Data Warehousing and Knowledge Discovery: 9th International Conference, DaWaK 2007, Regensburg, Germany, September 3-7, 2007, Proceedings

Data Warehousing and Knowledge Discovery have been widely accepted as key te- nologies 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 processed become more and more complex in both structure and semantics. Consequently, the process of retrieval and knowledge discovery 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 bringing together researchers, developers and practitioners. The DaWaK conferences served as a prominent forum for discussing latest research issues and experiences in developing and deploying data warehousing and knowledge d- covery systems, applications, and solutions. This year’s conference, the Ninth Inter- tional Conference on Data Warehousing and Knowledge Discovery (DaWaK 2007), built on this tradition of facilitating the cross-disciplinary exchange of ideas, expe- ence and potential research directions. DaWaK 2007 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.

Table of Contents:
Data Warehouse Architecture.- A Hilbert Space Compression Architecture for Data Warehouse Environments.- Evolution of Data Warehouses’ Optimization: A Workload Perspective.- What-If Analysis for Data Warehouse Evolution.- Data Warehouse Quality.- An Extensible Metadata Framework for Data Quality Assessment of Composite Structures.- Automating the Schema Matching Process for Heterogeneous Data Warehouses.- A Dynamic View Materialization Scheme for Sequences of Query and Update Statements.- Multidimensional Database.- Spatio-temporal Aggregations in Trajectory Data Warehouses.- Computing Join Aggregates over Private Tables.- An Annotation Management System for Multidimensional Databases.- Data Warehouse and OLAP.- On the Need of a Reference Algebra for OLAP.- OLAP Technology for Business Process Intelligence: Challenges and Solutions.- Built-In Indicators to Automatically Detect Interesting Cells in a Cube.- Emerging Cubes for Trends Analysis in Olap Databases.- Query Optimization.- Domination Mining and Querying.- Semantic Knowledge Integration to Support Inductive Query Optimization.- A Clustered Dwarf Structure to Speed Up Queries on Data Cubes.- Data Warehousing and Data Mining.- An OLAM-Based Framework for Complex Knowledge Pattern Discovery in Distributed-and-Heterogeneous-Data-Sources and Cooperative Information Systems.- Integrating Clustering Data Mining into the Multidimensional Modeling of Data Warehouses with UML Profiles.- A UML Profile for Representing Business Object States in a Data Warehouse.- Selection and Pruning Algorithms for Bitmap Index Selection Problem Using Data Mining.- Clustering.- MOSAIC: A Proximity Graph Approach for Agglomerative Clustering.- A Hybrid Particle Swarm Optimization Algorithm for Clustering Analysis.- Clustering Transactionswith an Unbalanced Hierarchical Product Structure.- Constrained Graph b-Coloring Based Clustering Approach.- Association Rules.- An Efficient Algorithm for Identifying the Most Contributory Substring.- Mining High Utility Quantitative Association Rules.- Extraction of Association Rules Based on Literalsets.- Healthcare and Biomedical Applications.- Cost-Sensitive Decision Trees Applied to Medical Data.- Utilization of Global Ranking Information in Graph- Based Biomedical Literature Clustering.- Ontology-Based Information Extraction and Information Retrieval in Health Care Domain.- Classification.- Fuzzy Classifier Based Feature Reduction for Better Gene Selection.- Two Way Focused Classification.- A Markov Blanket Based Strategy to Optimize the Induction of Bayesian Classifiers When Using Conditional Independence Learning Algorithms.- Learning of Semantic Sibling Group Hierarchies - K-Means vs. Bi-secting-K-Means.- Partitioning.- Mining Top-K Multidimensional Gradients.- A Novel Similarity-Based Modularity Function for Graph Partitioning.- Dual Dimensionality Reduction for Efficient Video Similarity Search.- Privacy and Crytography.- Privacy-Preserving Genetic Algorithms for Rule Discovery.- Fast Cryptographic Multi-party Protocols for Computing Boolean Scalar Products with Applications to Privacy-Preserving Association Rule Mining in Vertically Partitioned Data.- Privacy-Preserving Self-Organizing Map.- Miscellaneous Knowledge Discovery Techniques.- DWFIST: Leveraging Calendar-Based Pattern Mining in Data Streams.- Expectation Propagation in GenSpace Graphs for Summarization.- Mining First-Order Temporal Interval Patterns with Regular Expression Constraints.- Mining Trajectory Patterns Using Hidden Markov Models.