Bulut bilişimde veri madenciliği tekniklerinin uygulanması: Bir literatür taraması

Abstract

Son yıllarda, bilgi ve iletişim teknolojilerindeki yenilikler ve gelişmeler, analiz edilmesi gereken veri miktarını önemli derecede artırmıştır. Büyük ölçekli verilerin saklanması, yüksek hacimli verilerden veri madenciliği teknikleri ile yararlı bilgilerin çıkartılması ve geleceğin tahminlenmesi maliyetli ve zorlu işlemlerdir. Bu zorlukların üstesinden gelebilmek için, bilgi keşfi süreci; bulut bilişim, paralel ve dağıtık hesaplama kullanılarak etkin bir şekilde gerçekleştirilebilmektedir. Bu makale, bulut bilişimin ölçeklenebilirliği sayesinde veri madenciliği algoritmalarının performanslarının arttırılabileceğini, ayrıca her yerden ulaşılabilirlik, düşük maliyet ve kolay yönetilebilirlik avantajlarının sağlanabileceğini göstermektedir. Makalede, bulut platformunda gerçekleştirilen veri madenciliği uygulamaları; kullanılan metotlar, veriler ve elde edilen sonuçlar çerçevesinde sunulmaktadır. Literatürde, bu konuda önerilen çözüm yaklaşımları; sınıflandırma, kümeleme ve birliktelik kuralları analizi olmak üzere üç ana kategori altında ele alınmaktadır.

Keywords

• Veri madenciliği,Sınıflandırma,Kümeleme,Birliktelik kuralları analizi,Bulut bilişim

Application of data mining techniques in cloud computing: A literature review

Abstract

In recent years, new innovations and developments in information and communication technologies have hugely increased the quantity of data required to analyze. Storing large-scale datasets, extracting useful knowledge from the huge volumes of data by applying data mining techniques and predicting the future are costly and difficult processes. To overcome these challenges, the knowledge discovery process is performed efficiently by using cloud, parallel and distributed computing. This article shows that the performance algorithms in data mining can be increased by the scalability of cloud computing with the advantages in terms of accessibility from anywhere, low cost and maintainability. In this article, data mining applications that have been implemented on cloud platforms are presented, including methods, data and obtained results. Solution approaches that have been proposed related to this topic in the literature are handled in three main categories: classification, clustering and association rule mining.

Keywords

• Data mining,Classification,Clustering,Association rule mining,Cloud computing

References

1. Petre R-Ş. “Data mining in cloud computing”. Database Systems Journal, 3(3), 67-71, 2012.
2. Low Y, Gonzalez J, Kyrola A, Bickson D, Guestrin C, Hellerstein JM. “Distributed GraphLab: A framework for machine learning and data mining in the cloud”. Proceedings of the VLDB Endowment, 5(8), 716-727, 2012.
3. Talia D, Trunfio P. “How distributed data mining tasks can thrive as knowledge services”. Communications of the ACM, 53(7), 132-137, 2010.
4. Kholod I, Kuprianov M, Petukhov I. Parallel and Distributed Data Mining in Cloud Advances in Data Mining. Editor: Perner P. Advances in Data Mining. Applications and Theoretical Aspects, 349-362, New York, NY, USA, Springer, 2016.
5. Olaide AA. “On modeling confidentiality archetype and data mining in cloud computing”. African Journal of Computing & ICT, 6(1), 79-86, 2013.
6. Kamala B, “A study on integrated approach of data mining and cloud mining”. International Journal of Advances in Computer Science and Cloud Computing, 1(2), 35-38, 2013.
7. Fernández A, Peralta D, Benítez JM, Herrera F. “E-learning and educational data mining in cloud computing: an overview”. International Journal Learning Technology, 9(1), 25-52, 2014.
8. Anand A, Nithin Chandran R, Abhijith T, Varun KK. “Data mining over encrypted data for patient records”. International Journal on Applications in Engineering and Technology, 2(4), 13-16, 2016.

9. Dahmani D, Rahal SA, Belalem G. “Improving the performance of data mining by using big data in cloud environment”. Journal of Information & Knowledge Management, 2016.
10. Belcastro L, Marozzo F, Talia D, Trunfio P. “Using scalable data mining for predicting flight delays”. ACM Transactions on Intelligent Systems and Technology, 8(1), 2016.
11. Vrbic R. “Data mining and cloud computing”, Journal of Information Technology and Applications, 2(2), 75-87, 2012.
12. Kamdar AB, Jagani JM. “A survey: classification of huge cloud datasets with efficient map-reduce policy”. International Journal of Engineering Trends and Technology (IJETT), 18(2), 103-107, 2014.
13. Ayma VA, Ferreira RS, Happ P, Oliveira D, Feitosa R, Costa G, Plaza A, Gamba P. “Classification algorithms for big data analysis, a Map Reduce approach”. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-3(W2), 17-21, 2015.
14. Zhengqiao X, Dewei Z. “Research on clustering algorithm for massive data based on Hadoop platform”. International Conference on Computer Science and Service System, Nanjing, China, 11-13 August 2012.
15. Mahendra TV, Deepika N, Rao NK. “Data mining for high performance data cloud using association rule mining”. International Journal of Advanced Research in Computer Science and Software Engineering, 2(1), 1-6, 2012.
16. Yuan B, Herbert J. “A cloud-based mobile data analytics framework: case study of activity recognition using smartphone”. 2nd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, Oxford, UK, 2014.
17. Rahulamathavan Y, Phan RCW, Veluru S, Cumanan K, Rajarajan. “Privacy-Preserving multi-class support vector machine for outsourcing the data classification in cloud”. IEEE Transactions on Dependable and Secure Computing, 11(5), 467-479, 2014.
18. Lvshuhong. “Improved SVM in cloud computing information mining”. International Journal of Grid Distribution Computing, 8(1), 33-40, 2015.
19. Dai Y, Sun H. “The Naive Bayes text classification algorithm based on rough set in the cloud platform”. Journal of Chemical and Pharmaceutical Research, 6(7), 1636-1643, 2014.
20. Zhou L, Wang H, Wang W. “Parallel implementation of classification algorithms based on cloud computing environment”. Indonesian Journal of Electrical Engineering, 10(5), 1087-1092, 2012.
21. Tan AX, Liu VL, Kantarcioglu M, Thuraisingham B. “A comparison of approaches for large-scale data mining-utilizing MapReduce in large-scale data mining”. Department of Computer Science, The University of Texas at Dallas, Dallas, Texas, Technical Report, UTDCS-24-10, 2010.
22. Sarnovsky M, Kacur T. “Cloud-based classification of text documents using the Gridgain platform”. 7th IEEE International Symposium on Applied Computational Intelligence and Informatics, Timisoara, Romania, 24-26 May 2012.
23. Quirita VAA, Costa GAOP, Happ PN, Feitosa RQ, Ferreira RS, Oliveira DAB, Plaza A. “A new cloud computing architecture for the classification of remote sensing data”. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 99, 1-8, 2016.
24. Pranckevičius T. “Investigation of cloud computing technology on the visualisation and classification algorithms, Institute of Mathematics and Informatics, Vilnius University, Vilnius, Lithuania, Technical Report, MII-DS-07T-13-5, 2013.
25. Ding J, Yang S. “Classification rules mining model with genetic algorithm in cloud computing”. International Journal of Computer Applications, 48(18), 24-32, 2012.
26. Yuechao W, Shihe C. “An approach to smart grid online data mining based on cloud computing”. International Journal of Simulation Systems, Science & Technology, 17(2), 1-5, 2016.
27. Samanthula BK, Elmehdwi Y, Jiang W. “K-nearest neighbor classification over semantically secure encrypted relational data”. IEEE Transactions on Knowledge and Data Engineering, 27(5), 1261-1273, 2015.
28. Li Z, Song X, WenhuiZhu, YanxiaChen. “K-means clustering optimization algorithm based on MapReduce”. International Symposium on Computers & Informatics (ISCI 2015), Beijing, China, 17-18 January 2015.
29. Rallapalli S, Gondkar RR, Rao GVM. Cloud Based K-Means Clustering Running as a MapReduce Job for Big Data Healthcare Analytics Using Apache Mahout. Editors: Satapathy SC, Mandal JK, Udgata SK, Bhateja V. Advances in Intelligent Systems and Computing, 127-135, Springer, 2016.
30. Haut JM, Paoletti M, Plaza J, Plaza A. “Cloud implementation of the k-means algorithm for hyperspectral image analysis”. The Journal of Supercomputing, 73(1), 514-529, 2016.
31. Kamalraj N, Malathi A. “Hadoop operations management for big data clusters in telecommunication industry”. International Journal of Computer Applications, 105(12), 40-44, 2014.
32. Golghate AA, Shende SW. “Parallel k-means clustering based on Hadoop and Hama”. International Journal of Computing and Technology, 1(3), 33-37, 2014.
33. Zhang D, Shou Y. An Improved Parallel K-Means Algorithm Based on Cloud Computing. Editors: Li K, Li J, Liu Y, Castiglione A. Computational Intelligence and Intelligent Systems, 312-320, Springer, 2016.
34. Srivastava K, Shah R, Valia D, Swaminarayan H. “Data mining using hierarchical agglomerative clustering algorithm in distributed cloud computing environment”. International Journal of Computer Theory and Engineering, 5(3), 520-522, 2013.
35. Chen C-C, Chen M-S. “HiClus: Highly Scalable Density-based Clustering with Heterogeneous Cloud”. Procedia Computer Science, 53, 149-157, 2015.
36. Masih S, Tanwani S. “Distributed framework for data mining as a service on private cloud”. International Journal of Engineering Research and Applications, 4(11), 65-70, 2014.
37. Zhong L, Tang K, Li L, Ye J. “An improved clustering algorithm of tunnel monitoring data for cloud computing”. The Scientific World Journal, 2014, 1-6, 2014.
38. Kumari MC, Babu PN. “Survey on clustering on the cloud by using map reduce in large data applications”. International Journal of Engineering Trends and Technology (IJETT), 21(8), 392-395, 2015.
39. Nappina V, Revathi N. “Data mining over large datasets using Hadoop in cloud environment”. International Journal of Computer Science & Communication Networks, 3(2), 73-78, 2013.
40. Kamalraj R, Kannan AR, Vaishnavi S, Suganya V. “A data mining based approach for introducing products in saas (software as a service)”. International Journal of Engineering Innovation & Research, 1(2), 210-214, 2012.
41. Liang Z, Ploderer B, Martell MAC, Nishimura T. A Cloud-Based Intelligent Computing System for Contextual Exploration on Personal Sleep-Tracking Data Using Association Rule Mining. Editors: Martin-Gonzalez A, Uc-Cetina V. Intelligent Computing Systems, 83-96, Mérida, México, Springer, 2016.
42. Farzanyar Z, Cercone N. “Efficient mining of frequent itemsets in social network data based on MapReduce framework”. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Niagar Falls, ON, Canada, 25-28 August 2013.
43. Lal K, Mahanti NC. “A novel data mining algorithm for semantic web based data cloud”. International Journal of Computer Science and Security, 4(2), 160-175, 2010.
44. Zheng X, Wang S, “Study on the method of road transport management information data mining based on pruning Eclat algorithm and MapReduce”. Procedia-Social and Behavioral Sciences, 138, 757-766, 2014.
45. Chuchra R, Jindal M, Mehta B. “Role of component based systems in data mining & cloud computing”. International Journal of Emerging Technology and Advanced Engineering, 3(5), 513-517, 2013.
46. Yu H, Wang D. “Mass log data processing and mining based on Hadoop and cloud computing”. 7th International Conference on Computer Science & Education, Xiamen, Fujian, China, 14-17 July 2012.
47. Wang Z, Li H. “Research of massive web log data mining based on cloud computing”. International Conference on Computational and Information Sciences, Shiyan, Hubai, China, 21-23 June 2013.
48. Apiletti D, Baralis E, Cerquitelli T, Chiusano S, Grimaudo L. “SeARuM: A cloud-based service for association rule mining”. 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, Melbourne, Australia, 16-18 July 2013.
49. Chen M, Chiang IJ, Lai CW. “Frequent pattern mining for price fluctuation based on cloud computing”. IEEE International Conference on Granular Computing, Hangzhou, China, 11-13 August 2012.
50. Yi X, Rao FY, Bertino E, Bouguettaya A. “Privacy-Preserving association rule mining in cloud computing”. 10th ACM Symposium on Information, New York, NY, USA, Singapore, 14-17 April 2015.
51. Li L, Zhang M. “The strategy of mining association rule based on cloud computing”. International Conference on Business Computing and Global Informatization, Shanghai, China, 29-31 July 2011.
52. Wang SQ, Yang YB, Chen GP, Gao Y, Zhang Y. “MapReduce-based closed frequent itemset mining with efficient redundancy filtering”. 12th International Conference on Data Mining Workshops, Brussels, Belgium,10-10 December 2012.
53. Sahay S, Khetarpal S, Pradhan T. “Hybrid data mining algorithm in cloud computing using MapReduce framework”. International Conference on Advanced Communication Control & Computing (ICACCCT). India, 25-27 May 2016.
54. Wu Z, Cao J, Fang C, “Data cloud for distributed data mining via pipelined MapReduce”. 8th International Workshop, ADMI 2012, Valencia, Spain, 4-5 June 2012.