Data Warehousing and Knowledge Discovery

1. Front Matter

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2. Conceptual Design and Modeling

   1. UML-Based Modeling for What-If Analysis

      • Matteo Golfarelli, Stefano Rizzi

      Pages 1-12

   2. Model-Driven Metadata for OLAP Cubes from the Conceptual Modelling of Data Warehouses

      • Jesús Pardillo, Jose-Norberto Mazón, Juan Trujillo

      Pages 13-22

   3. An MDA Approach for the Development of Spatial Data Warehouses

      • Octavio Glorio, Juan Trujillo

      Pages 23-32

3. OLAP and Cube Processing

   1. Built-In Indicators to Discover Interesting Drill Paths in a Cube

      • Véronique Cariou, Jérôme Cubillé, Christian Derquenne, Sabine Goutier, Françoise Guisnel, Henri Klajnmic

      Pages 33-44

   2. Upper Borders for Emerging Cubes

      • Sébastien Nedjar, Alain Casali, Rosine Cicchetti, Lotfi Lakhal

      Pages 45-54

   3. Top_Keyword: An Aggregation Function for Textual Document OLAP

      • Franck Ravat, Olivier Teste, Ronan Tournier, Gilles Zurfluh

      Pages 55-64

4. Distributed Data Warehouse

   1. Summarizing Distributed Data Streams for Storage in Data Warehouses

      • Raja Chiky, Georges Hébrail

      Pages 65-74

   2. Efficient Data Distribution for DWS

      • Raquel Almeida, Jorge Vieira, Marco Vieira, Henrique Madeira, Jorge Bernardino

      Pages 75-86

   3. Data Partitioning in Data Warehouses: Hardness Study, Heuristics and ORACLE Validation

      • Ladjel Bellatreche, Kamel Boukhalfa, Pascal Richard

      Pages 87-96

5. Data Privacy in Data Warehouse

   1. A Robust Sampling-Based Framework for Privacy Preserving OLAP

      • Alfredo Cuzzocrea, Vincenzo Russo, Domenico Saccà

      Pages 97-114

   2. Generalization-Based Privacy-Preserving Data Collection

      • Lijie Zhang, Weining Zhang

      Pages 115-124

   3. Processing Aggregate Queries on Spatial OLAP Data

      • Kenneth Choi, Wo-Shun Luk

      Pages 125-134

6. Data Warehouse and Data Mining

   1. Efficient Incremental Maintenance of Derived Relations and BLAST Computations in Bioinformatics Data Warehouses

      • Gabriela Turcu, Svetlozar Nestorov, Ian Foster

      Pages 135-145

   2. Mining Conditional Cardinality Patterns for Data Warehouse Query Optimization

      • Mikołaj Morzy, Marcin Krystek

      Pages 146-155

   3. Up and Down: Mining Multidimensional Sequential Patterns Using Hierarchies

      • Marc Plantevit, Anne Laurent, Maguelonne Teisseire

      Pages 156-165

7. Clustering I

   1. Efficient K-Means Clustering Using Accelerated Graphics Processors

      • S. A. Arul Shalom, Manoranjan Dash, Minh Tue

      Pages 166-175

   2. Extracting Knowledge from Life Courses: Clustering and Visualization

      • Nicolas S. Müller, Alexis Gabadinho, Gilbert Ritschard, Matthias Studer

      Pages 176-185

   3. A Hybrid Clustering Algorithm Based on Multi-swarm Constriction PSO and GRASP

      • Yannis Marinakis, Magdalene Marinaki, Nikolaos Matsatsinis

      Pages 186-195

8. Clustering II

   1. Personalizing Navigation in Folksonomies Using Hierarchical Tag Clustering

      • Jonathan Gemmell, Andriy Shepitsen, Bamshad Mobasher, Robin Burke

      Pages 196-205

Data Warehousing and Knowledge Discovery have been widely accepted as key te- nologies for enterprises and organizations as a means of improving 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 processed is becoming more and more complex in both structure and semantics. Consequently, the process of retrieval and knowledge disc- ery from this huge amount of heterogeneous complex data constitutes the reality check for research in the area. During the past few years, the International Conference on Data Warehousing and Knowledge Discovery (DaWaK) has become one of the most important international scientific events to bring together researchers, developers and practitioners. The DaWaK conferences serve as a prominent forum for discussing the latest research issues and experiences in developing and deploying data warehousing and knowledge discovery systems, applications, and solutions. This year’s conference, the 10th Int- national Conference on Data Warehousing and Knowledge Discovery (DaWaK 2008), continued the tradition of facilitating the cross-disciplinary exchange of ideas, expe- ence and potential research directions. DaWaK 2008 sought to disseminate innovative principles, methods, algorithms and solutions to challenging problems faced in the development of data warehousing, knowledge discovery and data mining applications.

• Clustering
• classification
• data mining
• data warehouse
• knowledge
• knowledge discovery
• learning
• machine learning
• modeling
• text mining

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