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Data Science and Distributed Intelligence: Recent Developments and Future Insights

  • Conference paper
Book cover Intelligent Distributed Computing VI

Part of the book series: Studies in Computational Intelligence ((SCI,volume 446))

Abstract

Big Data, Data Science and MapReduce are three keywords that have flooded our research papers and technical articles during the last two years. Also, due to the inherent distributed nature of computational infrastructures supporting Data Science (like Clouds and Grids), it is natural to view Distributed Intelligence as the most natural underlying paradigm for novel Data Science challenges. Following this major trend, in this paper we provide a background of these new terms, followed by a discussion of recent developments in the data mining and data warehousing areas in the light of aforementioned keywords. Finally, we provide our insights of the next stages in research and developments in this area.

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References

  1. Abouzeid, A., Bajda-Pawlikowski, K., Abadi, D.J., Rasin, A., Silberschatz, A.: Hadoopdb: An architectural hybrid of mapreduce and dbms technologies for analytical workloads. PVLDB 2(1), 922–933 (2009)

    Google Scholar 

  2. Agrawal, D., Das, S., Abbadi, A.E.: Big data and cloud computing: current state and future opportunities. In: EDBT, pp. 530–533 (2011)

    Google Scholar 

  3. Apache. Hadoop (July 2011), http://wiki.apache.org/hadoop

  4. BBC. Gap scraps new logo after online outcry (2010), http://www.bbc.co.uk/news/business-11520930

  5. Chu, C.-T., Kim, S.K., Lin, Y.-A., Yu, Y., Bradski, G.R., Ng, A.Y., Olukotun, K.: Map-reduce for machine learning on multicore. In: NIPS, pp. 281–288 (2006)

    Google Scholar 

  6. Cohen, J., Dolan, B., Dunlap, M., Hellerstein, J.M., Welton, C.: Mad skills: New analysis practices for big data. PVLDB 2(2), 1481–1492 (2009)

    Google Scholar 

  7. Cordeiro, R.L.F., Traina Jr., C., Traina, A.J.M., López, J., Kang, U., Faloutsos, C.: Clustering very large multi-dimensional datasets with mapreduce. In: KDD, pp. 690–698 (2011)

    Google Scholar 

  8. Cuzzocrea, A.: CAMS: OLAPing Multidimensional Data Streams Efficiently. In: Pedersen, T.B., Mohania, M.K., Tjoa, A.M. (eds.) DaWaK 2009. LNCS, vol. 5691, pp. 48–62. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  9. Cuzzocrea, A.: Retrieving Accurate Estimates to OLAP Queries over Uncertain and Imprecise Multidimensional Data Streams. In: Bayard Cushing, J., French, J., Bowers, S. (eds.) SSDBM 2011. LNCS, vol. 6809, pp. 575–576. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  10. Cuzzocrea, A., Chakravarthy, S.: Event-based lossy compression for effective and efficient olap over data streams. Data Knowl. Eng. 69(7), 678–708 (2010)

    Article  Google Scholar 

  11. Cuzzocrea, A., Furfaro, F., Mazzeo, G.M., Saccá, D.: A Grid Framework for Approximate Aggregate Query Answering on Summarized Sensor Network Readings. In: Meersman, R., Tari, Z., Corsaro, A. (eds.) OTM-WS 2004. LNCS, vol. 3292, pp. 144–153. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  12. Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)

    Article  Google Scholar 

  13. Dittrich, J., Quiané-Ruiz, J.-A., Jindal, A., Kargin, Y., Setty, V., Schad, J.: Hadoop++: Making a yellow elephant run like a cheetah (without it even noticing). PVLDB 3(1), 518–529 (2010)

    Google Scholar 

  14. Ene, A., Im, S., Moseley, B.: Fast clustering using mapreduce. In: KDD, pp. 681–689 (2011)

    Google Scholar 

  15. Foster, I.T., Kesselman, C., Tuecke, S.: The anatomy of the grid: Enabling scalable virtual organizations. IJHPCA 15(3), 200–222 (2001)

    Google Scholar 

  16. Gaber, M.M.: Data stream mining using granularity-based approach. In: Foundations of Computational Intelligence, vol. (6), pp. 47–66. Springer (2009)

    Google Scholar 

  17. Ghoting, A., Kambadur, P., Pednault, E.P.D., Kannan, R.: Nimble: a toolkit for the implementation of parallel data mining and machine learning algorithms on mapreduce. In: KDD, pp. 334–342 (2011)

    Google Scholar 

  18. Bártolo Gomes, J., Gaber, M.M., Sousa, P.A.C., Menasalvas, E.: Context-Aware Collaborative Data Stream Mining in Ubiquitous Devices. In: Gama, J., Bradley, E., Hollmén, J. (eds.) IDA 2011. LNCS, vol. 7014, pp. 22–33. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  19. Hacigümüs, H., Mehrotra, S., Iyer, B.R.: Providing database as a service. In: ICDE, pp. 29–38 (2002)

    Google Scholar 

  20. Herodotou, H., Lim, H., Luo, G., Borisov, N., Dong, L., Cetin, F.B., Babu, S.: Starfish: A self-tuning system for big data analytics. In: CIDR, pp. 261–272 (2011)

    Google Scholar 

  21. Hill, K.: How target figured out a teen girl was pregnant before her father did. Forbes (2012)

    Google Scholar 

  22. Lintott, C.J., Schawinski, K., Slosar, A., Land, K., Bamford, S., Thomas, D., Raddick, M.J., Nichol, R.C., Szalay, A., Andreescu, D., Murray, P., Vandenberg, J.: Galaxy Zoo: morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey. Monthly Notices of the Royal Astronomical Society 389(3), 1179–1189 (2008)

    Article  Google Scholar 

  23. Loukides, M.: What is data science? the future belongs to the companies and people that turn data into products. An OReilly Radar Report (June 2010)

    Google Scholar 

  24. Muthukrishnan, S.: Data streams: algorithms and applications. Foundations and trends in theoretical computer science. Now Publishers (2005)

    Google Scholar 

  25. Papadimitriou, S., Sun, J.: Disco: Distributed co-clustering with map-reduce: A case study towards petabyte-scale end-to-end mining. In: ICDM, pp. 512–521 (2008)

    Google Scholar 

  26. Papadimitriou, S., Sun, J., Yan, R.: Large-scale data mining: Mapreduce and beyond. In: Tutorial in KDD 2010(July 2010)

    Google Scholar 

  27. Soulellis, G.: Emerging trends in big data and analytics. Big Data Innovation, London (2012)

    Google Scholar 

  28. Stonebraker, M., Hong, J.: Researchers’ big data crisis; understanding design and functionality. Commun. ACM 55(2), 10–11 (2012)

    Article  Google Scholar 

  29. Thusoo, A., Sarma, J.S., Jain, N., Shao, Z., Chakka, P., Zhang, N., Anthony, S., Liu, H., Murthy, R.: Hive - a petabyte scale data warehouse using hadoop. In: ICDE, pp. 996–1005 (2010)

    Google Scholar 

  30. Yin, J., Gaber, M.M.: Clustering distributed time series in sensor networks. In: ICDM, pp. 678–687 (2008)

    Google Scholar 

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Author information

Authors and Affiliations

  1. ICAR-CNR and University of Calabria, I-87036, Cosenza, Italy

    Alfredo Cuzzocrea & Mohamed Medhat Gaber

  2. School of Computing, University of Portsmouth, Portsmouth, PO1 3HE, Hampshire, UK

    Alfredo Cuzzocrea & Mohamed Medhat Gaber

Authors
  1. Alfredo Cuzzocrea
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  2. Mohamed Medhat Gaber
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Editor information

Editors and Affiliations

  1. , Department of Electronics, Informatics, University of Calabria, Via P. Bucci, cubo 41C, Rende (CS), 87036, Italy

    Giancarlo Fortino

  2. Faculty of Automatics, Computers and Ele, Software Engineering Department, University of Craiova, Bvd. Decebal 107, Craiova, 200440, Romania

    Costin Badica

  3. , Dipartimento di Ingegneria Elettrica,, Università di Catania, V.le Andrea Doria, 6, Catania, 95125, Italy

    Michele Malgeri

  4. , Institute for Computer Science and, University of Duisburg-Essen, Schuetzenbahn 70, Essen, 45117, Germany

    Rainer Unland

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Cuzzocrea, A., Gaber, M.M. (2013). Data Science and Distributed Intelligence: Recent Developments and Future Insights. In: Fortino, G., Badica, C., Malgeri, M., Unland, R. (eds) Intelligent Distributed Computing VI. Studies in Computational Intelligence, vol 446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32524-3_18

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  • DOI: https://doi.org/10.1007/978-3-642-32524-3_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32523-6

  • Online ISBN: 978-3-642-32524-3

  • eBook Packages: EngineeringEngineering (R0)

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