SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Multiple aggregations over data streams
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Semantics and evaluation techniques for window aggregates in data streams
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
BRAID: stream mining through group lag correlations
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Mining Minimal Distinguishing Subsequence Patterns with Gap Constraints
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Finding Maximal Frequent Itemsets over Online Data Streams Adaptively
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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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.