Advances in Intelligent Data Analysis X : 10th International Symposium, IDA 2011, Porto, Portugal, October 29-31, 2011, Proceedings / edited by João Gama, Elizabeth Bradley, Jaakko Hollmen.

Format:

Book

Contributor:

Gama, João, editor.

Bradley, Elizabeth, Professor, editor.

Hollmén, Jaakko, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

LNCS sublibrary. Information systems and applications, incl. Internet/Web, and HCI ; SL 3, 7014.

Information Systems and Applications, incl. Internet/Web, and HCI ; 7014

Language:

English

Subjects (All):

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.

Local Subjects:

Database Management.

Information Systems Applications (incl. Internet).

Artificial Intelligence.

Information Storage and Retrieval.

Algorithm Analysis and Problem Complexity.

Data Mining and Knowledge Discovery.

Physical Description:

1 online resource (XIV, 425 pages) : 144 illustrations.

Edition:

First edition 2011.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2011.

System Details:

text file PDF

Summary:

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; et cetera.

Other Format:

Printed edition:

ISBN:

978-3-642-24800-9

9783642248009

Access Restriction:

Restricted for use by site license.