Data warehousing and knowledge discovery : 9th International Conference, DaWaK 2007, Regensburg, Germany, September 3-7, 2007 : proceedings
著者
書誌事項
Data warehousing and knowledge discovery : 9th International Conference, DaWaK 2007, Regensburg, Germany, September 3-7, 2007 : proceedings
（Lecture notes in computer science, 4654）
Springer, c2007
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
This book constitutes the refereed proceedings of the 8th International Conference on Data Warehousing and Knowledge Discovery, DaWak 2007, held in Regensburg, Germany, September 2007. Coverage includes ETL processing, multidimensional design, OLAP and multidimensional model, cubes processing, data warehouse applications, frequent itemsets, ontology-based mining, clustering, association rules, miscellaneous applications, and classification.
目次
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 Transactions with 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.
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