Data Warehousing and Knowledge Discovery

Data warehousing and knowledge discovery are increasingly becoming mission-critical technologies for most organizations, both commercial and public, as it becomes incre- ingly important to derive important knowledge from both internal and external data sources. With the ever growing amount and complexity of the data and information available for decision making, the process of data integration, analysis, and knowledge discovery continues to meet new challenges, leading to a wealth of new and exciting research challenges within the area. Over the last decade, the International Conference on Data Warehousing and Knowledge Discovery (DaWaK) has established itself as one of the most important international scientific events within data warehousing and knowledge discovery. DaWaK brings together a wide range of researchers and practitioners working on these topics. The DaWaK conference series thus serves as a leading forum for discu- ing novel research results and experiences within data warehousing and knowledge th discovery. This year's conference, the 11 International Conference on Data Wa- housing and Knowledge Discovery (DaWaK 2009), continued the tradition by d- seminating and discussing innovative models, methods, algorithms, and solutions to the challenges faced by data warehousing and knowledge discovery technologies.

Klappentext

This book constitutes the refereed proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery, DaWak 2009 held in Linz, Austria in August/September 2009.

The 36 revised full papers presented were carefully reviewed and selected from 124 submissions. The papers are organized in topical sections on data warehouse modeling, data streams, physical design, pattern mining, data cubes, data mining applications, analytics, data mining, clustering, spatio-temporal mining, rule mining, and OLAP recommendation.

Inhalt
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.