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Data Warehousing and Knowledge Discovery

11th International Conference, DaWaK 2009 Linz, Austria, August 31-September 2, 2009 Proceedings

  • Conference proceedings
  • © 2009

Overview

Editors:
  1. Torben Bach Pedersen

    1. Department of Computer Science, Aalborg University, Aalborg Ø, Denmark

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  2. Mukesh K. Mohania

    1. IBM India Research Lab, New Delhi, India

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    You can also search for this editor in PubMed   Google Scholar

  3. A Min Tjoa

    1. Institute of Software Technology and Interactive Systems, Vienna University of Technology, Wien, Austria

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 5691)

Part of the book sub series: Information Systems and Applications, incl. Internet/Web, and HCI (LNISA)

Included in the following conference series:

Conference proceedings info: DaWaK 2009.

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About this book

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.

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Table of contents (37 papers)

  1. Front Matter

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  2. Invited Talk

    1. New Challenges in Information Integration

      • Laura M. Haas, Aya Soffer
      Pages 1-8

  3. Data Warehouse Modeling

    1. What Is Spatio-Temporal Data Warehousing?

      • Alejandro Vaisman, Esteban Zimányi
      Pages 9-23

    2. Towards a Modernization Process for Secure Data Warehouses

      • Carlos Blanco, Ricardo Pérez-Castillo, Arnulfo Hernández, Eduardo Fernández-Medina, Juan Trujillo
      Pages 24-35

    3. Visual Modelling of Data Warehousing Flows with UML Profiles

      • Jesús Pardillo, Matteo Golfarelli, Stefano Rizzi, Juan Trujillo
      Pages 36-47

  4. Data Streams

    1. CAMS: OLAPing Multidimensional Data Streams Efficiently

      • Alfredo Cuzzocrea
      Pages 48-62

    2. Data Stream Prediction Using Incremental Hidden Markov Models

      • Kei Wakabayashi, Takao Miura
      Pages 63-74

    3. History Guided Low-Cost Change Detection in Streams

      • Weiyun Huang, Edward Omiecinski, Leo Mark, Minh Quoc Nguyen
      Pages 75-86

  5. Physical Design

    1. HOBI: Hierarchically Organized Bitmap Index for Indexing Dimensional Data

      • Jan Chmiel, Tadeusz Morzy, Robert Wrembel
      Pages 87-98

    2. A Joint Design Approach of Partitioning and Allocation in Parallel Data Warehouses

      • Ladjel Bellatreche, Soumia Benkrid
      Pages 99-110

    3. Fast Loads and Fast Queries

      • Goetz Graefe
      Pages 111-124

  7. Data Cubes

    1. Efficient Online Aggregates in Dense-Region-Based Data Cube Representations

      • Kais Haddadin, Tobias Lauer
      Pages 177-188

    2. BitCube: A Bottom-Up Cubing Engineering

      • Alfredo Ferro, Rosalba Giugno, Piera Laura Puglisi, Alfredo Pulvirenti
      Pages 189-203

    3. Exact and Approximate Sizes of Convex Datacubes

      • Sébastien Nedjar
      Pages 204-215

  8. Data Mining Applications

    1. Finding Clothing That Fit through Cluster Analysis and Objective Interestingness Measures

      • Isis Peña, Herna L. Viktor, Eric Paquet
      Pages 216-228

    2. Customer Churn Prediction for Broadband Internet Services

      • B. Q. Huang, M-T. Kechadi, B. Buckley
      Pages 229-243
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Other volumes

  1. Data Warehousing and Knowledge Discovery

Editors and Affiliations

  • Department of Computer Science, Aalborg University, Aalborg Ø, Denmark

    Torben Bach Pedersen

  • IBM India Research Lab, New Delhi, India

    Mukesh K. Mohania

  • Institute of Software Technology and Interactive Systems, Vienna University of Technology, Wien, Austria

    A Min Tjoa

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