Web server workload characterization: the search for invariants
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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
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Finding Frequent Items in Data Streams
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Frequency Estimation of Internet Packet Streams with Limited Space
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
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ACM Transactions on Database Systems (TODS)
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Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
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Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamically maintaining frequent items over a data stream
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Finding (Recently) Frequent Items in Distributed Data Streams
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Holistic aggregates in a networked world: distributed tracking of approximate quantiles
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
An improved data stream summary: the count-min sketch and its applications
Journal of Algorithms
Sketching streams through the net: distributed approximate query tracking
VLDB '05 Proceedings of the 31st international conference on Very large data bases
An Algorithm for In-Core Frequent Itemset Mining on Streaming Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Communication-efficient distributed monitoring of thresholded counts
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
DSTree: A Tree Structure for the Mining of Frequent Sets from Data Streams
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Distributed set-expression cardinality estimation
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Online mining of frequent sets in data streams with error guarantee
Knowledge and Information Systems
Mining Frequent Itemsets in a Stream
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Verifying and Mining Frequent Patterns from Large Windows over Data Streams
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Efficient Constraint Monitoring Using Adaptive Thresholds
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Data Mining and Knowledge Discovery
Methods for finding frequent items in data streams
The VLDB Journal — The International Journal on Very Large Data Bases
Maintaining frequent itemsets over high-speed data streams
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Efficient computation of frequent and top-k elements in data streams
ICDT'05 Proceedings of the 10th international conference on Database Theory
Mining frequent itemsets in a stream
Information Systems
Expert Systems with Applications: An International Journal
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Mining frequent itemsets over data streams has attracted much research attention in recent years. In the past, we had developed a hash-based approach for mining frequent itemsets over a single data stream. In this paper, we extend that approach to mine global frequent itemsets from a collection of data streams distributed at distinct remote sites. To speed up the mining process, we make the first attempt to address a new problem on continuously maintaining a global synopsis for the union of all the distributed streams. The mining results therefore can be yielded on demand by directly processing the maintained global synopsis. Instead of collecting and processing all the data in a central server, which may waste the computation resources of remote sites, distributed computations over the data streams are performed. A distributed computation framework is proposed in this paper, including two communication strategies and one merging operation. These communication strategies are designed according to an accuracy guarantee of the mining results, determining when and what the remote sites should transmit to the central server (named coordinator). On the other hand, the merging operation is exploited to merge the information received from the remote sites into the global synopsis maintained at the coordinator. By the strategies and operation, the goal of continuously maintaining the global synopsis can be achieved. Rooted in the continuously maintained global synopsis, we propose a mining algorithm for finding global frequent itemsets. Moreover, the correctness guarantees of the communication strategies and merging operation, and the accuracy guarantee analysis of the mining algorithm are provided. Finally, a series of experiments on synthetic datasets and a real dataset are performed to show the effectiveness and efficiency of the distributed computation framework.