By Amal El Fallah Seghrouchni, Adina Magda Florea, Andrei Olaru (auth.), Mohammad Essaaidi, Michele Malgeri, Costin Badica (eds.)
This ebook represents the mixed peer-reviewed court cases of the Fourth foreign Symposium on clever dispensed Computing -- IDC 2010 and of the second one overseas Workshop on Multi-Agent structures know-how and Semantics -- MASTS'2010. either occasions have been held in Tangier, Morocco in the course of September 16-18, 2010. The 33 contributions released during this ebook deal with many themes concerning concept and purposes of clever dispensed computing and multi-agent structures, together with: agent-based e-business, ambient intelligence, bio-informatics, collaborative platforms, cryptography and safety, disbursed algorithms, dispensed facts mining, dispensed simulation, grid computing, snapshot processing, details extraction, wisdom administration, common sense programming, cellular brokers, ontologies, pervasive computing, Petri nets, technique modeling, self-organizing structures, semantic internet companies, provider orientated computing, social networks and belief, swarm intelligence, net purposes, instant sensor networks.
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Extra resources for Intelligent Distributed Computing IV: Proceedings of the 4th International Symposium on Intelligent Distributed Computing - IDC 2010, Tangier, Morocco, September 2010
Therefore, one goal of our paper is to introduce and to consider our work also in the context of Service-like Oriented Architecture (SOA)  paradigms. e. customizing or improving classical algorithms and developing new ones in the area of data mining. We propose a technical solution for allowing users to access properly formed data from various locations (the resources shall be identified by their URLs) and apply different data mining algorithms, all in an easy and transparent manner. In this paper we present an online system, WebKM, following the client–server paradigm, that allows execution of data mining tasks from a thin client (Web Browser).
Are organized in the Data Service Inventory and interoperate each other through a dedicated bus. Considering the nature of those services and the need of the planning system to historicize the information referred to specific situations of interest, such as the presence of a significant flow of traffic with a given weather conditions, the data provided by these services will be structured and persisted on a dedicated repository: the Traffic Data Repository. In the same way, the infrastructure services (e.
If the artificial ant does not decide to drop the service in this best position, then the ant performs random moves. The decision to pick up (Decide Pick) or drop (Decide Drop) a service is probabilistic and takes into account the DoM between the service and the surrounding Semantic Web Service Clustering for Efficient Discovery 29 Algorithm 2. Ant Clustering Input: S - the set of services; noAnts - the number of artificial ants; Output: CLS - the set of clusters; Comments: G - the toroidal grid; noIt - the number of iterations; Ants - the set of artificial ants; begin Ants = Initialize Ants(noAnts); G = Initialize Grid Of Services(S) forall ant ∈ Ants do ant = Pick Service Location(S, G) for i = 0 to noIt do ant = Choose Random Ant(Ants) ant = Move(G) if (Decide Drop(ant)) then ant = Drop Service(G) while (Decide Pick(ant) == false)do ant = Move(G) ant = Pick Service(S) else ant = Move(G) end if end for CLS = Retrieve Clusters(S, G);CLS = Clustering Refinement(CLS ) return CLS end services on the grid.