Advances in Intelligent Data Analysis X [electronic resource] : 10th International Symposium, IDA 2011, Porto, Portugal, October 29-31, 2011, Proceedings /
International Symposium on Intelligent Data AnalysisCorporate Author
International Symposium on Intelligent Data Analysis(10th :2011 :Porto, Portugal)
International Symposium on Intelligent Data Analysis
Gama, João.editor.
Bradley, Elizabeth.editor.
Hollmen, Jaakko.editor.
This book constitutes the refereed proceedings of the 10th International Conference on Intelligent Data Analysis, IDA 2011, held in Porto, Portugal, in October 2011. The 19 revised full papers and 16 revised poster papers resented together with 3 invited papers were carefully reviewed and selected from 73 submissions. All current aspects of intelligent data analysis are addressed, particularly intelligent support for modeling and analyzing complex, dynamical systems. The papers offer intelligent support for understanding evolving scientific and social systems including data collection and acquisition, such as crowd sourcing; data cleaning, semantics and markup; searching for data and assembling datasets from multiple sources; data processing, including workflows, mixed-initiative data analysis, and planning; data and information fusion; incremental, mixed-initiative model development, testing and revision; and visualization and dissemination of results; etc.
Bibliographic Level Mode of Issuance: Monograph
Includes bibliographical references and index.
This book constitutes the refereed proceedings of the 10th International Conference on Intelligent Data Analysis, IDA 2011, held in Porto, Portugal, in October 2011. The 19 revised full papers and 16 revised poster papers resented together with 3 invited papers were carefully reviewed and selected from 73 submissions. All current aspects of intelligent data analysis are addressed, particularly intelligent support for modeling and analyzing complex, dynamical systems. The papers offer intelligent support for understanding evolving scientific and social systems including data collection and acquisition, such as crowd sourcing; data cleaning, semantics and markup; searching for data and assembling datasets from multiple sources; data processing, including workflows, mixed-initiative data analysis, and planning; data and information fusion; incremental, mixed-initiative model development, testing and revision; and visualization and dissemination of results; etc.
Database management.
Application software.
Artificial intelligence.
Information storage and retrieval.
Algorithms.
Data mining.
Database Management.
Information Systems Applications (incl. Internet).
Artificial Intelligence.
Information Storage and Retrieval.
Algorithm Analysis and Problem Complexity.
Data Mining and Knowledge Discovery.

Lecture Notes in Computer Science