1. bookVolume 17 (2017): Edition 2 (June 2017)
Détails du magazine
Première parution
13 Mar 2012
4 fois par an
Accès libre

Review on Big Data & Analytics – Concepts, Philosophy, Process and Applications

Publié en ligne: 26 Jun 2017
Volume & Edition: Volume 17 (2017) - Edition 2 (June 2017)
Pages: 3 - 27
Détails du magazine
Première parution
13 Mar 2012
4 fois par an

1. Demchenko, Y., C. D. Laat, P. Membrey. Defining Architecture Components of the Big Data Ecosystem. – In: Proc. of International Conference Collaboration Technologies and Systems (CTS’14), Vol. 14, 2014, pp. 104-112.10.1109/CTS.2014.6867550Search in Google Scholar

2. Slavakis, K., G. B. Giannakis, G. Mateos. Modeling and Optimization for Big Data Analytics: (Statistical) Learning Tools for Our Era of Data Deluge. – IEEE Signal Processing Magazine, Vol. 31, 2014, pp. 18-31.10.1109/MSP.2014.2327238Search in Google Scholar

3. Sherman, R. Chapter 1 – The Business Demand for Data, Information, and Analytics. – Business Intelligence Guidebook, Morgan Kaufmann, Boston, 2015, pp. 3-19.10.1016/B978-0-12-411461-6.00001-0Search in Google Scholar

4. Linstedt, D., M. Olschimke. Chapter 1 – Introduction to Data Warehousing – In Data Vault 2.0, Morgan Kaufmann, Boston, 2016, pp. 1-15.10.1016/B978-0-12-802510-9.00001-5Search in Google Scholar

5. Sharma, S. Expanded Cloud Plumes Hiding Big Data Ecosystem. – Future Generation Computer Systems, Vol. 59, 2016, pp. 63-92.10.1016/j.future.2016.01.003Search in Google Scholar

6. Cohen, J., B. Dolan, M. Dunlap, J. M. Hellerstein, C. Welton. MAD Skills: New Analysis Practices for Big Data. – Proc. VLDB Endow, Vol. 2, 2009, pp. 1481-1492.10.14778/1687553.1687576Search in Google Scholar

7. Hu, H., Y. Wen, T. S. Chua, X. Li. Toward Scalable Systems for Big Data Analytics: A Technology Tutorial. – IEEE Access, Vol. 2, 2014, pp. 652-687.10.1109/ACCESS.2014.2332453Search in Google Scholar

8. Myerson, J. M. Cloud Computing Versus Grid Computing. 3 March 2009. http://www.ibm.com/developerworks/library/wa-cloudgrid/Search in Google Scholar

9. Alkhanak, E. N., S. P. Lee, R. Rezaei, R. M. Parizi. Cost Optimization Approaches for Scientific Workflow Scheduling in Cloud and Grid Computing: A Review, Classifications, and Open Issues. – Journal of Systems and Software, Vol. 113, 2016, pp. 1-26.10.1016/j.jss.2015.11.023Search in Google Scholar

10. The Digital Universe of Opportunities: Rich Data Increasing Value of the Internet of Things. – EMC Digital Universe with Research & Analysis by IDC. http://www.emc.com/leadership/digital-universe/2014iview/executive-summary.htmSearch in Google Scholar

11. Kim, L. Here’s What Happens in 60 Seconds on the Internet. 11 December 2015. http://smallbiztrends.com/2015/12/60-seconds-on-the-internet.htmlSearch in Google Scholar

12. Kart, N. H. L., F. Buytendijk. Survey Analysis: Big Data Adoption in 2013 Shows Substance behind the Hype. – Gartner’s 2013 Big Data Study, 2013.Search in Google Scholar

13. Contributors, W. Big Data. 12 March 2016. UTC. https://en.wikipedia.org/w/index.php?title=Big_data&oldid=709642525Search in Google Scholar

14. Ishwarappa, J. Anuradha. A Brief Introduction on Big Data 5Vs Characteristics and Hadoop Technology. – Procedia Computer Science, Vol. 48, 2015, pp. 319-324.10.1016/j.procs.2015.04.188Search in Google Scholar

15. Watson, H. J. Tutorial: Big Data Analytics: Concepts, Technology, and Applications. – Association for Informaiton Systems, Vol. 34, 2014, pp. 5-16.10.17705/1CAIS.03465Search in Google Scholar

16. Swan, M. Philosophy of Big Data: Expanding the Human-Data Relation with Big Data Science Services. – In: Proc. of First International IEEE Conference of Big Data Computing Service and Applications (BigDataService’2015), 2015, pp. 468-477.Search in Google Scholar

17. Farid, M., A. Roatis, I. F. Ilyas, H.-F. Hoffmann, X. Chu. CLAMS: Bringing Quality to Data Lakes. – In: Proc. of 2016 International Conference on Management of Data, San Francisco, California, USA, 2016, pp. 2089-2092.Search in Google Scholar

18. Don Kogan. Top 8 Bigdata Trends 2016. – White Paper, 2016.Search in Google Scholar

19. Rith, J., P. S. Lehmayr, K. Meyer-Wegener. Speaking in Tongues: SQL Access to NoSQL Systems. – In: Proc. of 29th Annual ACM Symposium on Applied Computing, Gyeongju, Republic of Korea, 2014, pp. 855-857.Search in Google Scholar

20. Gaitho, M. How Applications of Big Data Drive Industries. – Simplylearn. http://www.simplilearn.com/big-data-applications-in-industries-articleSearch in Google Scholar

21. Sherman, R. Chapter 15. Advanced Analytics. – In: Business Intelligence Guidebook. Boston, Morgan Kaufmann, 2015, pp. 375-402.10.1016/B978-0-12-411461-6.00015-0Search in Google Scholar

22. Gandomi, A., M. Haider. Beyond the Hype: Big Data Concepts, Methods, and Analytics. – International Journal of Information Management, Vol. 35, 2015, pp. 137-144.10.1016/j.ijinfomgt.2014.10.007Search in Google Scholar

23. Manyika, M. C. J., B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, A. H. Byers. Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute, June 2011.Search in Google Scholar

24. Vatrapu, R., R. R. Mukkamala, A. Hussain, B. Flesch. Social Set Analysis: A Set Theoretical Approach to Big Data Analytics. – IEEE Access, Vol. 4, 2016, pp. 2542-2571.10.1109/ACCESS.2016.2559584Search in Google Scholar

25. Ittoo, A., L. M. Nguyen, A. Van Den Bosch. Text Analytics in Industry: Challenges, Desiderata and Trends. – Computers in Industry, Vol. 78, 2016, pp. 96-107.10.1016/j.compind.2015.12.001Search in Google Scholar

26. Hermann, M., R. Klein. A Visual Analytics Perspective on Shape Analysis: State of the Art and Future Prospects. – Computers & Graphics, Vol. 53, Part A, 2015, pp. 63-71.10.1016/j.cag.2015.08.008Search in Google Scholar

27. González-Torres, A., F. J. García-Peñalvo, R. Therón-Sánchez, R. Colomo-Palacios. Knowledge Discovery in Software Teams by Means of Evolutionary Visual Software Analytics. – Science of Computer Programming, Vol. 121, 2016, pp. 55-74.10.1016/j.scico.2015.09.005Search in Google Scholar

28. Makonin, S., D. McVeigh, W. Stuerzlinger, K. Tran, F. Popowich. Mixed-Initiative for Big Data: The Intersection of Human + Visual Analytics + Prediction. – In: 2016 49th Hawaii International Conference on System Sciences (HICSS’16), 2016, pp. 1427-1436.Search in Google Scholar

29. Pääkkönen, P., D. Pakkala. Reference Architecture and Classification of Technologies, Products and Services for Big Data Systems. – Big DATA Research, Vol. 2, 2015, pp. 166-186.10.1016/j.bdr.2015.01.001Search in Google Scholar

30. Sun, N., J. G. Morris, J. Xu, X. Zhu, M. Xie. iCARE: A Framework for Big Data-Based Banking Customer Analytics. – IBM Journal of Research and Development, Vol. 58, 2014, pp. 4:1-4:9.10.1147/JRD.2014.2337118Search in Google Scholar

31. Batarseh, F. A., E. A. Latif. Assessing the Quality of Service Using Big Data Analytics: With Application to Healthcare. – Big Data Research, Vol. 4, 2016, pp. 13-24.10.1016/j.bdr.2015.10.001Search in Google Scholar

32. Archenaa, J., E. A. M. Anita. A Survey of Big Data Analytics in Healthcare and Government. – Procedia Computer Science, Vol. 50, 2015, pp. 408-413.10.1016/j.procs.2015.04.021Search in Google Scholar

33. Saraladevi, B., N. Pazhaniraja, P. V. Paul, M. S. S. Basha, P. Dhavachelvan. Big Data and Hadoop – a Study in Security Perspective. – Procedia Computer Science, Vol. 50, 2015, pp. 596-601.10.1016/j.procs.2015.04.091Search in Google Scholar

34. Uzunkaya, C., T. Ensari, Y. Kavurucu. Hadoop Ecosystem and Its Analysis on Tweets. – Procedia – Social and Behavioral Sciences, Vol. 195, 2015, pp. 1890-1897.10.1016/j.sbspro.2015.06.429Search in Google Scholar

35. Cassales, G. W., A. S. Charão, M. K. Pinheiro, C. Souveyet, L. A. Steffenel. Context-Aware Scheduling for Apache Hadoop over Pervasive Environments. – Procedia Computer Science, Vol. 52, 2015, pp. 202-209.10.1016/j.procs.2015.05.058Search in Google Scholar

36. Shyam, R., B. H. B. Ganesh, S. S. Kumar, P. Poornachandran, K. P. Soman. Apache Spark a Big Data Analytics Platform for Smart Grid. – Procedia Technology, Vol. 21, 2015, pp. 171-178.10.1016/j.protcy.2015.10.085Search in Google Scholar

37. Ma, Y., Y. Zhou, Y. Yu, C. Peng, Z. Wang, S. Du. A Novel Approach for Improving Security and Storage Efficiency on HDFS. – Procedia Computer Science, Vol. 52, 2015, pp. 631-635.10.1016/j.procs.2015.05.062Search in Google Scholar

38. Maitrey, S., C. K. Jha. MapReduce: Simplified Data Analysis of Big Data. – Procedia Computer Science, Vol. 57, 2015, pp. 563-571.10.1016/j.procs.2015.07.392Search in Google Scholar

39. Loshin, D. Chapter 7. Big Data Tools and Techniques. – In: Big Data Analytics. Boston, Morgan Kaufmann, 2013, pp. 61-72.10.1016/B978-0-12-417319-4.00007-7Search in Google Scholar

40. Yildiz, O., S. Ibrahim, G. Antoniu. Enabling Fast Failure Recovery in Shared Hadoop Clusters: Towards Failure-Aware Scheduling. – Future Generation Computer Systems, 2016.10.1016/j.future.2016.02.015Search in Google Scholar

41. Apache Hive TM. https://hive.apache.org/Search in Google Scholar

42. Chennamsetty, H., S. Chalasani, D. Riley. Predictive Analytics on Electronic Health Records (EHRs) Using Hadoop and Hive. – In: 2015 IEEE International Conference Electrical, Computer and Communication Technologies (ICECCT’15), 2015, pp. 1-5.10.1109/ICECCT.2015.7226129Search in Google Scholar

43. Xu, Y., S. Hu. QMapper: A Tool for SQL Optimization on Hive Using Query Rewriting. – In: Proc. of 22nd International Conference on World Wide Web, Rio De Janeiro, Brazil, ACM, Vol. 1, 2013, pp. 211-212.Search in Google Scholar

44. Apache Pig. https://pig.apache.org/Search in Google Scholar

45. Rajurkar, G. D., R. M. Goudar. Notice of Violation of IEEE Publication Principles, A Speedy Data Uploading Approach for Twitter Trend and Sentiment Analysis Using HADOOP. – In: International Conference on Computing Communication Control and Automation (ICCUBEA’15), Vol. 1, 2015, pp. 580-584.10.1109/ICCUBEA.2015.119Search in Google Scholar

46. Apache Flume. https://flume.apache.org/Search in Google Scholar

47. Apache Sqoop. http://sqoop.apache.org/Search in Google Scholar

48. Apache Spark. http://spark.apache.org/Search in Google Scholar

49. Li, H., K. Lu, S. Meng. Bigprovision: A Provisioning Framework for Big Data Analytics. – IEEE Network, Vol. 29, 2015, pp. 50-56.10.1109/MNET.2015.7293305Search in Google Scholar

50. Reyes-Ortiz, J. L., L. Oneto, D. Anguita. Big Data Analytics in the Cloud: Spark on Hadoop vs MPI/OpenMP on Beowulf. – Procedia Computer Science, Vol. 53, 2015, pp. 121-130.10.1016/j.procs.2015.07.286Search in Google Scholar

51. Elia, D., S. Fiore, A. D’Anca, C. Palazzo, I. Foster, D. N. Williams. An In-Memory Based Framework for Scientific Data Analytics. – In: Proc. of ACM International Conference on Computing Frontiers, 2016, pp. 424-429.10.1145/2903150.2911719Search in Google Scholar

52. Apache ZooKeeper™. https://zookeeper.apache.org/Search in Google Scholar

53. Lin, H.-K., J. A. Harding, C.-I. Chen. A Hyperconnected Manufacturing Collaboration System Using the Semantic Web and Hadoop Ecosystem System. – Procedia CIRP, Vol. 52, 2016, pp. 18-23.10.1016/j.procir.2016.07.075Search in Google Scholar

54. Plase, D., L. Niedrite, R. Taranovs. Accelerating Data Queries on Hadoop Framework by Using Compact Data Formats. – In: 4th IEEE Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE’16), 2016, pp. 1-7.10.1109/AIEEE.2016.7821807Search in Google Scholar

55. Splice Machine. http://www.splicemachine.com/product/Search in Google Scholar

56. Wang, K., J. Mi, C. Xu, L. Shu, D. J. Deng. Real-Time Big Data Analytics for Multimedia Transmission and Storage. – In: IEEE/CIC International Conference on Communications in China (ICCC’16), 2016, pp. 1-6.10.1109/ICCChina.2016.7636815Search in Google Scholar

57. Golov, N., L. Rönnbäck. Big Data Normalization for Massively Parallel Processing Databases. Computer Standards & Interfaces Available Online, 2017. ISSN 0920-5489.10.1016/j.csi.2017.01.009Search in Google Scholar

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