Data Warehousing and Knowledge Discovery
11th International Conference, DaWaK 2009 Linz, Austria, August 31-September 2, 2009 Proceedings
Published on 17. August 2009
Book
Paperback/Softback
XIV, 480 pages
978-3-642-03729-0 (ISBN)
Description
Data warehousing and knowledge discovery are increasingly becoming mission-critical technologies for most organizations, both commercial and public, as it becomes incre- ingly important to derive important knowledge from both internal and external data sources. With the ever growing amount and complexity of the data and information available for decision making, the process of data integration, analysis, and knowledge discovery continues to meet new challenges, leading to a wealth of new and exciting research challenges within the area. 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- ing novel research results and experiences within data warehousing and knowledge th discovery. This year's conference, the 11 International Conference on Data Wa- housing and Knowledge Discovery (DaWaK 2009), continued the tradition by d- seminating and discussing innovative models, methods, algorithms, and solutions to the challenges faced by data warehousing and knowledge discovery technologies.
More details
Series
Edition
2009 ed.
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Research
Illustrations
XIV, 480 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 27 mm
Weight
744 gr
ISBN-13
978-3-642-03729-0 (9783642037290)
DOI
10.1007/978-3-642-03730-6
Schweitzer Classification
Persons
Content
Invited Talk.- New Challenges in Information Integration.- Data Warehouse Modeling.- What Is Spatio-Temporal Data Warehousing?.- Towards a Modernization Process for Secure Data Warehouses.- Visual Modelling of Data Warehousing Flows with UML Profiles.- Data Streams.- CAMS: OLAPing Multidimensional Data Streams Efficiently.- Data Stream Prediction Using Incremental Hidden Markov Models.- History Guided Low-Cost Change Detection in Streams.- Physical Design.- HOBI: Hierarchically Organized Bitmap Index for Indexing Dimensional Data.- A Joint Design Approach of Partitioning and Allocation in Parallel Data Warehouses.- Fast Loads and Fast Queries.- Pattern Mining.- TidFP: Mining Frequent Patterns in Different Databases with Transaction ID.- Non-Derivable Item Set and Non-Derivable Literal Set Representations of Patterns Admitting Negation.- Which Is Better for Frequent Pattern Mining: Approximate Counting or Sampling?.- A Fast Feature-Based Method to Detect Unusual Patterns in Multidimensional Datasets.- Data Cubes.- Efficient Online Aggregates in Dense-Region-Based Data Cube Representations.- BitCube: A Bottom-Up Cubing Engineering.- Exact and Approximate Sizes of Convex Datacubes.- Data Mining Applications.- Finding Clothing That Fit through Cluster Analysis and Objective Interestingness Measures.- Customer Churn Prediction for Broadband Internet Services.- Mining High-Correlation Association Rules for Inferring Gene Regulation Networks.- Analytics.- Extend UDF Technology for Integrated Analytics.- High Performance Analytics with the R3-Cache.- Open Source BI Platforms: A Functional and Architectural Comparison.- Ontology-Based Exchange and Immediate Application of Business Calculation Definitions for Online Analytical Processing.- Data Mining.- Skyline View: Efficient Distributed Subspace Skyline Computation.- HDB-Subdue: A Scalable Approach to Graph Mining.- Mining Violations to Relax Relational Database Constraints.- Arguing from Experience to Classifying Noisy Data.- Clustering.- Dynamic Clustering-Based Estimation of Missing Values in Mixed Type Data.- The PDG-Mixture Model for Clustering.- Clustering for Video Retrieval.- Spatio-Temporal Mining.- Trends Analysis of Topics Based on Temporal Segmentation.- Finding N-Most Prevalent Colocated Event Sets.- Rule Mining.- Rule Learning with Probabilistic Smoothing.- Missing Values: Proposition of a Typology and Characterization with an Association Rule-Based Model.- Olap Recommendation.- Recommending Multidimensional Queries.- Preference-Based Recommendations for OLAP Analysis.