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Data Warehousing and Knowledge Discovery 5th International Conference, DaWaK 2003, Prague, Czech Republic, September 3-5,2003, Proceedings

Aus der Reihe Lecture Notes in Computer Science

51,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

21.08.2003

Herausgeber

Yahiko Kambayashi + weitere

Verlag

Springer Berlin

Seitenzahl

438

Maße (L/B/H)

23,5/15,5/2,5 cm

Gewicht

1400 g

Auflage

2003

Sprache

Englisch

ISBN

978-3-540-40807-9

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

21.08.2003

Herausgeber

Verlag

Springer Berlin

Seitenzahl

438

Maße (L/B/H)

23,5/15,5/2,5 cm

Gewicht

1400 g

Auflage

2003

Sprache

Englisch

ISBN

978-3-540-40807-9

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: Data Warehousing and Knowledge Discovery
  • Produktbild: Data Warehousing and Knowledge Discovery
  • 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.