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SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
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EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
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Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
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DEXA '09 Proceedings of the 20th International Conference on Database and Expert Systems Applications
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ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
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Daisy: the center for data-intensive systems at Aalborg University
ACM SIGMOD Record
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This paper proposes the concept of generalized sentinel rules (sentinels) and presents an algorithm for their discovery. Sentinels represent schema level relationships between changes over time in certain measures in a multi-dimensional data cube. Sentinels notify users based on previous observations, e.g., that revenue might drop within two months if an increase in customer problems combined with a decrease in website traffic is observed. If the vice versa also holds, we have a bi-directional sentinel, which has a higher chance of being causal rather than coincidental. We significantly extend prior work to combine multiple measures into better sentinels as well as auto-fitting the best warning period. We introduce two novel quality measures, Balance and Score, that are used for selecting the best sentinels. We introduce an efficient algorithm incorporating novel optimization techniques. The algorithm is efficient and scales to very large datasets, which is verified by extensive experiments on both real and synthetic data. Moreover, we are able to discover strong and useful sentinels that could not be found when using sequential pattern mining or correlation techniques.