New PDF release: Agents and Data Mining Interaction: 6th International

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

ISBN-10: 3642154190

ISBN-13: 9783642154195

ISBN-10: 3642154204

ISBN-13: 9783642154201

This booklet constitutes the refereed complaints 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 provided have been rigorously reviewed and chosen from 37 submissions. The papers are prepared in topical sections on brokers for facts mining; facts mining for brokers; info mining in brokers; and agent mining purposes.

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Extra resources 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

Goehner without the need for user interaction [1]. A typical issue is the involvement and integration of human intelligence and interaction in information gathering systems. It is a challenging task to include the different roles, the empirical experience and the qualitative intelligence of humans. Furthermore, it is challenging to support the different views on the system structure, data and functionality from the different individual users, such as engineers, process personnel and managers [2].

Three main goals need to be achieved in order to provide an information retrieval system, capable to support distributed heterogeneous data sources in industrial automation systems. These main goals are: 1. Provision of user interaction 2. Processing of queries 3. Integration of data sources Based on the main goals, the decomposition of system goals is conducted as follows. The provision of user interaction is a crucial aspect of the information retrieval process in industrial automation systems and represents the main goal.

Query Agents According to the mappings provided by the domain ontologies, query agents work on the creation of query plans which are used by information retrieval agents (IRAs) to query data sources. QAs play the role of query sequence processors. They encapsulate semantic information for the execution of particular query sequences. The knowledge to create a query sequence exists in the respective domain ontology. Thus, the user needs to have less information about the industrial automation system structure and the 34 S.

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


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