Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Querying and mining data streams: you only get one look a tutorial
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
Approximate join processing over data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Finding recent frequent itemsets adaptively over online data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
estWin: adaptively monitoring the recent change of frequent itemsets over online data streams
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Load Shedding for Aggregation Queries over Data Streams
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
The complexity of mining maximal frequent itemsets and maximal frequent patterns
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive load shedding for windowed stream joins
Proceedings of the 14th ACM international conference on Information and knowledge management
Load shedding in stream databases: a control-based approach
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Window-aware load shedding for aggregation queries over data streams
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A regression-based temporal pattern mining scheme for data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Load shedding in a data stream manager
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Incremental Algorithm for Discovering Frequent Subsequences in Multiple Data Streams
International Journal of Data Warehousing and Mining
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Data streams are usually generated in an online fashion characterized by huge volume, rapid unpredictable rates, and fast changing data characteristics. It has been hence recognized that mining over streaming data requires the problem of limited computational resources to be adequately addressed. Since the arrival rate of data streams can significantly increase and exceed the CPU capacity, the machinery must adapt to this change to guarantee the timeliness of the results. We present an online algorithm to approximate a set of frequent patterns from a sliding window over the underlying data stream -- given apriori CPU capacity. The algorithm automatically detects overload situations and can adaptively shed unprocessed data to guarantee the timely results. We theoretically prove, using probabilistic and deterministic techniques, that the error on the output results is bounded within a pre-specified threshold. The empirical results on various datasets also confirmed the feasiblity of our proposal.