Preference-Based Stream Analysis for Efficient Decision-Support Systems

  • Lena RudenkoEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 767)


Stream query processing is an important development trend as more time-oriented data is produced nowadays. It is not easy to find relevant and interesting content in large amount of data. Furthermore users want to have personalized results of stream data processing which correspond to their preferences. In this paper I present first research results achieved during my work on my doctoral thesis. I also discuss open issues and challenges on the way to my goal - the development of a preference-based stream analyzer for efficient decision-support.


  1. 1.
    Stefanidis, K., Koutrika, G., Pitoura, E.: A survey on representation, composition and application of preferences in database systems. ACM Trans. Database Syst. 36(3), 19:1–19:45 (2011)CrossRefGoogle Scholar
  2. 2.
    Golfarelli, M., Rizzi, S.: Expressing OLAP preferences. In: Winslett, M. (ed.) SSDBM 2009. LNCS, vol. 5566, pp. 83–91. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-02279-1_7 CrossRefGoogle Scholar
  3. 3.
    Kießling, W., Endres, M., Wenzel, F.: The preference SQL system - an overview. Bull. Tech. Comm. Data Eng. IEEE CS 34(2), 11–18 (2011)Google Scholar
  4. 4.
    Chen, J., DeWitt, D.J., Tian, F., Wang, Y.: NiagaraCQ: a scalable continuous query system for internet databases. In: Proceedings of SIGMOD 2000, pp. 379–390. ACM, New York (2000)Google Scholar
  5. 5.
    Bonnet, P., Gehrke, J., Seshadri, P.: Towards sensor database systems. In: Tan, K.-L., Franklin, M.J., Lui, J.C.-S. (eds.) MDM 2001. LNCS, vol. 1987, pp. 3–14. Springer, Heidelberg (2001). doi: 10.1007/3-540-44498-X_1 CrossRefGoogle Scholar
  6. 6.
    Sankaranarayanan, J., Samet, H., Teitler, B.E., Lieberman, M.D., Sperling, J.: Twitterstand: news in Tweets. In: Proceedings of ACM 2009, pp. 42–51 (2009)Google Scholar
  7. 7.
    Babu, S., Widom, J.: Continuous queries over data streams. SIGMOD Rec. 30(3), 109–120 (2001)CrossRefGoogle Scholar
  8. 8.
    Faria, E.R., Gonçalves, I.J.C.R., de Carvalho, A.C.P.L.F., Gama, J.: Novelty detection in data streams. Artif. Intell. Rev. 45(2), 235–269 (2016)CrossRefGoogle Scholar
  9. 9.
    Krempl, G., Žliobaite, I., Brzeziński, D., Hüllermeier, E., Last, M., Lemaire, V., Noack, T., Shaker, A., Sievi, S., Spiliopoulou, M., Stefanowski, J.: Open challenges for data stream mining research. In: SIGKDD 2014 Explorations Newsletter, vol. 16, no. 1 (2014)Google Scholar
  10. 10.
    Kontaki, M., Papadopoulos, A.N., Manolopoulos, Y.: Continuous processing of preference queries in data streams. In: Leeuwen, J., Muscholl, A., Peleg, D., Pokorný, J., Rumpe, B. (eds.) SOFSEM 2010. LNCS, vol. 5901, pp. 47–60. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-11266-9_4 CrossRefGoogle Scholar
  11. 11.
    Silva, J.A., Faria, E.R., Barros, R.C., Hruschka, E.R., de Carvalho, A., Gama, J., Clustering, D.S.: Data stream clustering: a survey. ACM Comput. Surv. 46(1), 13 (2013)CrossRefzbMATHGoogle Scholar
  12. 12.
    Baruah, R.D., Angelov, P., Baruah, D.: Dynamically evolving clustering for data streams. In: IEEE Conference on EAIS 2014, pp. 1–6 (2014)Google Scholar
  13. 13.
    Gu, X., Angelov, P.P.: Autonomous data-driven clustering for live data stream. In: SMC 2016, pp. 1128–1135. IEEE (2016)Google Scholar
  14. 14.
    Kastner, J., Endres, M., Kießling, W.: A pareto-dominant clustering approach for pareto-frontiers. In: Proceedings of the Workshops of the EDBT/ICDT 2017 Joint Conference, Venice, Italy (2017)Google Scholar
  15. 15.
    Arasu, A., Babcock, B., Babu, S., Datar, M., Ito, K., Nishizawa, I., Rosenstein, J., Widom, J.: STREAM: the stanford stream data manager. In: Proceedings of SIGMOD 2003, pp. 665–665. ACM, New York (2003)Google Scholar
  16. 16.
    Kießling, W.: Foundations of preferences in database systems. In: Proceedings of VLDB 2002, Hong Kong SAR, China, pp. 311–322. VLDB Endowment (2002)Google Scholar
  17. 17.
    Babcock, B., Datar, M., Motwani, R.: Sampling from a moving window over streaming data. In: Proceedings of SODA 2002, Philadelphia, USA, pp. 633–634 (2002)Google Scholar
  18. 18.
    Börzsönyi, S., Kossmann, D., Stocker, K.: The skyline operator. In: Proceedings of ICDE 2001, pp. 421–430. IEEE Computer Society, Washington (2001)Google Scholar
  19. 19.
    Hafenrichter, B., Kießling, W.: Optimization of relational preference queries. In: Proceedings of ADC 2005, Darlinghurst, Australia, pp. 175–184 (2005)Google Scholar
  20. 20.
    Morse, M., Patel, J.M., Jagadish, H.V.: Efficient skyline computation over low-cardinality domains. In: Proceedings of VLDB 2007, pp. 267–278 (2007)Google Scholar
  21. 21.
    Preisinger, T., Kießling, W.: The hexagon algorithm for pareto preference queries. In: Proceedings of the 3rd Multidisciplinary Workshop on Advances in Preference Handling in Conjunction with VLDB 2007, Vienna, Austria (2007)Google Scholar
  22. 22.
    Endres, M., Kießling, W.: High parallel skyline computation over low-cardinality domains. In: Manolopoulos, Y., Trajcevski, G., Kon-Popovska, M. (eds.) ADBIS 2014. LNCS, vol. 8716, pp. 97–111. Springer, Cham (2014). doi: 10.1007/978-3-319-10933-6_8 Google Scholar
  23. 23.
    Rudenko, L., Endres, M., Roocks, P., Kießling, W.: A preference-based stream analyzer. In: Workshop STREAMEVOLV 2016, Riva del Garda, Italy (2016)Google Scholar
  24. 24.
    Rudenko, L., Endres, M.: Personalized stream analysis with PreferenceSQL. In: BTW 2017, Stuttgart, Germanym, pp. 181–184 (2017)Google Scholar
  25. 25.
    Endres, M., Preisinger, T.: Beyond skylines: explicit preferences. In: Candan, S., Chen, L., Pedersen, T.B., Chang, L., Hua, W. (eds.) DASFAA 2017. LNCS, vol. 10177, pp. 327–342. Springer, Cham (2017). doi: 10.1007/978-3-319-55753-3_21 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of AugsburgAugsburgGermany

Personalised recommendations