By Chao-Tung Yang, Sung-Yi Chen, Tsui-Ting Chen (auth.), Christophe Cérin, Kuan-Ching Li (eds.)

This e-book constitutes the refereed court cases of the second one foreign convention on Grid and Pervasive Computing, GPC 2007, held in Paris, France in might 2007.

The fifty six revised complete papers and 12 revised brief papers have been rigorously chosen from 217 submissions in the course of rounds of reviewing and development. The papers deal with all points of grid and pervasive computing and concentrate on themes akin to cluster computing, excessive functionality computing, grid computing, semantic net and semantic grid, service-oriented computing, peer-to-peer computing, pervasive computing, cellular computing, community garage, in addition to grid and pervasive comparable applications.

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Extra info for Advances in Grid and Pervasive Computing: Second International Conference, GPC 2007, Paris, France, May 2-4, 2007. Proceedings

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Else, it saves the address of the sender as its parent. Then, its decrements TTL by one. If the value of TTL remains positive, it sends the message to its neighbors (except the parent) with the new TTL value. After propagating the message, p must wait to receive the responses of its neighbors. However, since some of the neighbors may leave the system and never response, the waiting time must be limited. We compute p’s waiting time using a cost function based on TTL, and network dependent parameters.

Then, we evaluate and analyze our model with a simulation. 1 Cost Model A critical issue in summary management is to trade off the summary updating cost against the benefits obtained for queries. Summary Update Cost. Here, our first undertaking is to optimize the update cost while taking into account query accuracy. In the next section, we discuss query accuracy which is measured in terms of the percentage of false positives and false negatives in query results. e. time cost and storage cost, and the traffic overhead generated in the network.

A unique feature of the summary system is its use of Background Knowledge (BK), a priori built on each attribute. It supports the translation of descriptions of database tuples into a user-defined vocabulary. Descriptors used for summary content representation are defined as linguistic variables [21] on the attribute domain. For example, Figure 2 shows a user-defined vocabulary on the attribute age. A detailed description of the S AINT E TI Q process is available in [14] and [16]. Concerning our work, we are interested in the scalability of the summarization process in a distributed environment.

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