A scalable machine learning online service for big data real-time analysis

IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIBD 2014: 2014 IEEE Symposium on Computational Intelligence in Big Data, Proceedings

Volumn , Issue , 2015, Pages

  Baldominos, Alejandro ^a   Albacete, Esperanza ^a   Saez, Yago ^a   Isasi, Pedro ^a  

^a UNIVERSIDAD CARLOS III DE MADRID   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ARCHITECTURE; BIG DATA; E-LEARNING; FORECASTING;

BUSINESS PERFORMANCE; CONCURRENT REQUESTS; DOMAIN INDEPENDENTS; PREDICTION SYSTEMS; PROPOSED ARCHITECTURES; REAL-TIME ANALYTICS; REAL-TIME PREDICTION; SCALABLE MACHINE LEARNING;

MACHINE LEARNING;

EID: 84923233633     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIBD.2014.7011537     Document Type: Conference Paper

References (58)

1. J. Dean and S. Ghemawat, " MapReduce: Simplified Data Processing on Large Clusters, " in Proc. 6th Symp. Operating Syst. Des. and Impl. (OSDI'04), 2004, pp. 137-150.
2. H. Hu, Y. Wen, T. Chua, and X. Li, " Toward Scalable Systems for Big Data Analytics: a Technology Tutorial, " IEEE Access, vol. 2, pp. 652-687, 2014.
3. K. Shvachko, " The Hadoop Distributed File System, " in Proc. 2010 IEEE 26th Symp. Mass Storage Syst. and Technol. (MSST'10), 2010, pp. 1-10.
4. S. Ghemawat, H. Gobioff, and S.-T. Leung, " The Google File System, " in Proc. 19th ACM Symp. Operating Syst. Principles (SOSP'03), 2003, pp. 29-43.
5. F. Chang, J. Dean, S. Ghemawat, W. C. Hsieh, D. A. Wallach, M. Burrows, T. Chandra, A. Fikes, and R. E. Gruber, " Bigtable: A Distributed Storage System for Structured Data, " in Proc. 7ty Symp. Operating Syst. Des. and Impl. (OSDI'06), 2006, pp. 205-218.
6. A. Thusoo, J. S. Sarma, N. Jain, Z. Shao, P. Chakka, S. Anthony, H. Lui, P. Wyckoff, and R. Murthy, " Hive-A Warehousing Solution Over a Map-Reduce Framework, " Proc. VLDB Endowment, vol. 2, no. 2, pp. 1626-1629, 2009.
7. A. F. Gates, O. Natkovich, S. Chopra, P. Kamath, S. M. Narayanamurthy, C. Olston, B. Reed, S. Srinivasan, and U. Srivastava, " Building a High-Level Dataflow System on Top of Map-Reduce: the Pig Experience, " Proc. VLDB Endowment, vol. 2, no. 2, pp. 1414-1425, 2009.
8. C. Olston, B. Reed, U. Srivastava, R. Kumar, and A. Tomkins, " Pig Latin: a Not-so-Foreign Language for Data Processing, " in Proc. 2008 ACM SIGMOD Int. Conf. Manag. of Data (SIGMOD'08), 2008, pp. 1099-1110.
9. G. Zhang, C. Li, Y. Zhang, C. Xing, and J. Yang, " MapReduce++: Efficient Processing of MapReduce Jobs in the Cloud, " J. of Comput. Inf. Syst., vol. 8, no. 14, pp. 5757-5764, 2012.
10. Y. Low, D. Bickson, J. Gonzalez, C. Guestrin, A. Kyrola, and J. M. Hellerstein, " Distributed GraphLab: a Framework for Machine Learning and Data Mining in the Cloud, " Proc. VLDB Endowment, vol. 5, no. 8, pp. 716-727, 2012.
11. M. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker, and I. Stoica, " Spark: Cluster Computing with Working Sets, " in Proc. 2nd USENIX Conf. Hot Topics in Cloud Comput. (HotCloud'10), 2010.
12. J. Lin and A. Kolcz, " Large-Scale Machine Learning at Twitter, " in Proc. 2012 ACM SIGMOD Int. Conf. Manag. of Data (SIGMOD'12), 2012, pp. 793-804.
13. R. Steves, " K-means Clustering in the Cloud-A Mahout Test, " in Proc. 2011 IEEE Workshops of Int. Conf. Advanced Inf. Netw and Appl. (WAINA'11), 2012, pp. 514-519.
14. L. Ma, E. Haihong, and K. Xu, " The Design and Implementation of Distributed Mobile Points of Interest (POI) based on Mahout, " in Proc. 6th Int. Conf. Perv. Comp. and Apps. (ICPCA'11), 2011, pp. 99-104.
15. C.-R. Lee, T. Hua, and Y.-F. Chang, " Enhancing Accuracy and Performance of Collaborative Filtering Algorithm by Stochastic SVD and its MapReduce Implementation, " in Proc. 2013 IEEE 27th Int. Conf. Parallel and Distrib. Process. Symp. Workshops & PhD Forum (IPDPSW'13), 2013, pp. 1869-1878.
16. R. Irudeen and S. Samaraweera, " Big Data Solutions for Sri Lankan Development: A Case Study from Travel and Tourism, " in Proc. 2013 Int. Conf. Advances in ICT for Emerging Regions (ICTer'13), 2013, pp. 207-216.
17. B. Liu, E. Blasch, Y. Chen, D. Shen, and G. Chen, " Scalable Sentiment Classification for Big Data Analysis using Naive Bayes Classifier, " in Proc. 2013 IEEE Int. Conf. Big Data, 2013, pp. 99-104.
18. T. Armes and M. Refern, " Using Big Data and Predictive Machine Learning in Aerospace Test Environments, " in Proc. 2013 IEEE AUTOTESTCON, 2013, pp. 301-305.
19. L. Li, S. Bagheri, H. Goot, A. Hasan, and G. Hazard, " Risk Adjustment of Patient Expenditures: A Big Data Analytics Approach, " in Proc. 2013 IEEE Int. Conf. Big Data, 2013, pp. 12-14.
20. P. Huijse, P. Estevez, P. Protopapas, J. Principe, and P. Zegers, " Computational Intelligence Challenges and Appl. on Large-Scale Astronomical Time Series Databases, " IEEE Comput. Intell. Magazine, vol. 9, no. 3, pp. 27-39, 2014.
21. J. Shi, W. Xue, W. Wang, and Y. Zhang, " Scalable Community Detection in Massive Social Networks using MapReduce, " IBM J. of Research and Develop., vol. 57, no. 3, 2013.
22. K. Hammond and A. Varde, " Cloud Based Predictive Analytics: Text Classification, Recommender Systems and Decision Support, " in Proc. 2013 IEEE 13th Int. Conf. Data Mining Workshops (ICDMW'13), 2013, pp. 607-612.
23. T. Condie, P. Mineiro, N. Polyzotis, and M. Weimer, " Machine Learning on Big Data, " in Proc. 2013 IEEE 29th Int. Conf. Data Eng. (ICDE'13), 2013, pp. 1242-1244.
24. E. Sparks, A. Talwalkar, V. Smith, J. Kottalam, X. Pan, J. Gonzalez, M. Franklin, M. Jordan, and T. Kraska, " MLI: An API for Distributed Machine Learning, " in Proc. 2013 IEEE 13th Int. Conf. Data Mining (ICDM'13), 2013, pp. 1187-1192.
25. D. Abadi, D. Carney, U. C, etintemel, M. Cherniack, C. Convey, S. Lee, M. Stonebraker, N. Tatbul, and S. Zdonik, " Aurora: A New Model and Architecture for Data Stream Management, " The Int. J. on Very Large Data Bases, vol. 12, no. 2, pp. 120-139, 2003.
26. A. Arasu, B. Babcock, S. Babu, J. Cieslewicz, K. Ito, R. Motwani, U. Srivastava, and J. Widom, " STREAM: The Stanford Data Stream Management System, " Stanford InfoLab, Tech. Rep., 2004.
27. D. J. Abadi, Y. Ahmad, M. Balazinska, M. Cherniack, J. hyon Hwang, W. Lindner, A. S. Maskey, E. Rasin, E. Ryvkina, N. Tatbul, Y. Xing, and S. Zdonik, " The Design of the Borealis Stream Processing Engine, " in Proc. 2nd Biennial Conf. Innovative Data Syst. Research (CIDR'05), 2005, pp. 277-289.
28. L. Amini, H. Andrade, R. Bhagwan, F. Eskesen, R. King, Y. Park, and C. Venkatramani, " SPC: A Distributed, Scalable Platform for Data Mining, " in Proc. 4th Int. Workshop on Data Mining Standards, Services and Platforms (DMSSP'06), 2006, pp. 27-37.
29. B. Gedik, P. S. Yu, H. Andrade, M. Doo, and K. lung Wu, " SPADE: The System S Declarative Stream Processing Engine, " in Proc. 2008 ACM SIGMOD Int. Conf. Manag. of Data (SIGMOD'08), 2008, pp. 1123-1134.
30. L. Neumeyer, B. Robbins, A. Nair, and A. Kesari, " S4: Distributed Stream Computing Platform, " in Proc. 2010 IEEE 10th Int. Conf. Data Mining Workshops (ICDMW'10), 2010, pp. 170-177.
31. A. Bifet, G. Holmes, R. Kirkby, and B. Pfahringer, " MOA: Massive Online Analysis, " J. of Mach. Learn., vol. 11, pp. 1601-1604, 2010.
32. G. D. F. Morales, " SAMOA: A Platform for Mining Big Data Streams, " in Proc. 22nd Int. Conf. World Wide Web Companion (WWW'13), 2013, pp. 777-778.
33. G. Rao, " Big Data and Real Time Analytics, " in Proc. 2011 Int. Conf. Recent Trends in Inf. Technol. (ICRTIT'11), 2011, p. 2.
34. K. Sato, " An Inside Look at Google BigQuery, " Google, Inc., Tech. Rep., 2012.
35. M. Hirzel, H. Andrade, B. Gedik, G. Jaques-Silva, R. Khandekar, V. Kumar, M. Mendell, H. Nasgaard, S. Schenider, and R. S. and. K.L. Wu, " IBM Streams Processing Language: Analyzing Big Data in Motion, " IBM J. of Research and Develop., vol. 57, no. 3, 2013.
36. M. Zaharia, T. Das, H. Li, S. Shenker, and I. Stoica, " Discretized Streams: an Efficient and Fault-Tolerant Model for Stream Processing on Large Clusters, " in Proc. 4th USENIX Conf. Hot Topics in Cloud Comput. (HotCloud'12), 2012.
37. J. Xu, C. Tekin, and M. van der Schaar, " Learning Optimal Classifier Chains for Real-Time Big Data Mining, " in Proc. 51st Annu. Allerton Conf. Comm., Control and Comput. (Allerton'13), 2013, pp. 512-519.
38. M. Bakillah, A. Mobasheri, S. Liang, and A. Zipf, " Towards an Efficient Routing Web Processing Service through Capturing Real-Time Road Conditions from Big Data, " in Proc. 5th Comp. Sci. and Electron. Eng. Conf. (CEEC'13), 2013, pp. 152-155.
39. E. Olmezogullari, I. Ari, O. Celebi, and S. Ergut, " Data Stream Mining to Address Big Data Problems, " in Proc. 21st Signal Process. and Commun. Appl. Conf. (SIU'13), 2013.
40. T. Chardonnens, P. Cudre-Mauroux, M. Grund, and B. Perroud, " Big Data Analytics on High Velocity Streams: a Case Study, " in Proc. 2013 IEEE Int. Conf. on Big Data, 2013, pp. 784-787.
41. K. Thomas, C. Grier, J. Ma, V. Paxson, and D. Song, " Design and Evaluation of a Real-Time URL Spam Filtering Service, " in Proc. 2011 IEEE Symp. Security and Privacy (SP'11), 2011, pp. 447-462.
42. S. Najmabadi, M. K. an Z. Wang, Y. Baroud, and S. Simon, " Stream Processing of Scientific Big Data on Heterogeneous Platforms-Image Analytics on Big Data in Motion, " in Proc. 2013 IEEE 16th Int. Conf. Comput. Sci. and Eng. (CSE'13), 2013, pp. 965-970.
43. S. Ayhan, J. Pesce, P. Comitz, D. Sweet, S. Bliesner, and G. Gerberick, " Predictive Analytics with Aviation Big Data, " in Proc. 2013 Integrated Commun., Navigation and Surveillance Conf. (ICNS'13), 2013.
44. J. Hayes, H. Kolar, A. Akhriev, M. Barry, M. Purcell, and E. McKeown, " Real-Time Analysis and Management of Big Time-Series Data, " IBM J. of Research and Develop., vol. 57, no. 3, 2013.
45. Y. Huand, H. Dong, Y. Yesha, and S. Zhou, " A Scalable System for Community Discovery in Twitter During Hurricane Sandy, " in Proc. 2014 IEEE/ACM 14th Int. Symp. Cluster, Cloud and Grid Comput. (CCGrid'14), 2014, pp. 893-899.
46. F. Zhao, G. Wang, C. Deng, and Y. Zhao, " A Real-Time Intelligent Abnormity Diagnosis Platform in Electric Power System, " in Proc. 16th Int. Conf. on Advanced Commun. Tech. (ICACT'14), 2014, pp. 83-87.
47. B. Jun, " Feasibility Analysis of Big Log Data Real Time Search based on HBase and ElasticSearch, " in Proc. 9th Int. Conf. Natural Comput. (ICNC'13), 2013, pp. 1166-1170.
48. S. Lee, S. Shakya, R. Sunderraman, and S. Belkasim, " Real Time Micro-Blog Summarization based on Hadoop/HBase, " in Proc. 2013 IEEE/WIC/ACM Int. Joint Conf. Web Intell. (WI'13) and Intell. Agent Technol. (IAT'13), 2013, pp. 46-49.
49. W. Yang, X. Liu, L. Zhang, and L. Yang, " Big Data Real-Time Processing based on Storm, " in Proc. 2013 IEEE 12th Int. Conf. Trust, Security and Privacy in Comp. and Comm. (TrustCom'13), 2013, pp. 1784-1787.
50. F. Li, M. Ozsu, G. Cheng, and B. C. Ooi, " R-Store: a Scalable Distributed System for Supporting Real-Time Analytics, " in Proc. 2014 IEEE 30th Int. Conf. Data Eng. (ICDE'14), 2014, pp. 40-51.
51. J. Kim, N. Kim, B. Lee, J. Park, K. Seo, and H. Park, " RUBA: Real-Time Unstructured Big Data Analysis Framework, " in Proc. 2013 Int. Conf. ICT Convergence (ICTC'13), 2013, pp. 518-522.
52. A. Osman, M. El-Refaey, and A. Elnaggar, " Towards Real-Time Analytics in the Cloud, " in Proc. 2013 IEEE 9th World Congr. Services (SERVICES'13), 2013.
53. R. Ranjan, " Streaming Big Data Processing in Datacenter Clouds, " IEEE Cloud Comput., vol. 1, no. 1, pp. 78-83, 2014.
54. R. Begleiter, R. El-Yaniv, and G. Yona, " On Prediction using Variable Order Markov Models, " J. of Artificial Intell. Research, vol. 22, no. 1, pp. 385-421, 2004.
55. X. Guangtao, L. Zhongwei, Z. Hongzi, and L. Yunhuai, " Traffic-Known Urban Vehicular Route Prediction based on Partial Mobility Patterns, " in Proc. 2009 15th Int. Conf. Parallel and Distrib. Syst. (ICPADS'09), 2009.
56. A. Bratko, G. V. Cormack, B. Filipi?c, T. R. Lynam, and B. Zupan, " Spam Filtering using Statistical Data Compression Models, " J. of Mach. Learn. Research, vol. 7, pp. 2673-2698, 2006.
57. J.-L. Hsieh and C. T. Sun, " Building a Player Strategy Model by Analyzing Replays of Real-Time Strategy Games, " in Proc. 2008 Int. Joint Conf. Neural Netw. (IJCNN'08), 2008, pp. 3106-3111.
58. C. Bauckhage, K. Kersting, R. Sifa, C. Thurau, A. Drachen, and A. Canossa, " How Players Lose Interest in Playing a Game: An Empirical Study Based on Distributions of Total Playing Times, " in Proc. 2012 Int. Conf. Comput. Intell. and Games (CIG'12), 2012, pp. 139-146.