Data Warehousing and Knowledge Discovery

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.

Klappentext

This book constitutes the refereed proceedings of the 10th International Conference on Data Warehousing and Knowledge Discovery, DaWak 2008, held in Turin, Italy, in September 2008.

The 40 revised full papers presented were carefully reviewed and selected from 143 submissions. The papers are organized in topical sections on conceptual design and modeling, olap and cube processing, distributed data warehouse, data privacy in data warehouse, data warehouse and data mining, clustering, mining data streams, classification, text mining and taxonomy, machine learning techniques, and data mining applications.

Inhalt
Conceptual Design and Modeling.- UML-Based Modeling for What-If Analysis.- Model-Driven Metadata for OLAP Cubes from the Conceptual Modelling of Data Warehouses.- An MDA Approach for the Development of Spatial Data Warehouses.- OLAP and Cube Processing.- Built-In Indicators to Discover Interesting Drill Paths in a Cube.- Upper Borders for Emerging Cubes.- Top_Keyword: An Aggregation Function for Textual Document OLAP.- Distributed Data Warehouse.- Summarizing Distributed Data Streams for Storage in Data Warehouses.- Efficient Data Distribution for DWS.- Data Partitioning in Data Warehouses: Hardness Study, Heuristics and ORACLE Validation.- Data Privacy in Data Warehouse.- A Robust Sampling-Based Framework for Privacy Preserving OLAP.- Generalization-Based Privacy-Preserving Data Collection.- Processing Aggregate Queries on Spatial OLAP Data.- Data Warehouse and Data Mining.- Efficient Incremental Maintenance of Derived Relations and BLAST Computations in Bioinformatics Data Warehouses.- Mining Conditional Cardinality Patterns for Data Warehouse Query Optimization.- Up and Down: Mining Multidimensional Sequential Patterns Using Hierarchies.- Clustering I.- Efficient K-Means Clustering Using Accelerated Graphics Processors.- Extracting Knowledge from Life Courses: Clustering and Visualization.- A Hybrid Clustering Algorithm Based on Multi-swarm Constriction PSO and GRASP.- Clustering II.- Personalizing Navigation in Folksonomies Using Hierarchical Tag Clustering.- Clustered Dynamic Conditional Correlation Multivariate GARCH Model.- Document Clustering by Semantic Smoothing and Dynamic Growing Cell Structure (DynGCS) for Biomedical Literature.- Mining Data Streams.- Mining Serial Episode Rules with Time Lags over Multiple Data Streams.- Efficient Approximate Mining of Frequent Patterns over Transactional Data Streams.- Continuous Trend-Based Clustering in Data Streams.- Mining Multidimensional Sequential Patterns over Data Streams.- Classification.- Towards a Model Independent Method for Explaining Classification for Individual Instances.- Selective Pre-processing of Imbalanced Data for Improving Classification Performance.- A Parameter-Free Associative Classification Method.- Text Mining and Taxonomy I.- The Evaluation of Sentence Similarity Measures.- Labeling Nodes of Automatically Generated Taxonomy for Multi-type Relational Datasets.- Towards the Automatic Construction of Conceptual Taxonomies.- Text Mining and Taxonomy II.- Adapting LDA Model to Discover Author-Topic Relations for Email Analysis.- A New Semantic Representation for Short Texts.- Document-Base Extraction for Single-Label Text Classification.- Machine Learning Techniques.- How an Ensemble Method Can Compute a Comprehensible Model.- Empirical Analysis of Reliability Estimates for Individual Regression Predictions.- User Defined Partitioning - Group Data Based on Computation Model.- Data Mining Applications.- Workload-Aware Histograms for Remote Applications.- Is a Voting Approach Accurate for Opinion Mining?.- Mining Sequential Patterns with Negative Conclusions.