Abstract
We study the problem of finding time-delayed associations among types of events from an event dataset. We present a baseline algorithm for the problem. We analyse the algorithm and identify two methods for improving efficiency. First, we propose pruning strategies that can effectively reduce the search space for frequent time-delayed associations. Second, we propose the breadth-first* (BF*) candidate-generation order. We show that BF*, when coupled with the least-recently-used cache replacement strategy, provides a significant saving in I/O cost. Experiment results show that combining the two methods results in a very efficient algorithm for solving the time-delayed association problem.
This research is supported by Hong Kong Research Grants Council Grant HKU 7138/04E.
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References
Ji, X., Bailey, J., Dong, G.: Mining minimal distinguishing subsequence patterns with gap constraints. In: ICDM, pp. 194–201 (2005)
Lee, D., Lee, W.: Finding maximal frequent itemsets over online data streams adaptively. In: ICDM, pp. 266–273 (2005)
Li, J., Maier, D., Tufte, K., Papadimos, V., Tucker, P.A.: Semantics and evaluation techniques for window aggregates in data streams. In: SIGMOD Conference, pp. 311–322 (2005)
Loo, K.K., Kao, B.: Mining time-delayed associations from discrete event datasets. Technical Report TR-2007-01, Department of Computer Science, The University of Hong Kong, Hong Kong (2007)
Mannila, H., Toivonen, H.: Discovering generalized episodes using minimal occurrences. In: KDD, pp. 146–151 (1996)
Papadimitriou, S., Sun, J., Faloutsos, C.: Streaming pattern discovery in multiple time-series. In: VLDB, pp. 697–708 (2005)
Sakurai, Y., Papadimitriou, S., Faloutsos, C.: Braid: Stream mining through group lag correlations. In: SIGMOD Conference, pp. 599–610 (2005)
Zaki, M.J.: Spade: An efficient algorithm for mining frequent sequences. Machine Learning 42(1/2), 31–60 (2001)
Zhang, R., Koudas, N., Ooi, B.C., Srivastava, D.: Multiple aggregations over data streams. In: SIGMOD Conference, pp. 299–310 (2005)
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Loo, K.K., Kao, B. (2007). Mining Time-Delayed Associations from Discrete Event Datasets. In: Kotagiri, R., Krishna, P.R., Mohania, M., Nantajeewarawat, E. (eds) Advances in Databases: Concepts, Systems and Applications. DASFAA 2007. Lecture Notes in Computer Science, vol 4443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71703-4_11
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DOI: https://doi.org/10.1007/978-3-540-71703-4_11
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