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Big Data Analytics

Dr. Tamaro J. Green

Big Data Analytics is a field of computer science that combines both computing, statistical, data storage, and data processing tools for the analysis of big data. Big data analytics can also combine computing industries such as health care, retail, government, manufacturing, and media. The big data analytics market is expected to grow from US$37.34 Billion in 2018 to US$105.8 Billion in 2027.1 Big data analytics developed as tools and techniques became available for the analysis of big data. As big data storage capabilities increased hardware and software that could process and analyze big data has developed.2 3 Big data analytics emerged as a field where emerging data analysis and data processing tools have developed to address limitations in traditional data management systems.4 Some of the emerging technologies for big data analytics include parallel processing and distributed computing.5

Big data analytics combines with artificial intelligence and machine learning to improve wireless networks and network security.6 Data scientists can automate network diagnostics on big data analytics platforms to identify root causes and anomolies of network problems.6 Machine learning improves big data analytics platforms with natural language processing that enables classification, categorization, extraction, and annotation of text.7 Artificial intelligence and machine learning can facilitate the analysis of big data that is retrieved from sensors and devices in the Internet of Things.8 Reliable and responsible artificial intelligence can improve the capabilities of big data analytics platforms.9


Big Data Analytics


Big data analytics has facilitated software services such as security monitoring, log analysis, and cyber-security threat detection.10 Big data analytics has also proven resourceful for large scale analysis in health care and financial systems.11 In health care big data analytics provide tools for analyzing large datasets of electronic health records.12 For financial systems, big data analytics has been the foundation of e-commerce and financial trading systems.13


The history of big data analytics is associated with the development of big data and the fields of data science and machine learning.14 As big data grew from storage capacities of database management systems beyond traditional servers, big data analytics grew from the business intelligence and reporting systems of traditional database management systems.15 Cloud computing and software-as-a-service systems also enhanced processing resources for large scale analytics.16 Software-as-a-service platforms allow for big data analytics on servers or cloud systems.17 The analysis of big data in specialized fields has also advanced the development of big data analysis.18

Related Fields

Healthcare analytics analyzes large sets of medical field data.19 Examples of healthcare analytics systems include the analysis of electronic health records, genomic data, or disease surveillance data.20 Healthcare analytics systems can combine machine learning and artificial intelligence to automate analysis.21 Artificial intelligence and big data analytics have been combined for the tasks of infectious disease surveillance and for developing precision health care analytics systems.22 23 Big data analytics can also increase the capabilities of image analysis in radiology.24 Big data analytics has also played a role in climate modeling, bioinformatics, and smart health care systems.25

Big data analytics supports audits and accounting for financial systems.26 Financial institutions incorporate artificial intelligence and machine learning to analyze large sets of transaction data.27 Financial institutions also implement big data analytics to measure the performance of firms.28 Big data analytics offer text mining tools to analyze large data sets of corporate documents.29 Big data analytics platforms supply text mining tools such as sentiment analysis to develop models that combine data from social media sources to evaluate systemic risk of financial systems.30 Big data analytics platforms combine natural language processing and machine learning to assist financial regulators govern financial systems.31 Big data analytics platforms are also capable of identifying patterns in large financial transaction datasets for fraud detection.32 Accountants can automate routine tasks and offer more time with their clients with big data analytics.33

Financial technology companies build off big data analysis platforms to provide wealth management services.34 Financial technology, or fintech, companies can offer customers personalized services with the advantage of big data analysis platforms.35 Big data analytics platforms run artificial intelligence algorithms to provide risk management solutions.36 Some of the services that big data analytics provides fintech companies are the ability to deliver digital experiences for customers, offer new products quickly, and provide investment decision support.37 Big data analytics and artificial intelligence also facilitate new technologies such as algorithmic trading, cryptocurrency markets, block chain, peer to peer lending, and crowdfunding.38

Big data analytics has driven changes in analysis of labor markets and the digitization of government services.39 Econometrics and census data can be collected, analyzed, and reported with greater speed and efficiency due to the technologies and methodologies of big data analytics.40 Information discovery from social media and other spaces of interaction can also assist governments and labor organizations.41 Big data analytics platforms have the potential of reducing the workload of paralegals, lawyers, and judges.42 Big data analytics may also provide policy makers tools for measuring accountability for human rights violations.43 Big data analytics may support the development of intelligent transportation systems and smart cities.44 45 Data scientists may also provide decision makers with information to design supply chain logistics, plan procurement, or optimize inventory with big data analytics platforms.46

In education, big data analytics has a role in both instruction and administration for educational institutions.47 Big data analytics serves as a tool for educators to improve the quality of education and provide resources for intervention.48 Learning analytics and education data science describe the role of big data analytics in education systems.49 Big data analytics platforms can serve education administrators in the evaluation, formulation, analysis, and implementation of education policy.50 Educators can operate predictive analytics on big data analytics platforms to identify patterns in students and develop evidence based cycles of improvements.51 Big data analytics platforms are tools to launch innovative learning environments with a divergence of channels and resources.52 Educational data mining and social learning analytics allow big data analytics platforms to support innovative learning environment in more than tracking progress but to augment communication and collaboration.53 Big data analytics text mining facilitates the instruction of foreign languages and presents resources for remote language learning.54 Big data analytics supplies tools for self-regulated assessments in and data mining model analysis of massive online open courses.55


There are three types of big data analytic systems, descriptive, predictive, and prescriptive.56 Descriptive big data analytics allow statistical methods to apply to large data-sets.57 Data scientists utilize predictive big data analytics to forecast performance or value of large data sets.58 Prescriptive big data analytics produces models and simulations from large data sets for scenario planning and decision making.59


Data visualization dashboards present charts, graphs, and other display tools for big data analytics.60 Interactive data visualization dashboards provide user interface controls to manipulate data that is presented in the visualization tools.61 The design of big data dashboards supports the ability of viewers to identify critical results of big data analytics.62 Big data analytics dashboards also allow for decision makers to take actionable steps through the analysis of big data.63 Data scientists can develop pipelines to feed data visualization tools data from heterogeneous sources.64

Big data analytics has also served social science and media with data journalism.65 Open data and digital journalism have contributed to large data sets available for big data analytics in journalism.66 Big data analytics provides an intermediary resource for digital transformations in data journalism.67

The ability to analyze large data sets has afforded natural language processing engines the tools to perform text analysis on corpuses of manuscripts and social media.68 Machine learning capabilities and research into the social constructs of language contributed to big data analytics in natural language processing.69 Big data analytics allows for categorization of large data sets of unstructured data for natural language processing engines.70


1.     * "Big Data Analytics Market | US$ 105.08 BN Market by 2027 | Global Trends and Forecast – Business". Retrieved 2021-01-20.

2.     * Avci, Cigdem; Tekinerdogan, Bedir; Athanasiadis, Ioannis N. (2020-08-14). "Software architectures for big data: a systematic literature review". Big Data Analytics. 5 (1): 5. doi:10.1186/s41044-020-00045-1ISSN 2058-6345.

3.     * "Becoming the orchestrator of the digital ecosystem - Data Matters". Retrieved 2021-01-20.

4.     * Tsai, Chun-Wei; Lai, Chin-Feng; Chao, Han-Chieh; Vasilakos, Athanasios V. (2015-10-01). "Big data analytics: a survey". Journal of Big Data. 2 (1): 21. doi:10.1186/s40537-015-0030-3ISSN 2196-1115.

5.     * Kambatla, Karthik; Kollias, Giorgos; Kumar, Vipin; Grama, Ananth (2014-07-01). "Trends in big data analytics". Journal of Parallel and Distributed Computing. Special Issue on Perspectives on Parallel and Distributed Processing. 74 (7): 2561–2573. doi:10.1016/j.jpdc.2014.01.003ISSN 0743-7315.

6.     ^ Jump up to:a b Kibria, M. G.; Nguyen, K.; Villardi, G. P.; Zhao, O.; Ishizu, K.; Kojima, F. (2018). "Big Data Analytics, Machine Learning, and Artificial Intelligence in Next-Generation Wireless Networks". IEEE Access. 6: 32328–32338. doi:10.1109/ACCESS.2018.2837692ISSN 2169-3536.

7.     * Moreno, A.; Redondo, T. (2016). "Text Analytics: the convergence of Big Data and Artificial Intelligence". IJIMAI. 3 (6): 57–64. ISSN 1989-1660.

8.     * Allam, Zaheer; Dhunny, Zaynah A. (2019-06-01). "On big data, artificial intelligence and smart cities". Cities. 89: 80–91. doi:10.1016/j.cities.2019.01.032ISSN 0264-2751.

9.     * Wong, Zoie S. Y.; Zhou, Jiaqi; Zhang, Qingpeng (2019-02-01). "Artificial Intelligence for infectious disease Big Data Analytics". Infection, Disease & Health. 24 (1): 44–48. doi:10.1016/j.idh.2018.10.002ISSN 2468-0451.

10.  * Cárdenas, A. A.; Manadhata, P. K.; Rajan, S. P. (November 2013). "Big Data Analytics for Security". IEEE Security Privacy. 11 (6): 74–76. doi:10.1109/MSP.2013.138ISSN 1558-4046.

11.  * Hu, H.; Wen, Y.; Chua, T.; Li, X. (2014). "Toward Scalable Systems for Big Data Analytics: A Technology Tutorial". IEEE Access. 2: 652–687. doi:10.1109/ACCESS.2014.2332453ISSN 2169-3536.

12.  * Raghupathi, Wullianallur; Raghupathi, Viju (2014-02-07). "Big data analytics in healthcare: promise and potential". Health Information Science and Systems. 2 (1): 3. doi:10.1186/2047-2501-2-3ISSN 2047-2501PMC 4341817PMID 25825667.

13.  * Akter, Shahriar; Wamba, Samuel Fosso (2016-05-01). "Big data analytics in E-commerce: a systematic review and agenda for future research". Electronic Markets. 26 (2): 173–194. doi:10.1007/s12525-016-0219-0ISSN 1422-8890.

14.  * Wang, Yichuan; Kung, LeeAnn; Byrd, Terry Anthony (2018-01-01). "Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations". Technological Forecasting and Social Change. 126: 3–13. doi:10.1016/j.techfore.2015.12.019ISSN 0040-1625.

15.  * Agrawal, Divyakant; Das, Sudipto; El Abbadi, Amr (2011-03-21). "Big data and cloud computing: current state and future opportunities". Proceedings of the 14th International Conference on Extending Database Technology. EDBT/ICDT '11. Uppsala, Sweden: Association for Computing Machinery: 530–533. doi:10.1145/1951365.1951432ISBN 978-1-4503-0528-0.

16.  * Mao, R.; Xu, H.; Wu, W.; Li, J.; Li, Y.; Lu, M. (2015-01-01). "Overcoming the challenge of variety: big data abstraction, the next evolution of data management for AAL communication systems". IEEE Communications Magazine. 53 (1): 42–47. doi:10.1109/MCOM.2015.7010514ISSN 1558-1896.

17.  * Zoumpatianos, K.; Palpanas, T. (2018-04-01). "Data Series Management: Fulfilling the Need for Big Sequence Analytics". 2018 IEEE 34th International Conference on Data Engineering (ICDE): 1677–1678. doi:10.1109/ICDE.2018.00211.

18.  * Batistič, Saša; Laken, Paul van der (2019). "History, Evolution and Future of Big Data and Analytics: A Bibliometric Analysis of Its Relationship to Performance in Organizations". British Journal of Management. 30 (2): 229–251. doi:10.1111/1467-8551.12340ISSN 1467-8551.

19.  * Ukil, A.; Bandyoapdhyay, S.; Puri, C.; Pal, A. (2016-03-01). "IoT Healthcare Analytics: The Importance of Anomaly Detection". 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA): 994–997. doi:10.1109/AINA.2016.158.

20.  * Ling, Zheng Jye; Tran, Quoc Trung; Fan, Ju; Koh, Gerald C. H.; Nguyen, Thi; Tan, Chuen Seng; Yip, James W. L.; Zhang, Meihui (2014-08-01). "GEMINI: an integrative healthcare analytics system". Proceedings of the VLDB Endowment. 7 (13): 1766–1771. doi:10.14778/2733004.2733081ISSN 2150-8097.

21.  * Islam, Md Saiful; Hasan, Md Mahmudul; Wang, Xiaoyi; Germack, Hayley D.; Noor-E-Alam, Md (2018-06-01). "A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining". Healthcare. 6 (2): 54. doi:10.3390/healthcare6020054.

22.  * Wong, Zoie S. Y.; Zhou, Jiaqi; Zhang, Qingpeng (2019-02-01). "Artificial Intelligence for infectious disease Big Data Analytics". Infection, Disease & Health. 24 (1): 44–48. doi:10.1016/j.idh.2018.10.002ISSN 2468-0451.

23.  * Firouzi, Farshad; Rahmani, Amir M.; Mankodiya, K.; Badaroglu, M.; Merrett, G. V.; Wong, P.; Farahani, Bahar (2018-01-01). "Internet-of-Things and big data for smarter healthcare: From device to architecture, applications and analytics". Future Generation Computer Systems. 78: 583–586. doi:10.1016/j.future.2017.09.016ISSN 0167-739X.

24.  * Lee, Choong Ho; Yoon, Hyung-Jin (2017-03-01). "Medical big data: promise and challenges". Kidney Research and Clinical Practice. 36(1): 3–11. doi:10.23876/j.krcp.2017.36.1.3ISSN 2211-9132PMC 5331970PMID 28392994.

25.  * Manogaran, Gunasekaran; Lopez, Daphne (2018-01-01). "Spatial cumulative sum algorithm with big data analytics for climate change detection". Computers & Electrical Engineering. 65: 207–221. doi:10.1016/j.compeleceng.2017.04.006ISSN 0045-7906.

26.  * Cao, Min; Chychyla, Roman; Stewart, Trevor (2015-06-01). "Big Data Analytics in Financial Statement Audits". Accounting Horizons. 29 (2): 423–429. doi:10.2308/acch-51068ISSN 0888-7993.

27.  * Lawler, James; Joseph, Anthony (2017-07-01). "Big Data Analytics Methodology in the Financial Industry". Information Systems Education Journal. 15 (4): 38.

28.  * Hafiz, A.; Lukumon, O.; Muhammad, B.; Olugbenga, A.; Hakeem, O.; Saheed, A. (2015-03-01). "Bankruptcy Prediction of Construction Businesses: Towards a Big Data Analytics Approach". 2015 IEEE First International Conference on Big Data Computing Service and Applications: 347–352. doi:10.1109/BigDataService.2015.30.

29.  * Pejić Bach, Mirjana; Krstić, Zivko; Seljan, Sanja; Turulja, Lejla (2019-01-01). "Text Mining for Big Data Analysis in Financial Sector: A Literature Review". Sustainability. 11 (5): 1277. doi:10.3390/su11051277.

30.  * Cerchiello, Paola; Giudici, Paolo (2016-10-01). "Big data analysis for financial risk management". Journal of Big Data. 3 (1): 18. doi:10.1186/s40537-016-0053-4ISSN 2196-1115.

31.  * O'Halloran, Sharyn; Maskey, Sameer; McAllister, Geraldine; Park, David K.; Chen, Kaiping (2015-08-25). "Big Data and the Regulation of Financial Markets". Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015. ASONAM '15. Paris, France: Association for Computing Machinery: 1118–1124. doi:10.1145/2808797.2808841ISBN 978-1-4503-3854-7.

32.  * Fay, Rebecca; Negangard, Eric M. (2017-03-01). "Manual journal entry testing: Data analytics and the risk of fraud". Journal of Accounting Education. Special Issue on Big Data. 38: 37–49. doi:10.1016/j.jaccedu.2016.12.004ISSN 0748-5751.

33.  * Richins, Greg; Stapleton, Andrea; Stratopoulos, Theophanis C.; Wong, Christopher (2017-09-01). "Big Data Analytics: Opportunity or Threat for the Accounting Profession?". Journal of Information Systems. 31 (3): 63–79. doi:10.2308/isys-51805ISSN 0888-7985.

34.  * Trelewicz, J. Q. (2017). "Big Data and Big Money: The Role of Data in the Financial Sector". IT Professional. 19 (3): 8–10. doi:10.1109/MITP.2017.45ISSN 1941-045X.

35.  * Lee, In; Shin, Yong Jae (2018-01-01). "Fintech: Ecosystem, business models, investment decisions, and challenges". Business Horizons. 61 (1): 35–46. doi:10.1016/j.bushor.2017.09.003ISSN 0007-6813.

36.  * Giudici, Paolo (2018). "Fintech Risk Management: A Research Challenge for Artificial Intelligence in Finance". Frontiers in Artificial Intelligence. 1doi:10.3389/frai.2018.00001ISSN 2624-8212.

37.  * Trelewicz, J. Q. (2017). "Big Data and Big Money: The Role of Data in the Financial Sector". IT Professional. 19 (3): 8–10. doi:10.1109/MITP.2017.45ISSN 1941-045X.

38.  * Cao, L.; Yuan, G.; Leung, T.; Zhang, W. (2020-03-01). "Special Issue on AI and FinTech: The Challenge Ahead". IEEE Intelligent Systems. 35 (2): 3–6. doi:10.1109/MIS.2020.2983494ISSN 1941-1294.

39.  * Loebbecke, Claudia; Picot, Arnold (2015-09-01). "Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda". The Journal of Strategic Information Systems. 24 (3): 149–157. doi:10.1016/j.jsis.2015.08.002ISSN 0963-8687.

40.  * Husain, Syed S.; Kalinin, Alexandr; Truong, Anh; Dinov, Ivo D. (2015-07-17). "SOCR data dashboard: an integrated big data archive mashing medicare, labor, census and econometric information". Journal of Big Data. 2 (1): 13. doi:10.1186/s40537-015-0018-zISSN 2196-1115PMC 4520712PMID 26236573.

41.  * Huang, Ronggui (2019-01-01). "Network fields, cultural identities and labor rights communities: Big data analytics with topic model and community detection". Chinese Journal of Sociology. 5 (1): 3–28. doi:10.1177/2057150X18820500ISSN 2057-150X.

42.  * Talley, Eric (2017-01-01). "Is the Future of Law a Driverless Car? Assessing How the Data Analytics Revolution Will Transform Legal Practice". Journal of Institutional & Theoretical Economics, Vol. 174, p 183, 2018.

43.  * Sarfaty, Galit (2017-01-01). "Can Big Data Revolutionize International Human Rights Law?". University of Pennsylvania Journal of International Law. 39 (1): 73. ISSN 1086-7872.

44.  * Ghofrani, Faeze; He, Qing; Goverde, Rob M. P.; Liu, Xiang (2018-05-01). "Recent applications of big data analytics in railway transportation systems: A survey". Transportation Research Part C: Emerging Technologies. 90: 226–246. doi:10.1016/j.trc.2018.03.010ISSN 0968-090X.

45.  * Amini, S.; Gerostathopoulos, I.; Prehofer, C. (2017-06-01). "Big data analytics architecture for real-time traffic control". 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS): 710–715. doi:10.1109/MTITS.2017.8005605.

46.  * Jeble, Shirish; Kumari, Sneha; Patil, Yogesh (2017-12-22). "Role of Big Data in Decision Making". Operations and Supply Chain Management: An International Journal. 11 (1): 36–44. doi:10.31387/oscm0300198.

47.  * Ray, Santosh; Saeed, Mohammed (2018), Alani, Mohammed M.; Tawfik, Hissam; Saeed, Mohammed; Anya, Obinna (eds.), "Applications of Educational Data Mining and Learning Analytics Tools in Handling Big Data in Higher Education", Applications of Big Data Analytics: Trends, Issues, and Challenges, Cham: Springer International Publishing, pp. 135–160, doi:10.1007/978-3-319-76472-6_7ISBN 978-3-319-76472-6, retrieved 2021-01-22

48.  * Gibson, David C.; Ifenthaler, Dirk (2017), Kei Daniel, Ben (ed.), "Preparing the Next Generation of Education Researchers for Big Data in Higher Education", Big Data and Learning Analytics in Higher Education: Current Theory and Practice, Cham: Springer International Publishing, pp. 29–42, doi:10.1007/978-3-319-06520-5_4ISBN 978-3-319-06520-5, retrieved 2021-01-22

49.  * Williamson, Ben (2017-05-01). "Who owns educational theory? Big data, algorithms and the expert power of education data science". E-Learning and Digital Media. 14 (3): 105–122. doi:10.1177/2042753017731238hdl:1893/26118ISSN 2042-7530.

50.  * Pencheva, Irina; Esteve, Marc; Mikhaylov, Slava Jankin (2020-01-01). "Big Data and AI – A transformational shift for government: So, what next for research?". Public Policy and Administration. 35 (1): 24–44. doi:10.1177/0952076718780537ISSN 0952-0767.

51.  * Bowers, Alex J. (2017-04-01). "Quantitative Research Methods Training in Education Leadership and Administration Preparation Programs as Disciplined Inquiry for Building School Improvement Capacity". Journal of Research on Leadership Education. 12 (1): 72–96. doi:10.1177/1942775116659462ISSN 1942-7751.

52.  * Huda, Miftachul; Maseleno, Andino; Atmotiyoso, Pardimin; Siregar, Maragustam; Ahmad, Roslee; Jasmi, Kamarul; Muhamad, Nasrul (2018-01-22). "Big Data Emerging Technology: Insights into Innovative Environment for Online Learning Resources". International Journal of Emerging Technologies in Learning (iJET). 13 (1): 23–36. ISSN 1863-0383.

53.  * Williams, Peter (2017-08-18). "Assessing collaborative learning: big data, analytics and university futures". Assessment & Evaluation in Higher Education. 42 (6): 978–989. doi:10.1080/02602938.2016.1216084ISSN 0260-2938.

54.  * Rienties, Bart; Lewis, Tim; McFarlane, Ruth; Nguyen, Quan; Toetenel, Lisette (2018-03-04). "Analytics in online and offline language learning environments: the role of learning design to understand student online engagement". Computer Assisted Language Learning. 31 (3): 273–293. doi:10.1080/09588221.2017.1401548ISSN 0958-8221.

55.  * Maldonado-Mahauad, Jorge; Pérez-Sanagustín, Mar; Kizilcec, René F.; Morales, Nicolás; Munoz-Gama, Jorge (2018-03-01). "Mining theory-based patterns from Big data: Identifying self-regulated learning strategies in Massive Open Online Courses". Computers in Human Behavior. 80: 179–196. doi:10.1016/j.chb.2017.11.011ISSN 0747-5632.

56.  * "Critical analysis of Big Data challenges and analytical methods". Journal of Business Research. 70: 263–286. 2017-01-01. doi:10.1016/j.jbusres.2016.08.001ISSN 0148-2963.

57.  * "Assessing the Quality of Service Using Big Data Analytics: With Application to Healthcare". Big Data Research. 4: 13–24. 2016-06-01. doi:10.1016/j.bdr.2015.10.001ISSN 2214-5796.

58.  * Gunasekaran, Angappa; Papadopoulos, Thanos; Dubey, Rameshwar; Wamba, Samuel Fosso; Childe, Stephen J.; Hazen, Benjamin; Akter, Shahriar (2017-01-01). "Big data and predictive analytics for supply chain and organizational performance". Journal of Business Research. 70: 308–317. doi:10.1016/j.jbusres.2016.08.004ISSN 0148-2963.

59.  * "Prescriptive analytics: Literature review and research challenges". International Journal of Information Management. 50: 57–70. 2020-02-01. doi:10.1016/j.ijinfomgt.2019.04.003ISSN 0268-4012.

60.  * Husain, Syed S.; Kalinin, Alexandr; Truong, Anh; Dinov, Ivo D. (2015-07-17). "SOCR data dashboard: an integrated big data archive mashing medicare, labor, census and econometric information". Journal of Big Data. 2 (1): 13. doi:10.1186/s40537-015-0018-zISSN 2196-1115PMC 4520712PMID 26236573.

61.  * Ali, S. M.; Gupta, N.; Nayak, G. K.; Lenka, R. K. (2016-12-01). "Big data visualization: Tools and challenges". 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I): 656–660. doi:10.1109/IC3I.2016.7918044.

62.  * Sedrakyan, Gayane; Mannens, Erik; Verbert, Katrien (2019-02-01). "Guiding the choice of learning dashboard visualizations: Linking dashboard design and data visualization concepts". Journal of Computer Languages. 50: 19–38. doi:10.1016/j.jvlc.2018.11.002ISSN 2590-1184.

63.  * Bumblauskas, Daniel; Nold, Herb; Bumblauskas, Paul; Igou, Amy (2017-01-01). "Big data analytics: transforming data to action". Business Process Management Journal. 23 (3): 703–720. doi:10.1108/BPMJ-03-2016-0056ISSN 1463-7154.

64.  * Mantzaris, Alexander V.; Walker, Thomas G.; Taylor, Cameron E.; Ehling, Dustin (2019-03-07). "Adaptive network diagram constructions for representing big data event streams on monitoring dashboards". Journal of Big Data. 6 (1): 24. doi:10.1186/s40537-019-0187-2ISSN 2196-1115.

65.  * Guo, Lei; Vargo, Chris J.; Pan, Zixuan; Ding, Weicong; Ishwar, Prakash (2016-06-01). "Big Social Data Analytics in Journalism and Mass Communication: Comparing Dictionary-Based Text Analysis and Unsupervised Topic Modeling". Journalism & Mass Communication Quarterly. 93 (2): 332–359. doi:10.1177/1077699016639231ISSN 1077-6990.

66.  * Baack, Stefan (2015-12-01). "Datafication and empowerment: How the open data movement re-articulates notions of democracy, participation, and journalism". Big Data & Society. 2 (2): 2053951715594634. doi:10.1177/2053951715594634ISSN 2053-9517.

67.  * Loebbecke, Claudia; Picot, Arnold (2015-09-01). "Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda". The Journal of Strategic Information Systems. 24 (3): 149–157. doi:10.1016/j.jsis.2015.08.002ISSN 0963-8687.

68.  * Agerri, Rodrigo; Artola, Xabier; Beloki, Zuhaitz; Rigau, German; Soroa, Aitor (2015-05-01). "Big data for Natural Language Processing: A streaming approach". Knowledge-Based Systems. 79: 36–42. doi:10.1016/j.knosys.2014.11.007ISSN 0950-7051.

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Opportunity to Improve for the Field of Technology


Dr. Tamaro J. Green


As the application of technology has become more commonplace, there may be opportunities to improve for technology companies to better be able to serve the community.  Recently, technology companies have been in the news for failures to treat employees fairly.  It may be considered quite distressing that in a time of crisis, companies that play a role in allowing operations to continue are not living up to their responsibilities to ensure the safety of their employees.  The employees are the heart of the company.  All employees should be treated fairly regardless of their individuality.  If technology companies continue to treat employees unfairly, the lesson learned from this crisis may be how to ensure that the next time a worldwide crisis occurs, how to ensure that labour is organized for fair treatment.

The crisis has shown some great resilience of people that have dedicated their lives to helping and healing others.  The frontline workers have risen to the occasion to help to overcome the global pandemic.  That selflessness should be matched with everyone taking personal responsibility to help to protect the frontline workers as well.   Personal responsibility may be one of the best defenses against the ills of the pandemic.  When we take personal responsibility in order to protect the safety of those that are working to maintain our health, we are taking collective responsibility to combat the pandemic.  The measure of our collective responsibility may reflect that the pandemic is easier to overcome than we may think.

In our personal and collective responsibility in handling the pandemic, technology companies should also reflect this commitment in an effort to support their employees that are also putting themselves at risk to provide a service to others.  Women and minority employees of companies should not be harassed during these times for the reason that everyone has a focus elsewhere.  Instead, they should be supported and encouraged.  It is a difficult time for everyone; however, leadership in technology companies can support recovery efforts by applying restraint from harassment of their employees.

The improvements that technology companies can make may start at the top.  Marketing the productivity of change does not meet the bar.  In the rapid communication of the world today, it is easy to discern through the best marketing efforts to distort the pain that people are enduring and reporting.  Leadership needs to make true, constructive efforts in protecting their employees in order to provide a safe workplace for all.  As these improvements take place, the next crisis, may be easier to endure.


Dr. Tamaro Green is a computer science researcher and the founder of TJG Web Services.  TJG Web Services, LLC is a consulting firm in the field of information technology.  Dr. Green writes on topics of privacy, security, and ethics in information technology and computer science.

TJG News Editorials are opinion pieces and do not necessarily express the opinion of TJG News.  To publish editorial pieces in TJG News send an email to



The Value of Ethics


Dr. Tamaro J. Green


The recent events in information technology raise the question of the value of ethics to technology corporations.  The role that ethics plays in the corporate social responsibility may be delivered through an external board or may be embedded within the structure of an organization.  Corporations may even have separate divisions that focus on ethics if they see it is necessary or they say it has a value.  If they can determine the value of ethics to the organization and their business model, they may be able to withstand from avoiding the concept of dividing ethics from the policies and procedures of the organization.

If the leading ethical researcher of artificial intelligence in the world is removed from a corporation, it may lead others to question the value of ethics to business.  Ethics may not be a commodity that can be bought or sold or a physical product that can be placed on a store shelf.  Yet, ethics has a role in the function of enterprise activity.  Ethics can increase the value of products.  For example, ethically sourced equipment may be of a higher value than equipment that is not ethically sourced.  However, if ethical processes and procedures are not incorporated in the core values of a corporation, it may be difficult to include ethics into the operations of the corporation.

Ethics may have a higher value than the actual products of the corporation.  The ease of building and developing technology products may fail in comparison to the ability to develop ethical virtues.  A new field of artificial intelligence may be necessary to provide the design and creation of value systems that have social benefit, ethical intelligence.  Ethical intelligence may present itself as the emerging curriculum of artificial intelligence.


Dr. Tamaro Green is a computer science researcher and the founder of TJG Web Services.  TJG Web Services, LLC is a consulting firm in the field of information technology.  Dr. Green writes on topics of privacy, security, and ethics in information technology and computer science.

TJG News Editorials are opinion pieces and do not necessarily express the opinion of TJG News.  To publish editorial pieces in TJG News send an email to



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II Data School

TJG NewsII Data School is the premier organization for bridging the world of academia with the world of practice. II Data School has four focus areas that highlight key research areas for contemporary issues. The four current research areas of II Data School are geographic information systems and climate change, economy and finance, information security, and last but not least health care and medicine. The research areas of focus for II Data School will grow and change over time. To learn more about the development or to participate in the formation of this organization, visit for more information.

Artificial intelligence and machine learning algorithms in financial trading markets

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Risk of cryptocurrencies in financial markets

TJG NewsAs the cryptocurrency markets start to show tremendous growth, one of the areas that may be of concern to financial regulators is the risk that these cryptocurrency markets place on existing financial systems. The rapidly rising value and market capitalization of these virtual currencies may eventually reach a point to where their volatility could influence other financial markets. Research into the potential risk of cryptocurrencies in financial markets may be an area of future research.

Image analysis in financial markets

TJG NewsImage analysis may potentially sever as a tool for large scale financial transaction analysis. Reducing large amounts of financial analysis to charts that can be analysed by image analysis may simplify modelling and simulations of historical and forecast financial data. Image analysis algorithms may facilitate large scale data analysis.

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TJG NewsTJG Web Services provides consulting and services for a wide range of industries including education, marketing, finance, technology, entertainment, media, and travel.

Big Data Platforms

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TJG NewsTJG Web Services provides research in big data platforms, programming languages, and training including Textual Analysis, Image Analysis, Financial Analysis, Knowledge Management Systems, and Cross platform and Internet of Things development

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