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Data Warehousing and Knowledge Discovery

12th International Conference, DAWAK 2010, Bilbao, Spain, August/September 2010. Proceedings

  • Torben Bach Pedersen
  • Mukesh K. Mohania
  • A Min Tjoa

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6263)

Table of contents

  1. Front Matter
  2. Data Warehouse Modeling and Spatial Data Warehouses

    1. Carlo dell’Aquila, Francesco Di Tria, Ezio Lefons, Filippo Tangorra
      Pages 1-12
    2. Andrea Carmè, Jose-Norberto Mazón, Stefano Rizzi
      Pages 13-24
    3. Leticia Gómez, Alejandro Vaisman, Esteban Zimányi
      Pages 25-39
    4. Thiago Luís Lopes Siqueira, Ricardo Rodrigues Ciferri, Valéria Cesário Times, Cristina Dutra de Aguiar Ciferri
      Pages 40-51
  3. Mining Social Networks and Graphs

    1. Bin-Hui Chou, Einoshin Suzuki
      Pages 52-64
    2. Symeon Papadopoulos, Yiannis Kompatsiaris, Athena Vakali
      Pages 65-76
    3. Chuntao Jiang, Frans Coenen, Michele Zito
      Pages 77-88
  4. Physical Data Warehouse Design

    1. Ladjel Bellatreche, Kamel Boukhalfa
      Pages 105-116
    2. Christian Lemke, Kai-Uwe Sattler, Franz Faerber, Alexander Zeier
      Pages 117-129
  5. Dependency Mining

    1. Bart Goethals, Dominique Laurent, Wim Le Page
      Pages 142-156
    2. Taylor Phillips, Chris GauthierDickey, Ramki Thurimella
      Pages 157-171
  6. Business Intelligence and Analytics

    1. Todd Eavis, Hiba Tabbara, Ahmad Taleb
      Pages 172-189
    2. Qiming Chen, Meichun Hsu
      Pages 190-202
    3. Eduardo Zanoni Marques, Rodrigo Sanches Miani, Everton Luiz de Almeida Gago Júnior, Leonardo de Souza Mendes
      Pages 203-214
  7. Outlier and Image Mining

    1. Minh Quoc Nguyen, Edward Omiecinski, Leo Mark, Danesh Irani
      Pages 215-226
    2. Hanuma Kumar, Rohit Paravastu, Vikram Pudi
      Pages 227-238
    3. Ashraf Elsayed, Frans Coenen, Marta García-Fiñana, Vanessa Sluming
      Pages 239-250
  8. Pattern Mining

    1. Petr Berka, Jan Rauch
      Pages 251-262
    2. Corrado Loglisci, Michelangelo Ceci, Donato Malerba
      Pages 263-274
    3. Kang Deng, Osmar R. Zaïane
      Pages 275-284
    4. Anamika Gupta, Vasudha Bhatnagar, Naveen Kumar
      Pages 285-296
  9. Data Cleaning and Variable Selection

    1. Frantchesco Cecchin, Cristina Dutra de Aguiar Ciferri, Carmem Satie Hara
      Pages 297-308
    2. Alfredo Ferro, Rosalba Giugno, Piera Laura Puglisi, Alfredo Pulvirenti
      Pages 309-323
    3. Françoise Fessant, Aurélie Le Cam, Marc Boullé, Raphaël Féraud
      Pages 324-335
  10. Back Matter

About these proceedings

Introduction

Data warehousing and knowledge discovery has been widely accepted as a key te- nology for enterprises and organizations to improve their abilities in data analysis, decision support, and the automatic extraction of knowledge from data. With the exponentially growing amount of information to be included in the decision-making process, the data to be considered become more and more complex in both structure and semantics. New developments such as cloud computing add to the challenges with massive scaling, a new computing infrastructure, and new types of data. Consequently, the process of retrieval and knowledge discovery from this huge amount of heterogeneous complex data forms the litmus test for research in the area. In the last decade, the International Conference on Data Warehousing and Kno- edge Discovery (DaWaK) has become one of the most important international sci- tific events bringing together researchers, developers, and practitioners to discuss the latest research issues and experiences in developing and deploying data warehousing and knowledge discovery systems, applications, and solutions. th This year’s conference, the 12 International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2010), continued the tradition by discussing and disseminating innovative principles, methods, algorithms, and solutions to challe- ing problems faced in the development of data warehousing, knowledge discovery, the emerging area of "cloud intelligence," and applications within these areas. In order to better reflect novel trends and the diversity of topics, the conference was organized in four tracks: Cloud Intelligence, Data Warehousing, Knowledge Discovery, and Industry and Applications.

Keywords

Business Intelligence algorithms business process intelligence clustering data cleansing data mining data warehouse database management knowledge discovery

Editors and affiliations

  • Torben Bach Pedersen
    • 1
  • Mukesh K. Mohania
    • 2
  • A Min Tjoa
    • 3
  1. 1.Department of Computer ScienceAalborg UniversityAalborgDenmark
  2. 2.IBM India Research Lab, 4, Block C, Institutional AreaNew DelhiIndia
  3. 3.Institute of Software TechnologyVienna University of TechnologyViennaAustria

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-15105-7
  • Copyright Information Springer-Verlag Heidelberg 2010
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-15104-0
  • Online ISBN 978-3-642-15105-7
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site