Data Warehousing and Knowledge Discovery: 5th International Conference, DaWaK 2003, Prague, Czech Republic, September 3-5,2003, Proceedings (Lecture Notes in Computer Science) - Taschenbuch

EAN (ISBN-13): 9783540408079
ISBN (ISBN-10): 354040807X
Taschenbuch
Erscheinungsjahr: 2003
Herausgeber: Springer Berlin
432 Seiten
Gewicht: 0,678 kg

Buch in der Datenbank seit 2007-12-19T04:38:56+01:00 (Zurich)
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ISBN/EAN: 9783540408079

ISBN - alternative Schreibweisen:
3-540-40807-X, 978-3-540-40807-9
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: wolfram, wos, wor, wöss, wolf
Titel des Buches: republic, prague, science, czech, data warehousing and knowledge discovery, september, 2003, ware

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Daten vom Verlag:

Autor/in: Yahiko Kambayashi; Mukesh Mohania; Wolfram Wöß
Titel: Lecture Notes in Computer Science; Data Warehousing and Knowledge Discovery - 5th International Conference, DaWaK 2003, Prague, Czech Republic, September 3-5,2003, Proceedings
Verlag: Springer; Springer Berlin
438 Seiten
Erscheinungsjahr: 2003-08-21
Berlin; Heidelberg; DE
Sprache: Englisch
53,49 € (DE)
54,99 € (AT)
59,00 CHF (CH)
Available
XIV, 438 p.

BC; Hardcover, Softcover / Informatik, EDV/Allgemeines, Lexika; Informatik; Verstehen; Data Warehouse; classification; clustering; data analysis; database; database systems; performance; performance evaluation; programming; semantic web; Computer Science; EA

Invited Talk.- XML for Data Warehousing Chances and Challenges.- Data Cubes and Queries.- CPM: A Cube Presentation Model for OLAP.- Computation of Sparse Data Cubes with Constraints.- Answering Joint Queries from Multiple Aggregate OLAP Databases.- An Approach to Enabling Spatial OLAP by Aggregating on Spatial Hierarchy.- Multidimensional Data Model.- A Multidimensional Aggregation Object (MAO) Framework for Computing Distributive Aggregations.- The GMD Data Model for Multidimensional Information: A Brief Introduction.- An Application of Case-Based Reasoning in Multidimensional Database Architecture.- Web Warehousing.- MetaCube XTM: A Multidimensional Metadata Approach for Semantic Web Warehousing Systems.- Designing Web Warehouses from XML Schemas.- Building XML Data Warehouse Based on Frequent Patterns in User Queries.- Change Detection.- A Temporal Study of Data Sources to Load a Corporate Data Warehouse.- Automatic Detection of Structural Changes in Data Warehouses.- Performance Tests in Data Warehousing ETLM Process for Detection of Changes in Data Origin.- Web Mining and Association Rule.- Recent Developments in Web Usage Mining Research.- Parallel Vector Computing Technique for Discovering Communities on the Very Large Scale Web Graph.- Association Rules and Decision Trees.- Ordinal Association Rules towards Association Rules.- Rough Set Based Decision Tree Model for Classification.- Inference Based Classifier: Efficient Construction of Decision Trees for Sparse Categorical Attributes.- Generating Effective Classifiers with Supervised Learning of Genetic Programming.- Clustering I.- Clustering by Regression Analysis.- Handling Large Workloads by Profiling and Clustering.- Incremental OPTICS: Efficient Computation of Updates in a Hierarchical Cluster Ordering.- Clustering II.- On Complementarity of Cluster and Outlier Detection Schemes.- Cluster Validity Using Support Vector Machines.- FSSM: Fast Construction of the Optimized Segment Support Map.- Association Rule Mining.- Using a Connectionist Approach for Enhancing Domain Ontologies: Self-Organizing Word Category Maps Revisited.- Parameterless Data Compression and Noise Filtering Using Association Rule Mining.- Performance Evaluation of SQL-OR Variants for Association Rule Mining.- Data Analysis and Discovery.- A Distance-Based Approach to Find Interesting Patterns.- Similarity Search in Structured Data.- Ontologies and Improving Data Quality.- Using an Interest Ontology for Improved Support in Rule Mining.- Fraud Formalization and Detection.- Combining Noise Correction with Feature Selection.- Queries and Data Patterns.- Pre-computing Approximate Hierarchical Range Queries in a Tree-Like Histogram.- Comprehensive Log Compression with Frequent Patterns.- Non-recursive Generation of Frequent K-itemsets from Frequent Pattern Tree Representations.- Improving Database Query Engine.- A New Computation Model for Rough Set Theory Based on Database Systems.- Computing SQL Queries with Boolean Aggregates.- Fighting Redundancy in SQL.- Sampling and Vector Classification.- “On-the-fly” VS Materialized Sampling and Heuristics.- Incremental and Decremental Proximal Support Vector Classification using Decay Coefficients.