Data Warehousing and Knowledge Discovery

10th International Conference, DaWak 2008 Turin, Italy, September 1-5, 2008, Proceedings
Herausgegeben:Song, Il-Yeol; Song, Il-Yeol; Nguyen, Tho Manh; Eder, Johann

• Broschiertes Buch

Produktbeschreibung

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.

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Produktdetails

• Lecture Notes in Computer Science 5182
• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 12466535, 978-3-540-85835-5
• 2008
• Seitenzahl: 452
• Erscheinungstermin: 18. August 2008
• Englisch
• Abmessung: 235mm x 155mm x 25mm
• Gewicht: 679g
• ISBN-13: 9783540858355
• ISBN-10: 3540858350
• Artikelnr.: 25047806

Inhaltsangabe

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.