目次

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.