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What If Mixing Technologies for Big Data Mining and Queries Optimization

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Computational Collective Intelligence

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9330))

Abstract

Data is growing at an alarming speed in both volume and structure. The data volume and the multitude of sources have an exponential number of technical and application challenges. The classic tools of data management became unsuitable for processing and unable to offer effective tools to deal with the data explosion. Hence, the imposition of the Big Data in our technological landscape offers new solutions for data processing. In this work, we propose a model that integrates a Big Data solution and a classic DBMS, in a goal of queries optimization. Then, we valid the proposed optimized model through experiments showing the gain of the execution cost saved up.

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References

  1. Sagiroglu, S., Sinanc, D.: Big data: a review. In: Collaboration Technologies and Systems (CTS) 2013 International Conference on IEEE, pp. 42–47 (2013)

    Google Scholar 

  2. Narasimhan, R., Bhuvaneshwari T.: Big Data - A Brief Study. International Journal of Scientic and Engineering Research 5(9) (2014)

    Google Scholar 

  3. Dean, J., Ghemawats, S.: Mapreduce : Simplified data processing on large clusters. Communications of the ACM 51(1), 107–113 (2008)

    Article  Google Scholar 

  4. Zaharia, M., Konwinski, A., Joseph, A.D., Katz, R., Stoica, I.: Improving MapReduce performance in heterogeneous environments. In: Proceedings of the 8th USENIX Conference on Operating Systems Design and Implementation OSDI, vol. 8, no 4, pp. 29–42 (2008)

    Google Scholar 

  5. Stonebraker, M., Abadi, D., Dewitt, D., Madden, S., Paulsone, E., Pavlo, A., Rasin, A.: Mapreduce and parallel dbmss: friends or foes? Communications of the ACM 53(1), 64–71 (2010)

    Article  Google Scholar 

  6. Douglas, K., Douglas, S.: PostgreSQL: A comprehensive guide to building, programming, and administring PostreSQL databases, 1st edn. Sams Publishing (2003)

    Google Scholar 

  7. McClean, A., Conceicao, R., O’halloran, M.: A comparison of MapReduce and parallel database management systems. In: ICONS 2013 The Eighth International Conference on Systems, pp. 64–68 (2013)

    Google Scholar 

  8. Nance, C., Losser, T., Iype, R., Harmon, G.: NOSQL vs RDBMS – why there is room for both. In: Proceedings of the Southern Association for Information Systems Conference Savannah USA, pp. 111–116 (2013)

    Google Scholar 

  9. Gruska, N., Martin, P.: Integrating MapReduce and RDBMSs. In: Proceedings of the 2010 Conference of the Center for Advanced Studies on Collaborative Research, IBM Corp., pp. 212–223 (2010)

    Google Scholar 

  10. Yui, M., Kojima, I.: A database-hadoop hybrid approach to scalable machine learning. In: IEEE International Congress on Big Data 2013, pp. 1–8. IEEE (2013)

    Google Scholar 

  11. Abouzeid, A., Pawlikowski, K., Abadi, D., Silberschatz, A., Rasin, A.: Hadoopdb: an architectural hybrid of mapreduce and dbms technologies for analytical workloads. In: Proceedings of the VLDB Endowment, pp. 922–933 (2009)

    Google Scholar 

  12. Pavlo, A., Rasin, A., Madden, S., Stonebraker, M., Dewitt, D., Paulson, E., Shrinivas, L., Abadi, D.: A comparison of approaches to large scale data analysis. In: Proceedings of the 2009 ACM SIGMOD International Conference on Management of data, pp. 165–178 (2009)

    Google Scholar 

  13. Boukorca, A., Faget, Z., Bellatreche, L.: What-if physical design for multiple query plan generation. In: Decker, H., Lhotská, L., Link, S., Spies, M., Wagner, R.R. (eds.) DEXA 2014, Part I. LNCS, vol. 8644, pp. 492–506. Springer, Heidelberg (2014)

    Google Scholar 

  14. Brighen, A.: Conception de bases de données volumineuses sur le cloud. In: Doctoral dissertation, Université Abderrahmane Mira de Béjaia (2012)

    Google Scholar 

  15. Demirkan, H., Delen, D.: Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud. Decision Support Systems 55(1), 412–421 (2013)

    Article  Google Scholar 

  16. Ordonez, C.: Can we analyze big data inside a DBMS? In: Proceedings of the Sixteenth International Workshop on Data Warehousing and OLAP, pp. 85–92. ACM (2013)

    Google Scholar 

  17. Brown, P.G.: Object-Relational Database Development: A Plumber’s Guide. Prentice Hall PTR, USA (2000)

    Google Scholar 

  18. Chandrasekar, S., Dakshinamurthy, R., Seshakumar, P.G., Prabavathy, B., Babu, C.: A novel indexing scheme for efficient handling of small files in hadoop distributed file system. In: 2013 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–8 (2013)

    Google Scholar 

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Author information

Authors and Affiliations

  1. LARODEC, ISG Tunis, University of Tunis, Tunis, Tunisia

    Dhouha Jemal

  2. LARODEC, IHEC Carthage, University of Carthage, Carthage, Tunisia

    Rim Faiz

Authors
  1. Dhouha Jemal
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  2. Rim Faiz
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Corresponding author

Correspondence to Dhouha Jemal .

Editor information

Editors and Affiliations

  1. Universidad Complutense de Madrid, Madrid, Spain

    Manuel Núñez

  2. Wroclaw University of Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

  3. Computer Science Department, Universidad Autónoma De Madrid, Madrid, Spain

    David Camacho

  4. Wroclaw University of Technology, Wroclaw, Poland

    Bogdan Trawiński

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© 2015 Springer International Publishing Switzerland

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Jemal, D., Faiz, R. (2015). What If Mixing Technologies for Big Data Mining and Queries Optimization. In: Núñez, M., Nguyen, N., Camacho, D., Trawiński, B. (eds) Computational Collective Intelligence. Lecture Notes in Computer Science(), vol 9330. Springer, Cham. https://doi.org/10.1007/978-3-319-24306-1_60

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  • DOI: https://doi.org/10.1007/978-3-319-24306-1_60

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24305-4

  • Online ISBN: 978-3-319-24306-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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