caa a22 4500 445871334 CHVBK 20180317145512.0 cr unu---uuuuu 170323e20110901xx s 000 0 eng 10.1007/s10618-010-0204-8 doi (NATIONALLICENCE)springer-10.1007/s10618-010-0204-8 Mining frequent itemsets over distributed data streams by continuously maintaining a global synopsis [Elektronische Daten] [En Wang, Arbee Chen] 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. The Author(s), 2010 Distributed data streams nationallicence Data mining nationallicence Frequent itemset nationallicence Continuous distributed model nationallicence Hash-based approach nationallicence Wang En Cloud Computing Center for Mobile Applications, Industrial Technology Research Institute, Hsinchu, Taiwan, ROC aut Chen Arbee Department of Computer Science, National Chengchi University, Taipei, Taiwan, ROC aut Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 23/2(2011-09-01), 252-299 1384-5810 23:2<252 2011 23 10618 https://doi.org/10.1007/s10618-010-0204-8 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10618-010-0204-8 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Wang En Cloud Computing Center for Mobile Applications, Industrial Technology Research Institute, Hsinchu, Taiwan, ROC aut NATIONALLICENCE 700 1- Chen Arbee Department of Computer Science, National Chengchi University, Taipei, Taiwan, ROC aut NATIONALLICENCE 773 0- Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 23/2(2011-09-01), 252-299 1384-5810 23:2<252 2011 23 10618 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer