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From Business Intelligence to Semantic Data Stream Management

  • Marie-Aude Aufaure
  • Raja Chiky
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8823)

Abstract

The Semantic Web technologies are being increasingly used for exploiting relations between data. In addition, new tendencies of real-time systems, such as social networks, sensors, cameras or weather information, are continuously generating data. This implies that data and links between them are becoming extremely vast. Such huge quantity of data needs to be analyzed, processed, as well as stored if necessary. In this paper, we will introduce recent work on Real-Time Business Intelligence that includes semantic data stream management. We will also present underlying approaches such as continuous queries and data summarization.

Keywords

Sensor Network Sensor Node Data Stream Business Intelligence Adaptive Sampling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Aggarwal, C. (ed.): Data Streams – Models and Algorithms. Springer (2007)Google Scholar
  2. 2.
    Arasu, A., Babcock, B., Babu, S., Datar, M., Ito, K., Motwani, R., Nishizawa, I., Srivastava, U., Thomas, D., Varma, R., Widom, J.: Stream: The stanford stream data manager. IEEE Data Eng. Bull. 26(1), 19–26 (2003)Google Scholar
  3. 3.
    Babcock, B., Datar, M., Motwani, R.: Sampling from a moving window over streaming data. In: Proceedings of the Thirteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2002, pp. 633–634. Society for Industrial and Applied Mathematics, Philadelphia (2002)Google Scholar
  4. 4.
    Barbieri, D.F., Braga, D., Ceri, S., Valle, E.D., Grossniklaus, M.: C-sparql: Sparql for continuous querying. In: Proceedings of the 18th International Conference on World Wide Web, pp. 1061–1062. ACM (2009)Google Scholar
  5. 5.
    Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Scientific American 284(5), 34–43 (2001)CrossRefGoogle Scholar
  6. 6.
    Brown, P.G., Haas, P.J.: Techniques for warehousing of sample data. In: Liu, L., Reuter, A., Whang, K.-Y., Zhang, J. (eds.) ICDE, p. 6. IEEE Computer Society (2006)Google Scholar
  7. 7.
    Chandrasekaran, S., Cooper, O., Deshpande, A., Franklin, M.J., Hellerstein, J.M., Hong, W., Krishnamurthy, S., Madden, S.R., Reiss, F., Shah, M.A.: Telegraphcq: Continuous dataflow processing. In: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, SIGMOD 2003, pp. 668–668. ACM, New York (2003)Google Scholar
  8. 8.
    Cohen, E., Cormode, G., Duffield, N.: Structure-aware sampling on data streams. In: Proceedings of the ACM SIGMETRICS Joint International Conference on Measurement and Modeling of Computer Systems, pp. 197–208. ACM (2011)Google Scholar
  9. 9.
    Cormode, G., Garofalakis, M.N.: Approximate continuous querying over distributed streams. ACM Trans. Database Syst. 33(2) (2008)Google Scholar
  10. 10.
    Gibbons, P.B., Matias, Y.: New sampling-based summary statistics for improving approximate query answers. In: Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, SIGMOD 1998, pp. 331–342. ACM, New York (1998)CrossRefGoogle Scholar
  11. 11.
    Golab, L., Özsu, M.T.: Issues in data stream management. SIGMOD Rec. 32(2), 5–14 (2003)CrossRefGoogle Scholar
  12. 12.
    Hitzler, P., Krtzsch, M., Rudolph, S.: Foundations of Semantic Web Technologies, 1st edn. Chapman & Hall/CRC (2009)Google Scholar
  13. 13.
    Jain, A., Chang, E.Y.: Adaptive sampling for sensor networks. In: Proceeedings of the 1st International Workshop on Data Management for Sensor Networks: In Conjunction with VLDB 2004, DMSN 2004, pp. 10–16. ACM, New York (2004)Google Scholar
  14. 14.
    Jain, N., Pozo, M., Chiky, R., Kazi-Aoul, Z.: Sampling semantic data stream: Resolving overload and limited storage issues. In: DaEng, pp. 41–48 (2013)Google Scholar
  15. 15.
    Kobsa, A.: Generic user modeling systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 136–154. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  16. 16.
    Komazec, S., Cerri, D., Fensel, D.: Sparkwave: continuous schema-enhanced pattern matching over rdf data streams. In: Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems, DEBS 2012, pp. 58–68. ACM, New York (2012)Google Scholar
  17. 17.
    Le-Phuoc, D., Dao-Tran, M., Xavier Parreira, J., Hauswirth, M.: A native and adaptive approach for unified processing of linked streams and linked data. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 370–388. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  18. 18.
    Liu, C., Wu, K., Tsao, M.: Energy efficient information collection with the arima model in wireless sensor networks. In: GLOBECOM, p. 5. IEEE (2005)Google Scholar
  19. 19.
    Marbini, A.D., Sacks, L.E.: Adaptive sampling mechanisms in sensor networks (2003)Google Scholar
  20. 20.
    Melo, C.A., Mikheev, A., Le Grand, B., Aufaure, M.-A.: Cubix: A visual analytics tool for conceptual and semantic data. In: Vreeken, J., Ling, C., Zaki, M.J., Siebes, A., Yu, J.X., Goethals, B., Webb, G.I., Wu, X. (eds.) ICDM Workshops, pp. 894–897. IEEE Computer Society (2012)Google Scholar
  21. 21.
    Sheth, A., Henson, C., Sahoo, S.S.: Semantic sensor web. IEEE Internet Computing 12(4), 78–83 (2008)CrossRefGoogle Scholar
  22. 22.
    Tatbul, N., Çetintemel, U., Zdonik, S., Cherniack, M., Stonebraker, M.: Load shedding in a data stream manager. In: Proceedings of the 29th International Conference on Very Large Data Bases, VLDB 2003, vol. 29, pp. 309–320. VLDB Endowment (2003)Google Scholar
  23. 23.
    Trujillo, J., Maté, A.: Business intelligence 2.0: A general overview. In: Aufaure, M.-A., Zimányi, E. (eds.) eBISS 2011. LNBIP, vol. 96, pp. 98–116. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  24. 24.
    Vitter, J.S.: Random sampling with a reservoir. ACM Trans. Math. Softw. 11(1), 37–57 (1985)MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Willett, R., Martin, A., Nowak, R.: Backcasting: Adaptive sampling for sensor networks. In: Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks, IPSN 2004, pp. 124–133. ACM, New York (2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Marie-Aude Aufaure
    • 1
  • Raja Chiky
    • 2
  1. 1.MAS Lab Ecole Centrale ParisFrance
  2. 2.ISEP - LISITEParisFrance

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