4 (2013): 429–41. http://dx.doi.org/10.2964/jsik.23_429.

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Iosifidis, Alexandros, Anastasios Tefas, Ioannis Pitas, and Moncef Gabbouj. "Big Media Data Analysis." Signal Processing: Image Communication 59 (November 2017): 105–8. http://dx.doi.org/10.1016/j.image.2017.10.004.

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Mikryukov, Аleksey А., Mikhail G. Granatov, and Zulfiya A. Abdrakhmanova. "BIG DATA ANALYSIS METHODS." Society, Economy, Management 8, no. 4 (December 6, 2023): 70–74. http://dx.doi.org/10.47475/2618-9852-2023-8-4-70-74.

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The volume of digital data generated by humanity is growing faster than the development of methods, tools and software for their processing and analysis. This article will discuss: “cluster method”, “hypothesis analysis”, methods of qualitative, quantitative, regression analysis and machine learning, as well as their advantages and disadvantages.
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Rawat, R., and R. Yadav. "Big Data: Big Data Analysis, Issues and Challenges and Technologies." IOP Conference Series: Materials Science and Engineering 1022 (January 19, 2021): 012014. http://dx.doi.org/10.1088/1757-899x/1022/1/012014.

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Alhussain, Thamer. "Medical Big Data Analysis Using Big Data Tools and Methods." Journal of Medical Imaging and Health Informatics 8, no. 4 (May 1, 2018): 793–95. http://dx.doi.org/10.1166/jmihi.2018.2400.

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11

Khezrimotlagh, Dariush, Joe Zhu, Wade D. Cook, and Mehdi Toloo. "Data envelopment analysis and big data." European Journal of Operational Research 274, no. 3 (May 2019): 1047–54. http://dx.doi.org/10.1016/j.ejor.2018.10.044.

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12

Bilge, Yasar. "Error analysis in big data analysis." Atlantic Journal of Medical Science and Research 3, no. 2 (2023): 81. http://dx.doi.org/10.5455/atjmed.2022.11.019.

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Practice errors in medicine are among the situations that disrupt patient-physician communication, reduce work efficiency, and eventually lead to the formation of an atmosphere of distrust. Various aspects of the subject have been examined in the symposiums we have held with Prof Dr. Ethem Geçim for 14 years. We shared our information at medikolegalduzlem.com. Big Data analysis is important in system audits. It is inevitable to reach erroneous results if the data belonging to these are made carelessly and negligently. The main problems encountered in big data analysis are: 1. Dependency: Computer dependency is at the forefront of algorithm and process monitoring. 2. Chaotic situation: The problem of systematic change is observed in the supervised learning machine. In the unsupervised learning machine, the definition of the identity of grouping is problematic. In the learning machine system of the reward-punishment system, there is a case study based on random data, not systematic. It is very difficult to reach an inductive conclusion from this situation. There is absolutely a need to organize a constant, standard data flow. 3. Loss of control: Steps that need to be checked are skipped in big data monitoring. Injustice occurs. 4. Waiver of rights: The expert is content with the data given by the machine as a dependent. There is a problem with the development of innovative processes. In that case, it is useful to pay attention to the following points in the regulation on data ethics: 1. Identity: In the big data analysis of the expert, it is important to classify and group the personal data and to accept it in court. In this respect, it is necessary to control whether the expert is legitimate, sufficient, and effective. 2. Confidentiality: The problem of privacy of the person being examined is important. The approach of the telemedicine application, which we call image-based analysis, to this problem is subject to legal and ethical evaluation. 3. Ownership: There are inadequacies in the regulation of the infrastructure applied in the field of health. The purchase of each tool and its accessible application also needs regulation. 4. Reputation: Audit problematic of non-standard practices of practitioners is evident. As a result, there is a need to evaluate error definition, error measurement, error effect, and error tolerability in big data analysis.
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S., Geetha. "Big Data Analysis - Cybercrime Detection in Social Network." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 147–52. http://dx.doi.org/10.5373/jardcs/v12sp4/20201476.

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14

Prathibha, U., M. Thillainayaki, and A. Jenneth. "Big Data Analysis with R Programming and RHadoop." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 2623–27. http://dx.doi.org/10.31142/ijtsrd15705.

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15

Song, Jihyun, and Kyeongjoo Kim. "A Big Data Analysis and Mining Approach for IoT Big Data." International Journal of Advances in Computer Science and Technology 7, no. 1 (January 23, 2018): 1–3. http://dx.doi.org/10.30534/ijacst/2018/01712018.

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Choi, JunHyeog, and Sunghae Jun. "Big Data Smoothing and Outlier Removal for Patent Big Data Analysis." Journal of the Korea Society of Computer and Information 21, no. 8 (August 31, 2016): 77–84. http://dx.doi.org/10.9708/jksci.2016.21.8.077.

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Bopche, Archana. "Big Data Analysis using Hadoop." International Journal of Computer Applications 179, no. 6 (December 15, 2017): 1–4. http://dx.doi.org/10.5120/ijca2017915960.

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Sareen, Sanyam, and Shivangi Ahuja. "Big Data Analysis: A Review." International Journal of Computer Applications 181, no. 23 (October 17, 2018): 5–9. http://dx.doi.org/10.5120/ijca2018917994.

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Bae, Jung-ho, and Eunae Burm. "Big Data Analysis: Medical Accident." Medico-Legal Update 19, no. 1 (2019): 646. http://dx.doi.org/10.5958/0974-1283.2019.00115.4.

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KADOWAKI, Kohmei. "Big Data Analysis in Ecology:." Journal of Environmental Conservation Engineering 48, no. 3 (May 20, 2019): 126–30. http://dx.doi.org/10.5956/jriet.48.3_126.

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21

Ryu, Seewon, and Tae-Min Song. "Big Data Analysis in Healthcare." Healthcare Informatics Research 20, no. 4 (2014): 247. http://dx.doi.org/10.4258/hir.2014.20.4.247.

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22

Wright, Joshua D., and Elyse Dorsey. "Antitrust Analysis of Big Data." Competition Law & Policy Debate 2, no. 4 (December 2016): 35–41. http://dx.doi.org/10.4337/clpd.2016.04.05.

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23

Alsaqqaf, Amal, Majdah Alsharef, Asia Aljahdali, Ghalia Alluhaib, and Rasha Alqarni. "Big Data Analysis and Forensics." International Journal of Electronic Security and Digital Forensics 1, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijesdf.2022.10044312.

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24

St-Pierre, N. "1295 Big data analysis techniques." Journal of Animal Science 94, suppl_5 (October 1, 2016): 624. http://dx.doi.org/10.2527/jam2016-1295.

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25

Heer, Jeffrey, and Sean Kandel. "Interactive analysis of big data." XRDS: Crossroads, The ACM Magazine for Students 19, no. 1 (September 2012): 50–54. http://dx.doi.org/10.1145/2331042.2331058.

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26

Fan, Jianqing, Fang Han, and Han Liu. "Challenges of Big Data analysis." National Science Review 1, no. 2 (February 5, 2014): 293–314. http://dx.doi.org/10.1093/nsr/nwt032.

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Abstract Big Data bring new opportunities to modern society and challenges to data scientists. On the one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This paper gives overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasize on the viability of the sparsest solution in high-confidence set and point out that exogenous assumptions in most statistical methods for Big Data cannot be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions.
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27

Ale, Ben. "Risk analysis and big data." Safety and Reliability 36, no. 3 (July 2, 2016): 153–65. http://dx.doi.org/10.1080/09617353.2016.1252080.

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28

Tiwari, Prayag. "Comparative Analysis of Big Data." International Journal of Computer Applications 140, no. 7 (April 15, 2016): 24–29. http://dx.doi.org/10.5120/ijca2016909400.

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29

Aggarwal, Lakshita. "Analysis of big data tools." JIMS8I � International Journal of Information Communication and Computing Technology 6, no. 2 (2018): 370. http://dx.doi.org/10.5958/2347-7202.2018.00009.9.

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Aljahdali, Asia Othman, Ghalia Alluhaib, Rasha Alqarni, Majdah Alsharef, and Amal Alsaqqaf. "Big data analysis and forensics." International Journal of Electronic Security and Digital Forensics 14, no. 6 (2022): 579. http://dx.doi.org/10.1504/ijesdf.2022.126454.

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31

Lv, Zhihan, and Liang Qiao. "Analysis of healthcare big data." Future Generation Computer Systems 109 (August 2020): 103–10. http://dx.doi.org/10.1016/j.future.2020.03.039.

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32

Trenčeva, Žaneta, Aleksandar Risteski, Toni i. Janevski, and Borislav Popovski. "BIGQUERY FOR BIG DATA ANALYSIS." Journal of Electrical Engineering and Information Technologies 7, no. 2 (2022): 77–84. http://dx.doi.org/10.51466/jeeit2272198077t.

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33

Cho, June-Suh. "Market Impact Analysis Service Based on Big Data in Gyeonggi-Do." Institute of Global Business Research 35, no. 1 (December 31, 2023): 35–54. http://dx.doi.org/10.46775/jgbr.2023.35.1.02.

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The Fourth Industrial Revolution is the driving force of change as the fundamental structure of the industry changes. Gyeonggi-do provides big data analysis services to improve citizens’ quality of life by providing various big data services. Local governments in Korea provide various services for citizens and small business owners. This paper discusses Gyeonggi-do’s ‘Market Impact Analysis Service,’ an information analysis infrastructure. This service helps start-ups, small business owners, and self-employed people who need help starting a business and management due to more information to make big data-based decisions. In this paper, we discuss the efforts of Gyeonggi-Do to provide information service based on big data analysis for local businesses using a Big data platform.
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34

Ibrahim, Nadia, Alaa Hassan, and Marwah Nihad. "Big Data Analysis of Web Data Extraction." International Journal of Engineering & Technology 7, no. 4.37 (December 13, 2018): 168. http://dx.doi.org/10.14419/ijet.v7i4.37.24095.

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In this study, the large data extraction techniques; include detection of patterns and secret relationships between factors numbering and bring in the required information. Rapid analysis of massive data can lead to innovation and concepts of the theoretical value. Compared with results from mining between traditional data sets and the vast amount of large heterogeneous data interdependent it has the ability expand the knowledge and ideas about the target domain. We studied in this research data mining on the Internet. The various networks that are used to extract data onto different locations complex may appear sometimes and has been used to extract information on the web technology to extract and data analysis (Marwah et al., 2016). In this research, we extracted the information on large quantities of the web pages and examined the pages of the site using Java code, and we added the extracted information on a special database for the web page. We used the data network function to get accurate results of evaluating and categorizing the data pages found, which identifies the trusted web or risky web pages, and imported the data onto a CSV extension. Consequently, examine and categorize these data using WEKA to obtain accurate results. We concluded from the results that the applied data mining algorithms are better than other techniques in classification and extraction of data and high performance.
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Maitrey, Seema, and C. K. Jha. "MapReduce: Simplified Data Analysis of Big Data." Procedia Computer Science 57 (2015): 563–71. http://dx.doi.org/10.1016/j.procs.2015.07.392.

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36

Kong, Deshi, Xin Cao, and Xuefeng Xiong. "Data analysis method for power big data." Advances in Engineering Technology Research 11, no. 1 (July 18, 2024): 469. http://dx.doi.org/10.56028/aetr.11.1.469.2024.

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This paper discusses a data quality analysis and evaluation mode based on power big data. Its basic content is to use the analysis platform template to conduct acquisition control and process rule analysis, and then use the data quality management module to call out the platform database system that realizes the inspection and analysis function, and retrieve and generate inspection data from the entity base. The analysis program will classify, count, sort, and sort the data, The final generated quantitative indicator data reflecting the project implementation status and data quality will be saved in the analysis result table. The analysis result report called from the middle platform can obtain a more detailed data quality analysis and evaluation report reflecting the data quality of various quantitative projects. This paper has improved the intelligent degree of information quality management and evaluation, realized the intelligent control of information service quality, adapted to the needs of large-scale information service management, and completed the quantitative research and evaluation of information completeness, timeliness, accuracy, accuracy and other important parameters.
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Protasowicki, Tomasz, and Jerzy Stanik. "Big data within national security threat analysis." Ekonomiczne Problemy Usług 123 (2016): 275–86. http://dx.doi.org/10.18276/epu.2016.123-26.

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38

Lee, Seung-Joo. "Big Data Analysis Using Principal Component Analysis." Journal of Korean Institute of Intelligent Systems 25, no. 6 (December 25, 2015): 592–99. http://dx.doi.org/10.5391/jkiis.2015.25.6.592.

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Kim, Jin Ah, and Jin Ki Kim. "Airport Congestion Analysis with Big Data Analysis." Journal of the Korean Society for Aviation and Aeronautics 28, no. 2 (June 2020): 36–46. http://dx.doi.org/10.12985/ksaa.2020.28.2.036.

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40

ZHANG, DU. "GRANULARITIES AND INCONSISTENCIES IN BIG DATA ANALYSIS." International Journal of Software Engineering and Knowledge Engineering 23, no. 06 (August 2013): 887–93. http://dx.doi.org/10.1142/s0218194013500241.

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Big data and big data analysis are a multi-dimensional scientific and technological pursuit that has profound impact on the society as a whole. Though big data has become such a catchy buzzword, to make any significant stride in this pursuit, we must have a clear picture of what big data is and what big data analysis entails. In this paper, after a brief account on the landscape of big data and big data analysis, we focus attention on two issues: granularities of knowledge content in big data, and utility of inconsistencies in big data analysis.
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41

Wax, Amy, Raquel Asencio, and Dorothy R. Carter. "Thinking Big About Big Data." Industrial and Organizational Psychology 8, no. 4 (December 2015): 545–50. http://dx.doi.org/10.1017/iop.2015.81.

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Guzzo, Fink, King, Tonidandel, and Landis (2015) review important issues—privacy, informed consent, and data/data analysis integrity—that are critical logistical considerations in any program of research with human subjects, including studies utilizing big data. Overall, we agree with the cautionary sentiment conveyed throughout the focal article; industrial and organizational (I-O) psychology researchers and practitioners should not assume that big data is a panacea, and many of our established disciplinary approaches for ensuring ethical and accurate research are applicable—or modifiable—in big data contexts. However, we believe that the conversation about big data in I-O psychology is broader than that reviewed by Guzzo et al., and we would like to further elaborate on the focal article. We present this commentary from our perspective as junior scholars entering the field at a critical time—a time when I-O psychology is becoming increasingly intertwined with big data science.
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Jeon, Gwanggil, Valerio Bellandi, Abdellah Chehri, and Ernesto Damiani. "Big Multimodal Data Analysis: Models and Performance Analysis." Big Data 10, no. 5 (October 1, 2022): 369–70. http://dx.doi.org/10.1089/big.2022.0216.

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43

ORLOV, GRIGORY A., ANDREY V. KRASOV, and ARTEM M. GELFAND. "THE USE OF BIG DATA IN THE ANALYSIS OF BIG DATA IN COMPUTER NETWORKS." H&ES Research 12, no. 4 (2020): 76–84. http://dx.doi.org/10.36724/2409-5419-2020-12-4-76-84.

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The concept of Big Data includes the totality of all data sets, the total size of which is several times larger than the capabilities of conventional databases.it is also necessary to note the use of non-classical data processing methods. For example, in the management, analysis of information received, or simply storage. Big Data algorithms have emerged in parallel with the introduction of the first high-performance servers of their kind, such as the mainframe, which have sufficient resources required for operational information processing, as well as corresponding to computer calculations with subsequent analysis. The algorithms are based on performing series-parallel calculations, which significantly increases the speed of performing various tasks. Entrepreneurs and scientists are interested in Big Data, who are concerned with issues related to not only high-quality, but also up-to-date interpretation of data, as well as creating innovative tools for working with them. A huge amount of data is processed in order for the end user to get the results they need for their further effective use. Big Data enables companies to expand the number of their customers, attract new target audiences, and also helps them implement projects that will be in demand not only among current customers, but also attract new ones. Active implementation and subsequent use of Big Data correspond to the solution of these problems. In this paper, we compare the main types of databases and analyze intrusion detection using the example of distributed information system technologies for processing Big Data. Timely detection of intrusions into data processing systems is necessary to take measures to preserve the confidentiality and integrity of data, as well as to correctly correct errors and improve the protection of the data processing system.
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Kaisler, Stephen H., William H. Money, Frank Armour, and J. Alberto Espinosa. "Big Data." International Journal of Systems and Service-Oriented Engineering 7, no. 2 (April 2017): 1–23. http://dx.doi.org/10.4018/ijssoe.2017040101.

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Big Data refers to data volumes in the range of exabytes (1018th) requiring processing from distributed on-line storage systems with thousands of processors, mainframes or supercomputers where processing speed is measured in GFLOPS. The rate at which data are being collected are accelerating and will approach the zettabyte/year range. Other attributes of Bi Data are also concurrently expanding including variety/variability, velocity, value, and vital concerns for veracity. Storage and data transport technology issues may be solvable in the near-term. However, these communication, quantity management, and processing technologies also represent long-term challenges that require research, paradigms and analytical practices. This paper extends the authors' previous analysis of the issues and challenges with Big Data. It presents a table that contrasts their previous research finding and projects with the state of Big Data today, and their projections of what managers and decision makers will (or should) seek to accomplish as the Big Data universe continues to expand and evolve.
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Arena, Fabio, and Giovanni Pau. "An overview of big data analysis." Bulletin of Electrical Engineering and Informatics 9, no. 4 (August 1, 2020): 1646–53. http://dx.doi.org/10.11591/eei.v9i4.2359.

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Big data represents one of the most profound and most pervasive evolutions in the digital world. Examples of big data come from Internet of Things (IoT) devices, as well as smart cars, but also the use of social networks, industries, and so on. The sources of data are numerous and continuously increasing, and, therefore, what characterizes big data is not only the volume but also the complexity due to the heterogeneity of information that can be obtained. The fastest growth in spending on big data technologies is happening within banking, healthcare, insurance, securities and investment services, and telecommunications. Remarkably, three of those industries lie within the financial sector, which has many particularly serviceable use cases for big data analytics, such as fraud detection, risk management, and customer service optimization. In fact, the definition of big data analysis refers to the process that encompasses the gathering and analysis of big data to obtain useful information for the business. This paper focuses on delivering a short review concerning the current technologies, future perspectives, and the evaluation of some use cased associated with the analysis of big data.
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46

Borrill, Julian, Reijo Keskitalo, and Theodore Kisner. "Big Bang, Big Data, Big Iron: Fifteen Years of Cosmic Microwave Background Data Analysis at NERSC." Computing in Science & Engineering 17, no. 3 (May 2015): 22–29. http://dx.doi.org/10.1109/mcse.2015.1.

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47

Dehdouh, Khaled, Omar Boussaid, and Fadila Bentayeb. "Big Data Warehouse." International Journal of Decision Support System Technology 12, no. 1 (January 2020): 1–24. http://dx.doi.org/10.4018/ijdsst.2020010101.

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In the Big Data warehouse context, a column-oriented NoSQL database system is considered as the storage model which is highly adapted to data warehouses and online analysis. Indeed, the use of NoSQL models allows data scalability easily and the columnar store is suitable for storing and managing massive data, especially for decisional queries. However, the column-oriented NoSQL DBMS do not offer online analysis operators (OLAP). To build OLAP cubes corresponding to the analysis contexts, the most common way is to integrate other software such as HIVE or Kylin which has a CUBE operator to build data cubes. By using that, the cube is built according to the row-oriented approach and does not allow to fully obtain the benefits of a column-oriented approach. In this article, the focus is to define a cube operator called MC-CUBE (MapReduce Columnar CUBE), which allows building columnar NoSQL cubes according to the columnar approach by taking into account the non-relational and distributed aspects when data warehouses are stored.
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48

Omar, Hoger Khayrolla, and Alaa Khalil Jumaa. "Distributed big data analysis using spark parallel data processing." Bulletin of Electrical Engineering and Informatics 11, no. 3 (June 1, 2022): 1505–15. http://dx.doi.org/10.11591/eei.v11i3.3187.

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Nowadays, the big data marketplace is rising rapidly. The big challenge is finding a system that can store and handle a huge size of data and then processing that huge data for mining the hidden knowledge. This paper proposed a comprehensive system that is used for improving big data analysis performance. It contains a fast big data processing engine using Apache Spark and a big data storage environment using Apache Hadoop. The system tests about 11 Gigabytes of text data which are collected from multiple sources for sentiment analysis. Three different machine learning (ML) algorithms are used in this system which is already supported by the Spark ML package. The system programs were written in Java and Scala programming languages and the constructed model consists of the classification algorithms as well as the pre-processing steps in a figure of ML pipeline. The proposed system was implemented in both central and distributed data processing. Moreover, some datasets manipulation manners have been applied in the system tests to check which manner provides the best accuracy and time performance. The results showed that the system works efficiently for treating big data, it gains excellent accuracy with fast execution time especially in the distributed data nodes.
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49

Zvyagin, L. S. "DATA MINING: BIG DATA AND DATA SCIENCE." SOFT MEASUREMENTS AND COMPUTING 5, no. 54 (2022): 81–90. http://dx.doi.org/10.36871/2618-9976.2022.05.006.

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Data mining is the process of discovering information that can be used in large amounts of data. This method uses mathematical analysis, which helps to identify patterns and trends in the data. Such patterns cannot be noticed during normal data viewing due to the complexity of the relationships that arise with a large amount of data. All of them are a set of tools and methods that help humanity in the changing world around us. It is becoming more and more voluminous, we receive huge aggregates of data on various processes. Big Data and Data Science allow large companies to systematize information about the markets in which they operate, which allows them to get a large amount of profit and benefits.
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Park, Sung-Uk. "Keyword Analysis of Data Technology Using Big Data Technique." Journal of Korea Technology Innovation Society 22, no. 2 (April 30, 2019): 265–81. http://dx.doi.org/10.35978/jktis.2019.04.22.2.265.

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