By Kai Hwang (auth.), Song Wu, Laurence T. Yang, Tony Li Xu (eds.)
This e-book constitutes the refereed complaints of the 3rd overseas convention on Grid and Pervasive Computing, GPC 2008, held in Kunming, China, in could 2008.
The forty five revised complete papers offered including 2 keynote lectures have been rigorously reviewed and chosen from 184 submissions. The papers disguise all present problems with grid and pervasive computing and concentrate on subject matters equivalent to cluster computing, grid computing, excessive functionality computing, community garage, peer-to-peer computing, pervasive computing, the Semantic internet and the Semantic Grid, and service-oriented computing.
Read or Download Advances in Grid and Pervasive Computing: Third International Conference, GPC 2008, Kunming, China, May 25-28, 2008. Proceedings PDF
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Additional info for Advances in Grid and Pervasive Computing: Third International Conference, GPC 2008, Kunming, China, May 25-28, 2008. Proceedings
784–790 (1998) 4. : The role of planning in grid computing. In: Proceedings of ICAPS 2003, pp. 26–33. AAAI Press, Menlo Park (2003) 5. : Optimizing User-Level Communication Patterns on the Fujitsu AP3000. In: Proceedings of the 1st IEEE International Workshop on Cluster Computing, pp. 105–111 (1999) 6. : Building an open Grid. In: Proceedings of the second IEEE international symposium on Network Computing and Applications, p. 191 (2003) 7. : The Grid: Blueprint for a New Computing Infrastructure.
Many parallel-distributed methods have been proposed [6,7,9,10,12]. However, when the given threshold is small, or the average length of transaction is long, since too much information must be exchanged between mining processes. The performance deteriorates notably when the database size increases. In this study, a parallel frequent-pattern mining algorithm was used to solve frequent pattern mining problems. Moreover, a transaction identification set (Tidset) was used to directly select transactions instead of scanning whole databases.
Finally, the conclusion and future work are discussed in section 5. 2 Related Work First of all, the frequent pattern mining problem is defined. where is set of transactions. Each transaction denotes the support of itemset items in the database. including . which is the number of transactions in is a be a set of all in a database (1) 20 J. -M. Yu Given a minimum support threshold , an item set is frequent if . The frequent pattern mining problem is to find all item sets where for a given threshold .