Download Agents and Data Mining Interaction: 6th International by Sandip Sen (auth.), Longbing Cao, Ana L. C. Bazzan, Vladimir PDF

By Sandip Sen (auth.), Longbing Cao, Ana L. C. Bazzan, Vladimir Gorodetsky, Pericles A. Mitkas, Gerhard Weiss, Philip S. Yu (eds.)

This publication constitutes the refereed lawsuits of the sixth foreign Workshop on brokers and knowledge Mining interplay, ADMI 2010, held in Toronto, Canada, in could 2010. The 15 revised complete papers awarded have been conscientiously reviewed and chosen from 37 submissions. The papers are prepared in topical sections on brokers for facts mining; info mining for brokers; information mining in brokers; and agent mining purposes.

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Additional info for Agents and Data Mining Interaction: 6th International Workshop on Agents and Data Mining Interaction, ADMI 2010, Toronto, ON, Canada, May 11, 2010, Revised Selected Papers

Example text

Sensors and actuators and the directly associated hardware for process monitoring and control. The work routine in industrial automation sites has become more and more like ordinary work routine in office environments. However, the tools developed for office environments are not always suitable for industrial automation environments, especially for information gathering tasks. Compared to users of ordinary information systems, users in industrial automation have different needs. Whereas in ordinary information systems state changes or information changes are the result of user activities, in industrial automation the process and the associated industrial automation system is primarily running on its own L.

Table 2 presents a summary of these results. Table 2. Summary of results. 4% As with the previous experiment, these results indicate an area of concern for the hospital. It seems clear that those who are admitted to the hospital during the late-night and early morning hours have a lower 60-day mortality rate, when compared to the patients who are admitted during the daytime hours. 1 Iterative Process between CAS Simulation and Real-World Data One must be careful in assigning causal factors to correlations in the data.

They may suggest a set of possible search targets that helps users to formulate queries. UAs translate the agent communication into a clear text representation form the user is able to understand. As a particular form of a top-level ontology, the user ontology contains the user specific terminology and supports UAs interacting with the user. There is one dedicated UA for each user, which remains in the system, as long as the user is using the system. Query Management Agents Supported by the top-level ontology, query management agents (QMAs) work on solving queries by cooperating with UAs and query agents (QAs) within the network.

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