Preview

Труды Института системного программирования РАН

Расширенный поиск

Большие данные: аналитические решения, исследовательские задачи и тенденции

https://doi.org/10.15514/ISPRAS-2020-32(1)-10

Полный текст:

Аннотация

Термин «большие данные» относится к объемным коллекциям цифровых данных, генерируемых каждую секунду. Производимые наборы данных представлены в структурированном, полуструктурированном и неструктурированном форматах по всему миру, и их трудно анализировать с применением традиционных систем управления базами данных. В последнее время аналитика больших данных становится важной областью исследований из-за популярности Интернета и появления новых веб-технологий. Эта растущая область исследований представляет собой междисциплинарную деятельность, которая привлекает исследователей из различных областей. Исследователи проектируют, разрабатывают и внедряют инструменты, технологии, архитектуры и платформв для анализа этих больших объемов данных. Эта статья начинается с краткого введения в проблематику большие данные и связанные с ними концепции, включая основные характеристики больших данных, после чего обсуждаются наиболее важные открытые исследовательские проблемы и возникающие тенденции. Далее приводится обзор исследований в области аналитики больших данных, обсуждаются преимущества использования решений для больших данных и обсуждаются виды оценок, требуемых перед переходом с традиционных решений. Наконец, представлен обзор основных существующих приложений, обеспечивающий общую панораму аналитики больших данных.

Об авторах

Ноаман Мухаммед Али
Университет Порт-Саида, Египет; Санкт-Петербургский государственный университет, Россия
Египет
Ассистент на кафедре информационных технологий и систем университета Порт-Саида, аспирант кафедры информатики Санкт-Петербургского государственного университета


Борис Асенович Новиков
Национальный исследовательский университет «Высшая школа экономики»
Россия
Доктор физико-математических наук, профессор, кафедра информатики в НИУ ВШЭ, Санкт-Петербург


Список литературы

1. Ghani N.A., Hamid S., Hashem I.A.T., Ahmed E. Social Media Big Data Analytics: A Survey. Computers in Human Behavior, vol. 101, 2019, pp. 417-428.

2. Emani C.K., Cullot N., Nicolle C. Understandable Big Data: A Survey. Computer Science Review, vol. 17, 2015, pp. 70-81.

3. Stieglitz S., Mirbabaie M., Ross B., Neuberger C. Social Media Analytics – Challenges in Topic Discovery, Data Collection, and Data Preparation. International Journal of Information Management, vol. 39, 2018, pp. 156-168.

4. Tsai C.W., Lai C.F., Chao H.C., Vasilakos A.V. Big Data Analytics: A Survey. Journal of Big Data, vol. 2, no. 21, 2015, pp. 1-32.

5. Yadav K., Rautaray S.S, Pandey M. A Prototype for Sentiment Analysis Using Big Data Tools. In Proc. of the First International Conference on Computational Intelligence, Communications, and Business Analytics, 2017, vol. 775, pp. 103–117.

6. Eckroth J. A Course on Big Data Analytics. Journal of Parallel and Distributed Computing, vol. 118, no. 1, 2018, pp. 166-176.

7. Smirnova E., Ivanescu A., Bai J., Crainiceanu C.M. A Practical Guide to Big Data. Statistics & Probability Letters, vol. 136, 2018, pp. 25-29.

8. Siddiqa A., Hashem I.A.T., Yaqoob I., Marjani M., Shamshirband S., Gani A., Nasaruddin F.A Survey of Big Data Management: Taxonomy and State-of-the-Art. Journal of Network and Computer Applications, vol. 71, 2016, pp. 151-166.

9. Soufi A.M., El-Aziz A.A.A., Hefny H.A. A Survey on Big Data and Knowledge Acquisition Techniques. IPASJ International Journal of Computer Science (IIJCS), vol. 06, no. 07, 2018, pp. 15-29.

10. Halevi G., Moed H.F. The Evolution of Big Data as a Research and Scientific Topic: Overview of the Literature. Research Trends, no. 30, 2012, pp. 3-6.

11. Manyika J., Chui M., Brown B., Bughin J., Dobbs R., Roxburgh C., Byers A. H. Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute, 2011, 143 p.

12. Gartner Glossary: Big Data. Available at: https://www.gartner.com/en/information-technology/glossary/big-data, accessed 14.10.2019.

13. Chapter 1. Market and Business Drivers for Big Data Analytics. In Loshin D. Big Data Analytics, Morgan Kaufmann, 2013, pp. 1-9.

14. Chapter 1. Introduction to Big Data. In Krishnan K. Data Warehousing in the Age of Big Data, Morgan Kaufmann, 2013, pp. 3-14.

15. Davis K. Ethics of Big Data: Balancing Risk and Innovation. O’Reilly Media, 2012, 82 p.

16. Zikopoulos P.C., Eaton C., deRoos D., Deutsch T., Lapis G. Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. McGraw-Hill Osborne Media, 2012, 176 p.

17. Owais S. S., Hussein N. S. Extract Five Categories CPIVW from the 9V’s Characteristics of the Big Data. International Journal of Advanced Computer Science and Applications (IJACSA), vol. 7, no. 3, 2016, pp. 254-258.

18. Laney D. 3D Data Management: Controlling Data Volume, Velocity and Variety. Application Delivery Strategies, META Group Research Note, 2001.

19. Kemp S. Digital 2019: Internet Trends in Q3 2019. Available at: https://datareportal.com/reports/digital-2019-internet-trends-in-q3, accessed 06.11.2019.

20. Kemp S. Digital Trends 2019: Every Single Stat You Need to know About the Internet. Available at: https://thenextweb.com/contributors/2019/01/30/digital-trends-2019-every-single-stat-you-need-to-know-about-the-internet/, accessed 06.11.2019.

21. Reinsel D., Gantz J., Rydning J. The Digitization of the World: From Edge to Core, 2018, Available at: https://www.seagate.com/our-story/data-age-2025/, accessed 06.11.2019.

22. Hale J.L. More Than 500 Hours of Content Are Now Being Uploaded to YouTube Every Minute. Available at: https://www.tubefilter.com/2019/05/07/number-hours-video-uploaded-to-youtube-per-minute/, accessed 07.11.2019.

23. Mention.com. 2018 Twitter Report. Available at: https://mention.com/en/reports/twitter/, accessed 07.11.2019.

24. Wiener J., Bronson N. Facebook’s Top Open Data Problems. Available at: https://research.fb.com/blog/2014/10/facebook-s-top-open-data-problems/, accessed 07.11.2014.

25. Torrecilla J.L., Romob J. Data Learning from Big Data. Statistics and Probability Letters, vol. 136, 2018, pp. 15-19.

26. Osman A.M.S. A Novel Big Data Analytics Framework for Smart Cities. Future Generation Computer Systems, vol. 91, 2019. pp. 620-633.

27. Jin X., Wah B.W., Cheng X., Wang Y. Significance and Challenges of Big Data Research. Big Data Research, vol. 2, no. 2, 2015, pp. 59-64.

28. Reeve A. Chapter 21. Big Data Integration. In Reeve A. Managing Data in Motion, Morgan Kaufmann, 2013, pp. 141-156.

29. Dhupia B., Rani M.U. Research Challenges in Big Data Solutions in Different Applications. In Social Network Forensics, Cyber Security, and Machine Learning, Springer, 2019, pp. 105-116.

30. Baig M.I., Shuib L., Yadegaridehkordi E. Big Data Adoption: State of the Art and Research Challenges. Information Processing & Management, vol. 56, no. 6, 2019, article 102095.

31. Malik S.U.R., Khan S.U., Ewen S.J., Tziritas N., Kolodziej J., Zomaya A.Y., Madani S.A., Min-Allah N., Wang L., Xu C.-Z., Malluhi Q.M., Pecero J.E., Balaji P., Vishnu A., Ranjan R., Zeadally S., Li H. Performance Analysis of Data Intensive Cloud Systems Based on Data Management and Replication: A Survey. Distributed and Parallel Databases, vol. 34, no. 2, 2016, pp. 179-215.

32. Bellatreche L., Furtado P., Mohania M.K. Guest Editorial: A Special Issue in Physical Design for Big Data Warehousing and Mining. Distributed and Parallel Databases, vol. 34, no. 3, 2016, pp. 289–292.

33. Lakshman A., Malik P. Cassandra: A Decentralized Structured Storage System. ACM SIGOPS Operating Systems Review, vol. 44, no. 2, 2010, pp. 35-40.

34. Dean J., Ghemawat S. MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM, vol. 51, no. 1, 2008, pp. 107-113.

35. Rotondi Azevedo D.N., Parente de Oliveira J.M. Application of Data Mining Techniques to Storage Management and Online Distribution of Satellite Images. Studies in Computational Intelligence, vol. 169, 2009, pp. 1-15.

36. Agrawal D., Abbadi A.E., Antony S., Das S. Data Management Challenges in Cloud Computing Infrastructures. Lecture Notes in Computer Science, vol. 5999, 2010, pp. 1-10.

37. Buza K., Nagy G.I., Nanopoulos A. Storage-Optimizing Clustering Algorithms for High-Dimensional Tick Data. Expert Systems with Applications, vol. 41, no. 9, 2014, pp. 4148-4157.

38. Mateus R.C., Siqueira T.L.L., Times V.C., Ciferri R.R., de Aguiar Ciferri C.D. Spatial Data Warehouses and Spatial OLAP Come Towards the Cloud: Design and Performance. Distributed and Parallel Databases, vol. 34, no. 3, 2016, pp. 425–461.

39. Merino J., Caballero I., Rivas B., Serrano M., Piattini M. A Data Quality in Use Model for Big Data. Future Generation Computer Systems, vol. 63, 2016, pp. 123-130.

40. McGilvray D.. Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information. Morgan Kaufmann, 2008, 352 p.

41. Russom P. Data Quality in the Age of Big Data. Available at: https://tdwi.org/Articles/2019/04/19/DIQ-ALL-Data-Quality-in-the-Age-of-Big-Data.aspx?Page=1, accessed 18.11.2019.

42. SAS. Data Integration Déjà Vu: Big Data Reinvigorates DI - White Paper. Available at: https://www.sas.com/ru_ua/whitepapers/data-integration-deja-vu-107865.html, accessed 18.11.2019.

43. FlyData I. The 6 Challenges of Big Data Integration. Available at: https://www.flydata.com/the-6-challenges-of-big-data-integration/, accessed 18.11.2019.

44. Akusok A., Björk K.-M., Miche Y., Lendasse A. High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications. IEEE Access, vol. 3, 2015, pp. 1011–1025.

45. Ji C., Li Y., Qiu W., Jin Y., Xu Y., Awada U., Li K.,Qu W. Big Data Processing: Big Challenges and Opportunities. Journal of Interconnection Networks, vol. 13, no. 03 & 04, 2012, article 1250009.

46. White T. Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale, 4 ed. O’Reilly Media, Inc., 2015, 756 p.

47. Candela L., Castelli D., Pagano P. Managing Big Data through Hybrid Data Infrastructures. ERCIM News, no. 89, 2012, pp. 37-38.

48. Tao H., Bhuiyan M.Z.A., Rahman M.A., Wang G., Wang T., Ahmed M.M., Li J. Economic Perspective Analysis of Protecting Big Data Security and Privacy. Future Generation Computer Systems, vol. 98, 2019, pp. 660-671.

49. Tawalbeh L.A., Saldamli G. Reconsidering Big Data Security and Privacy in Cloud and Mobile Cloud Systems. Journal of King Saud University - Computer and Information Sciences, May 2019, 10 p. DOI: 10.1016/j.jksuci.2019.05.007

50. Kantarcioglu M., Xi B. Adversarial Data Mining: Big Data Meets Cyber Security. In Proc. of the ACM SIGSAC Conference on Computer and Communications Security, 2016, pp. 1866-1867.

51. Cavoukian A., Chibba M., Williamson G., Ferguson A. The Importance of ABAC to Big Data: Privacy and Context. The Privacy and Big Data Institute, Ryerson University, Toronto, Canada, 2015. Available at: https://www.ryerson.ca/content/dam/pbdce/papers/The-Importance-of-ABAC-to-Big-Data-05-2015.pdf, accessed 18.11.2019.

52. Talha M., Kalam A.A.E., Elmarzouqi N. Big Data: Trade-off between Data Quality and Data Security. Procedia Computer Science, vol. 151, 2019, pp. 916-922.

53. Xu L., Jiang C., Wang J., Yuan J., Ren Y. Information Security in Big Data: Privacy and Data Mining. IEEE Access, vol. 2, 2014, pp. 1149–1176.

54. Chardin B., Lacombe J.-M., Petit J.-M. Chronos A. NoSQL System on Flash Memory for Industrial Process Data. Distributed and Parallel Databases, vol. 34, no. 3, 2016, pp. 293-319.

55. Sivarajah U., Kamal M.M., Irani Z., Weerakkody V. Critical Analysis of Big Data Challenges and Analytical Methods. Journal of Business Research, vol. 70, 2017, pp. 263-286.

56. Ali S. M., Gupta N., Nayak G.K., Lenka R.K. Big Data Visualization: Tools and challenges. In Proc. of the 2nd International Conference on Contemporary Computing and Informatics, 2016, pp. 656-660.

57. Yang A., Troup M., Ho J.W.K. Scalability and Validation of Big Data Bioinformatics Software. Computational and Structural Biotechnology Journal, vol. 15, 2017, pp. 379-386.

58. Elgendy N., Elragal A. Big Data Analytics: A Literature Review Paper. Lecture Notes in Computer Science, vol. 8557, 2014, vol. 8557, pp. 214-227.

59. Shim J.P., French A.M., Guo C., Jablonski J. Big Data and Analytics: Issues, Solutions, and ROI. Communications of the Association for Information Systems, vol. 37, 2015, pp. 797-810.

60. Gandomi A., Haider M. Beyond the Hype: Big Data Concepts, Methods, and Analytics. International Journal of Information Management, vol. 35, no. 2, 2015, pp. 137-144.

61. Russom P. Big Data Analytics. TDWI Best Practices Report, Fourth Quarter, 2011. Available at: https://tdwi.org/research/2011/09/best-practices-report-q4-big-data-analytics.aspx, accessed 18.11.2019.

62. Jha A., Dave M., Madan S. A Review on the Study and Analysis of Big Data Using Data Mining Techniques. International Journal of Latest Trends in Engineering and Technology (IJLTET), vol. 6, no. 3, 2016, pp. 94-102.

63. Berman J.J. Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information. Morgan Kaufmann, 2013, p. 288.

64. van der Lans R. F. Analytics of Textual Big Data: Text Exploration of the Big Untapped Data Source. Independent Business Intelligence Analyst R20: Consultancy2013. Available at: http://www.data.net.ma/wp-content/uploads/2015/12/Analytics-of-Textual-Big-Data-Text-Exploration-of-the-Big-Untapped-Data-Source.pdf, accessed 18.11.2019.

65. Malaka I., Brown I. Challenges to the Organisational Adoption of Big Data Analytics: A Case Study in the South African Telecommunications Industry. In Proc, of the Annual Research Conference on South African Institute of Computer Scientists and Information Technologists, 2015, Article No. 27.

66. Lavalle S., Hopklns M.S., Lesser E., Shockley R., Kruschwitz N. Analytics: The New Path to Value. Research Report, Fall 2010, MIT Sloan Management Review and the IBM Institute for Business Value. Available at: https://sloanreview.mit.edu/projects/analytics-the-new-path-to-value/, accessed 18.11.2019.

67. Fahmideh M., Beydoun G. Big Data Analytics Architecture Design - An Application in Manufacturing Systems. Computers & Industrial Engineering, vol. 128, 2019, pp. 948-963.

68. Lopes C., Cabral B., Bernardino J. Personalization Using Big Data Analytics Platforms. In Proc. of the Ninth International C* Conference on Computer Science & Software Engineering, 2016, pp. 131-132.

69. White C., Research B. Using Big Data for Smarter Decision Making. BI Research, IBM Big Data & Analytics Hub, 2011. Available at: https://www.ibmbigdatahub.com/whitepaper/using-big-data-smarter-decision-making, accessed 18.11.2019.

70. Samosir R.S., Hendric H.L., Gaol F.L., Abdurachman E., Soewito B. Measurement Metric Proposed for Big Data Analytics System. In Proc. of the International Conference on Computer Science and Artificial Intelligence, 2017, pp. 265–269.

71. Chapter 2. Business Problems Suited to Big Data Analytics. In Loshin D. Big Data Analytics, Morgan Kaufmann, 2013, pp. 11-19.

72. Romary L. Data Management in the Humanities. ERCIM News, no. 89, 2012, p. 14.

73. Lianzhi L. Evaluation Model of Education Service Quality Satisfaction in Colleges and Universities Dependent on Classification Attribute Big Data Feature Selection Algorithm. In Proc. of the International Conference on Intelligent Transportation, Big Data & Smart City, 2019, pp. 645-649.

74. Li Y., Zhai X. Review and Prospect of Modern Education using Big Data. Procedia Computer Science, vol. 129, 2018, pp. 341-347.

75. Xiong Z., Zhi L., Jiang J. Research on Art Education Digital Platform Based on Big Data. In Proc. of the IEEE 4th International Conference on Big Data Analytics, 2019, pp. 208-211.

76. Kim Y.H., Ahn J.-H. A Study on the Application of Big Data to the Korean College Education System, Procedia Computer Science, vol. 91, 2016, pp. 855-861.

77. Santoso L.W., Yulia. Data Warehouse with Big Data Technology for Higher Education. Procedia Computer Science, vol. 124, 2017, pp. 93-99.

78. Ramos T.G., Machado J.C.F., Cordeiro B.P.V. Primary Education Evaluation in Brazil Using Big Data and Cluster Analysis. Procedia Computer Science, vol. 55, 2015, pp. 1031-1039.

79. Huang Y., Chen Z., Yu T., Huang X., Gu X. Agricultural Remote Sensing Big Data: Management and Applications. Journal of Integrative Agriculture, vol. 17, no. 9, 2018, pp. 1915-1931.

80. Sabarina K., Priya N. Lowering Data Dimensionality in Big Data for the Benefit of Precision Agriculture. Procedia Computer Science, vol. 48, 2015, pp. 548-554.

81. Klerkx L., Jakku E., Labarthe P. A Review of Social Science on Digital Agriculture, Smart Farming and Agriculture 4.0: New Contributions and A Future Research Agenda. NJAS – Wageningen Journal of Life Sciences, vol. 90-91, 2019, article 100315.

82. Gonzalez-Sanchez A., Frausto-Solis J., Ojeda-Bustamante W. Predictive Ability of Machine Learning Methods for Massive Crop Yield Prediction. Spanish Journal of Agricultural Research, vol. 12, no. 2, 2014, pp. 313-328.

83. Senthilvadivu S., Kiran S.V., Devi S.P., Manivannan S. Big Data Analysis on Geographical Segmentations and Resource Constrained Scheduling of Production of Agricultural Commodities for Better Yield. Procedia Computer Science, vol. 87, 2016, pp. 80-85.

84. Palanisamy V., Thirunavukarasu R. Implications of Big Data Analytics in Developing Healthcare Frameworks – A Review. Journal of King Saud University – Computer and Information Sciences, vol. 31, no. 4, 2019, pp. 415-425.

85. Patel J.A., Sharma P. Big Data for Better Health Planning, In Proc. of the International Conference on Advances in Engineering & Technology Research, 2014, pp. 1-5.

86. Pashazadeh A., Navimipour N.J. Big Data Handling Mechanisms in the Healthcare Applications: A Comprehensive and Systematic Literature Review. Journal of Biomedical Informatics, vol. 82, 2018, pp. 47-62.

87. Abouelmehdi K., Beni-Hssane A., Khaloufi H., Saadi M. Big Data Security and Privacy in Healthcare: A Review. Procedia Computer Science, vol. 113, 2017, pp. 73-80.

88. Kaur P., Sharma M., Mittal M. Big Data and Machine Learning Based Secure Healthcare Framework. Procedia Computer Science, vol. 132, 2018, pp. 1049-1059.

89. Khaloufi H., Abouelmehdi K., Beni-hssane A., Saadi M. Security Model for Big Healthcare Data Lifecycle. Procedia Computer Science, vol. 141, 2018, pp. 294-301.

90. United Nations, Department of Economic and Social Affairs, Population Division. World Urbanization Prospects: The 2018 Revision. Available at: https://population.un.org/wup/Publications/Files/WUP2018-Report.pdf, accessed 18.11.2019.

91. DeRen L., JianJun C., Yuan Y. Big Data in Smart Cities. Science China Information Sciences, vol. 58, no. 10, 2015, pp. 1-12.

92. Rathore M.M., Paul A., Ahmad A., Chilamkurthi N., Hong W.-H., Seo H. Real-Time Secure Communication for Smart City in High-Speed Big Data Environment. Future Generation Computer Systems, vol. 83, 2018, pp. 638-652.

93. Rathore M.M., Paul A., Hong W.-H., Seo H., Awan I., Saeed S. Exploiting IoT and Big Data Analytics: Defining Smart Digital City Using Real-Time Urban Data. Sustainable Cities and Society, vol. 40, 2018, pp. 600-610.

94. Hashem I.A.T., Chang V., Anuar N.B., Adewole K., Yaqoob I., Gani A., Ahmed E., Chiroma H. The Role of Big Data in Smart City. International Journal of Information Management, vol. 36, no. 5, 2016, pp. 748–758.

95. Lima C., Kimb K.-J., Maglio P.P. Smart Cities with Big Data: Reference Models, Challenges, and Considerations. Cities, vol. 82, 2018, pp. 86-99.

96. Pal D., Triyason T., Padungweang P. Big Data in Smart-Cities: Current Research and Challenges. Indonesian Journal of Electrical Engineering and Informatics, vol. 6, no. 4, 2018, pp. 351-360.

97. Allama Z., Dhunny Z.A. On Big Data, Artificial Intelligence and Smart Cities. Cities, vol. 89, 2019, pp. 80-91.

98. Doku R., Rawat DB. Chapter 8. Big Data in Cybersecurity for Smart City Applications. In Smart Cities Cybersecurity and Privacy, Rawat D.B., Ghafoor K.Z., eds. Elsevier, 2019, pp. 103-112.

99. Hayes M.A., Capretz M.A. Contextual Anomaly Detection Framework for Big Sensor Data. Journal of Big Data, vol. 2, 2015, article no. 2.

100. Goswami K., Park Y., Song C. Impact of Reviewer Social Interaction on Online Consumer Review Fraud Detection. Journal of Big Data, vol. 4, 2017, article no. 15,

101. Shalaginov A., Johnsen J.W., Franke K. Cyber Crime Investigations in the Era of Big Data. In Proc. of the IEEE International Conference on Big Data, 2017, pp. 3672-3676.

102. Pramanik M.I., Zhang W., Lau R.Y.K., Li C. A Framework for Criminal Network Analysis Using Big Data. In Proc. of the IEEE 13th International Conference on e-Business Engineering, 2016, pp. 17-23.

103. Hu J. Big Data Analysis of Criminal Investigations. In Proc. of the 5th International Conference on Systems and Informatics, 2018, pp. 649-654.

104. Vaughan G. Efficient Big Data Model Selection with Applications to Fraud Detection. International Journal of Forecasting, June 2018, https://doi.org/10.1016/j.ijforecast.2018.03.002.

105. Khan E.S., Azmi H., Ansari F., Dhalvelkar S. Simple Implementation of Criminal Investigation Using Call Data Records (CDRs) Through Big Data Technology. In Proc. of the International Conference on Smart City and Emerging Technology, 2018, pp. 1-5.

106. Zhao Q., Chen K., Li T., Yang Y., Wang X. Detecting Telecommunication Fraud by Understanding the Contents of A Call. Cybersecurity, vol. 1, no. 8, 2018, p. 12.

107. Chen Y.-J., Wu C.-H. On Big Data-Based Fraud Detection Method for Financial Statements of Business Groups. In Proc. of the 6th IIAI International Congress on Advanced Applied Informatics, 2017, pp. 986-987.

108. Makki S., Assaghir Z., Taher Y., Haque R., Hacid M.-S., Zeineddine H. An Experimental Study with Imbalanced Classification Approaches for Credit Card Fraud Detection. IEEE Access, vol. 7, 2019, pp. 93010-93022.

109. Zhou H., Sun G., Fu S., Jiang W., Xue J. A Scalable Approach for Fraud Detection in Online E-Commerce Transactions with Big Data Analytics. CMC: Computers, Materials & Continua, vol. 60, no. 1, 2019, pp. 179-192.

110. Herland M., Khoshgoftaar T.M., Bauder R.A. Big Data Fraud Detection Using Multiple Medicare Data Sources. Journal of Big Data, vol. 5, 2018, article no. 29.

111. Castaneda G., Morris P., Khoshgoftaar T.M. Maxout Neural Network for Big Data Medical Fraud Detection. In Proc. of the IEEE Fifth International Conference on Big Data Computing Service and Applications, 2019, pp. 357-362.

112. Castaneda G., Morris P., Khoshgoftaar T. M. Evaluation of Maxout Activations in Deep Learning Across Several Big Data Domains. Journal of Big Data, vol. 6, 2019, article no. 72.

113. Lnenicka M., Komarkova J. Developing A Government Enterprise Architecture Framework to Support the Requirements of Big and Open Linked Data with the Use of Cloud Computing. International Journal of Information Management, vol. 46, 2019, pp. 124-141.

114. Yang P., Xia H., Liu W., Li Z. Research on Government Integrity Evaluation Based on Big Data. In Proc. of the 2nd International Conference on Artificial Intelligence and Big Data, 2019, pp. 28-35.

115. LaBrie R.C., Steinke G.H., Li X., Cazier J.A. Big Data Analytics Sentiment: US-China Reaction to Data Collection by Business and Government. Technological Forecasting and Social Change, vol. 130, 2018, pp. 45-55.

116. Laude H. Chapter 6. France’s Governmental Big Data Analytics: From Predictive to Prescriptive Using R. In Federal Data Science: Transforming Government and Agricultural Policy Using Artificial Intelligence, Batarseh F.A., Yang R., eds. Academic Press, 2018, pp. 81-94.

117. Yan Z. Big Data and Government Governance. In Proc. of the International Conference on Information Management and Processing, 2018, pp. 111-114.

118. Aron J.L., Niemann B. Sharing Best Practices for the Implementation of Big Data Applications in Government and Science Communities. In Proc. of the IEEE International Conference on Big Data, 2014, pp. 8-10.

119. Hardy K., Maurushat A. Opening up Government Data for Big Data Analysis and Public Benefit. Computer Law & Security Review, vol. 33, no. 1, pp. 30-37.

120. Archenaa J., Anita E.A.M. A Survey of Big Data Analytics in Healthcare and Government. Procedia Computer Science, vol. 50, 2015, pp. 408-413.

121. Lee Y., Park S. Design of A Government Collaboration Service Map by Big Data Analytics. Procedia Computer Science, vol. 91, 2016, pp. 751-760.

122. Amado A., Cortez P., Rita P., Moro S. Research Trends on Big Data in Marketing: A Text Mining and Topic Modeling Based Literature Analysis. European Research on Management and Business Economics, vol. 24, no. 1, 2018, pp. 1-7.

123. [Saidali J., Rahich H., Tabaa Y., Medouri A. The Combination Between Big Data and Marketing Strategies to Gain Valuable Business Insights for Better Production Success. Procedia Manufacturing, vol. 32, 2019, pp. 1017-1023.

124. Akter S., Wamba S.F. Big Data Analytics in E-Commerce: A Systematic Review and Agenda for Future Research, Electronic Markets, vol. 26, no. 2, 2016, pp. 173-194.

125. Chong A.Y.L., Li B., Ngai E.W.T., Ch'ng E., Lee F. Predicting Online Product Sales Via Online Reviews, Sentiments, and Promotion Strategies: A Big Data Architecture and Neural Network Approach. International Journal of Operations & Production Management, vol. 36, no. 4, 2016, pp. 358-383.

126. Erevelles S., Fukawa N., Swayne L. Big Data Consumer Analytics and the Transformation of Marketing. Journal of Business Research, vol. 69, no. 2, 2016, pp. 897-904.

127. Jabbar A., Akhtar P., Dani S. Real-time Big Data Processing for Instantaneous Marketing Decisions: A Problematization Approach. Industrial Marketing Management, Sept. 2019, https://doi.org/10.1016/j.indmarman.2019.09.001.

128. Li T. Using Big Data Analytics to Build Prosperity Index of Transportation Market. In Proc. of the 4th ACM SIGSPATIAL International Workshop on Safety and Resilience, 2018, no. 17, p. 6.

129. See-To E.W.K., Ngai E.W.T. Customer Reviews for Demand Distribution and Sales Nowcasting: A Big Data Approach. Annals of Operations Research, vol. 270, no. 1-2, 2018, pp. 415–431.

130. Kumar A., Shankar R., Aljohani N.R. A Big Data Driven Framework for Demand-driven Forecasting with Effects of Marketing-mix Variables. Industrial Marketing Management, June 2019, https://doi.org/10.1016/j.indmarman.2019.05.003.

131. Zheng K., Zhang Z., Song B. E-Commerce Logistics Distribution Mode in Big-Data Context: A Case Analysis of JD.COM. Industrial Marketing Management, Oct. 2019. DOI: https://doi.org/10.1016/j.indmarman.2019.10.009.

132. Salehan M., Kim D.J. Predicting the Performance of Online Consumer Reviews: A Sentiment Mining Approach to Big Data Analytics. Decision Support Systems, vol. 81, 2016, pp. 30–40.

133. Malhotra D., Rishi O.P. An Intelligent Approach to Design of E-Commerce Metasearch and Ranking System Using Next-Generation Big Data Analytics. Journal of King Saud University - Computer and Information Sciences, Mar. 2018, https://doi.org/10.1016/j.jksuci.2018.02.015.

134. Wu P.-J., Lin K.-C. Unstructured Big Data Analytics for Retrieving E-Commerce Logistics Knowledge. Telematics and Informatics, vol. 35, no. 1, 2018, pp. 237-244.

135. Zhaoa Y., Xu X., Wang M. Predicting Overall Customer Satisfaction: Big Data Evidence From Hotel Online Textual Reviews. International Journal of Hospitality Management, vol. 76, 2019, pp. 111-121.

136. Liu X., Shin H., Burns A.C. Examining the Impact of Luxury Brand's Social Media Marketing on Customer Engagements: Using Big Data Analytics and Natural Language Processing. Journal of Business Research, May 2019, https://doi.org/10.1016/j.jbusres.2019.04.042.

137. Kauffmann E., Peral J., Gil D., Ferrández A., Sellers R., Mora H. A Framework for Big Data Analytics in Commercial Social Networks: A Case Study on Sentiment Analysis and Fake Review Detection for Marketing Decision-making. Industrial Marketing Management, Aug. 2019, https://doi.org/10.1016/j.indmarman.2019.08.003.


Для цитирования:


Али Н.М., Новиков Б.А. Большие данные: аналитические решения, исследовательские задачи и тенденции. Труды Института системного программирования РАН. 2020;32(1):181-204. https://doi.org/10.15514/ISPRAS-2020-32(1)-10

For citation:


Ali N.M., Novikov B.A. Big Data: Analytical Solutions, Research Challenges and Trends. Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS). 2020;32(1):181-204. https://doi.org/10.15514/ISPRAS-2020-32(1)-10

Просмотров: 101


Creative Commons License
Контент доступен под лицензией Creative Commons Attribution 4.0 License.


ISSN 2079-8156 (Print)
ISSN 2220-6426 (Online)