
Transactions on Large-Scale Data- and Knowledge-Centered Systems II
Springer (Publisher)
1st Edition
Published on 27. September 2010
Book
Paperback/Softback
XI, 141 pages
978-3-642-16174-2 (ISBN)
Description
This special issue of TLDKS contains two kinds of papers. First, it contains a sel- th tion of the best papers from the 11 International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2009), which was held from August 31 to S- tember 2, 2009 in Linz, Austria. Second, it contains a special section of papers on a particularly challenging domain in information retrieval, namely patent retrieval. Over the last decade, the International Conference on Data Warehousing and Knowledge Discovery (DaWaK) has established itself as one of the most important international scientific events within data warehousing and knowledge discovery. DaWaK brings together a wide range of researchers and practitioners working on these topics. The DaWaK conference series thus serves as a leading forum for discu- th ing novel research results and experiences within the field. The 11 International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2009) cont- ued the tradition by disseminating and discussing innovative models, methods, al- rithms, and solutions to the challenges faced by data warehousing and knowledge discovery technologies.
More details
Series
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Research
Illustrations
65 s/w Abbildungen
XI, 141 p. 65 illus.
Dimensions
Height: 0 mm
Width: 0 mm
Weight
247 gr
ISBN-13
978-3-642-16174-2 (9783642161742)
DOI
10.1007/978-3-642-16175-9
Schweitzer Classification
Other editions
Additional editions

E-Book
09/2010
Springer
€53.49
Available for download
Persons
Content
Data Warehousing and Knowledge Discovery.- Discovery of Frequent Patterns in Transactional Data Streams.- Fast Loads and Queries.- Efficient Online Aggregates in Dense-Region-Based Data Cube Representations.- Information Retrieval.- Improving Access to Large Patent Corpora.- Improving Retrievability and Recall by Automatic Corpus Partitioning.