Abstract
Summary mining aims to find interesting summaries for a data set and to use data mining techniques to improve the functionality of Online Analytical Processing (OLAP) systems. In this paper, we propose an interactive summary mining approach, called GenSpace summary mining, to find interesting summaries based on user expectations. In the mining process, to record the user’s evolving knowledge, the system needs to update and propagate new expectations. In this paper, we propose a linear method for consistently and efficiently propagating user expectations in a GenSpace graph. For a GenSpace graph where uninteresting nodes can be marked by the user before the mining process, we propose a greedy algorithm to determine the propagation paths in a GenSpace subgraph that reduces the time cost subject to a fixed amount of space.
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References
Beyer, K., Ramakrishnan, R.: Bottom-up Computation of Sparse and Iceberg CUBEs. In: Proceedings of ACM SIGMOD 1999, pp. 359–370 (1999)
Geng, L.: Finding Interesting Summaries in GenSpace Graphs, Ph.D Thesis, Department of Computer Science, University of Regina, Regina, Canada (2005)
Hamilton, H.J., Geng, L., Findlater, L., Randall, D.J.: Efficient Spatio-Temporal Data Mining with GenSpace Graphs. Journal of Applied Logic 4(2), 192–214 (2006)
Harinarayan, V., Rajaraman, A., Ullman, J.D.: Implementing Data Cubes Efficiently. In: Proceedings of ACM SIGMOD 1996, pp. 205–216 (1996)
Hilderman, R.J., Hamilton, H.J.: Knowledge Discovery and Measures of Interest. Kluwer Acadamic, Dordrecht (2001)
Lawrence, M., Rau-Chaplin, A.: Dynamic View Selection for OLAP. In: Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2006. LNCS, vol. 4081, pp. 33–44. Springer, Heidelberg (2006)
Li, H., Huang, H., Liu, S.: PMC: Select Materialized Cells in Data Cubes. In: Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2005. LNCS, vol. 3589, pp. 168–178. Springer, Heidelberg (2005)
Sarawagi, S.: Explaining Differences in Multidimensional Aggregates. In: Proc. of the 25th Int. Conf. on Very Large Databases (VLDB), Edinburgh (September 1999)
Sarawagi, S., Agrawal, R., Megiddo, N.: Discovery Driven Exploration of OLAP Data Cubes. In: Schek, H.-J., Saltor, F., Ramos, I., Alonso, G. (eds.) EDBT 1998. LNCS, vol. 1377, Springer, Heidelberg (1998)
Sarawagi, S., Agrawal, R., Gupta, A.: On Computing the Data Cube. Research Report RJ10026, IBM Almaden Research Center (1996)
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Geng, L., Hamilton, H.J., Korba, L. (2007). Expectation Propagation in GenSpace Graphs for Summarization. In: Song, I.Y., Eder, J., Nguyen, T.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2007. Lecture Notes in Computer Science, vol 4654. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74553-2_42
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DOI: https://doi.org/10.1007/978-3-540-74553-2_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74552-5
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