Approximate OLAP Query Processing over Uncertain and Imprecise Multidimensional Data Streams

  • Alfredo Cuzzocrea
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8056)


Anovel framework for estimating OLAP queries over uncertain and imprecise multidimensional data streams is introduced and experimentally assessed in this paper. We complete our theoretical contributions by means of an innovative approach for providing theoretically-founded estimates to OLAP queries over uncertain and imprecise multidimensional data streams that exploits the well-recognized probabilistic estimators theory. Finally, we provide an experimental assessment and analysis of the performance of our framework against several classes of synthetic data stream sets.


Data Stream Probability Distribution Function Input Data Stream Joint Probability Distribution Function Uncertain Data Stream 
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  1. 1.
    Abadi, D., Carney, D., Cherniack, M., Convey, C., Lee, S., Stonebraker, M., Tatbul, N., Zdonik, S.: Aurora: A New Model and Architecture for Data Stream Management. VLDB Journal 12(2) (2003)Google Scholar
  2. 2.
    Aggarwal, C.C., Yu, P.S.: A Framework for Clustering Uncertain Data Streams. In: IEEE ICDE (2008)Google Scholar
  3. 3.
    Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and Issues in Data Stream Systems. In: ACM PODS (2002)Google Scholar
  4. 4.
    Burdick, D., Deshpande, P., Jayram, T.S., Ramakrishnan, R., Vaithyanathan, S.: OLAP over Uncertain and Imprecise Data. In: VLDB (2005)Google Scholar
  5. 5.
    Cai, Y.D., Clutterx, D., Papex, G., Han, J., Welgex, M., Auvilx, L.: MAIDS: Mining Alarming Incidents from Data Streams. In: ACM SIGMOD (2004)Google Scholar
  6. 6.
    Chen, Y., Dong, G., Han, J., Wah, B.W., Wang, J.: Multi-Dimensional Regression Analysis of Time-Series Data Streams. In: VLDB (2002)Google Scholar
  7. 7.
    Cormode, G., Garofalakis, M.: Sketching Probabilistic Data Streams. In: ACM SIGMOD (2007)Google Scholar
  8. 8.
    Cormode, G., Korn, F., Tirthapura, S.: Exponentially Decayed Aggregates on Data Streams. In: IEEE ICDE (2008)Google Scholar
  9. 9.
    Cuzzocrea, A., Chakravarthy, S.: Event-based Lossy Compression For Effective And Efficient OLAP Over Data Streams. Data & Knowledge Engineering 69(7) (2010)Google Scholar
  10. 10.
    Cuzzocrea, A., Furfaro, F., Masciari, E., Saccà, D.: Improving OLAP Analysis of Multidimensional Data Streams via Efficient Compression Techniques. In: Cuzzocrea, A. (ed.) Intelligent Techniques for Warehousing and Mining Sensor Network Data. IGI Global (2009)Google Scholar
  11. 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)CrossRefGoogle Scholar
  12. 12.
    Cuzzocrea, A., Serafino, P.: LCS-Hist: Taming Massive High-Dimensional Data Cube Compression. In: EDBT (2009)Google Scholar
  13. 13.
    Dobra, A., Gehrke, J., Garofalakis, M., Rastogi, R.: Processing Complex Aggregate Queries over Data Streams. In: ACM SIGMOD (2002)Google Scholar
  14. 14.
    Dalvi, N.N., Suciu, D.: Efficient Query Evaluation on Probabilistic Databases. In: VLDB (2004)Google Scholar
  15. 15.
    Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., Venkatrao, M., Pellow, F., Pirahesh, H.: Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals. Data Mining and Knowledge Discovery 1(1) (1997)Google Scholar
  16. 16.
    Han, J., Chen, Y., Dong, G., Pei, J., Wah, B.W., Wang, J., Cai, Y.D.: Stream Cube: An Architecture for Multi-Dimensional Analysis of Data Streams. Distributed and Parallel Databases 18(2) (2005)Google Scholar
  17. 17.
    Hellerstein, J.M., Haas, P.J., Wang, H.J.: Online Aggregation. In: ACM SIGMOD (1997)Google Scholar
  18. 18.
    Jayram, T.S., McGregor, A., Muthukrishnan, S., Vee, E.: Estimating Statistical Aggregates on Probabilistic Data Streams. In: ACM PODS (2007)Google Scholar
  19. 19.
    Jin, C., Yi, K., Chen, L., Xu Yu, J., Lin, X.: Sliding-Window Top-K Queries on Uncertain Streams. PVLDB 1(1) (2008)Google Scholar
  20. 20.
    Papoulis, A.: Probability, Random Variables, and Stochastic Processes, 2nd edn. McGraw-Hill, New York City (1984)zbMATHGoogle Scholar
  21. 21.
    Timko, I., Dyreson, C.E., Pedersen, T.B.: Pre-Aggregation with Probability Distributions. In: ACM DOLAP (2006)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alfredo Cuzzocrea
    • 1
  1. 1.ICAR-CNR and University of CalabriaCosenzaItaly

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