Data Warehousing and Knowledge Discovery : 9th International Conference, DaWaK 2007, Regensburg, Germany, September 3-7, 2007, Proceedings / edited by Il Yeol Song, Johann Eder, Tho Manh Nguyen.

Format:

Book

Contributor:

Song, Il-Yeol, editor.

Eder, J. (Johann), 1958- editor.

Nguyen, Tho Manh, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

LNCS sublibrary. Information systems and applications, incl. Internet/Web, and HCI ; SL 3, 4654.

Information Systems and Applications, incl. Internet/Web, and HCI ; 4654

Language:

English

Subjects (All):

Data structures (Computer science).

Database management.

Data mining.

Application software.

Computer networks.

Artificial intelligence.

Data Structures and Information Theory.

Database Management.

Data Mining and Knowledge Discovery.

Information Systems Applications (incl. Internet).

Computer Communication Networks.

Artificial Intelligence.

Local Subjects:

Data Structures and Information Theory.

Database Management.

Data Mining and Knowledge Discovery.

Information Systems Applications (incl. Internet).

Computer Communication Networks.

Artificial Intelligence.

Physical Description:

1 online resource (XVI, 484 pages).

Edition:

First edition 2007.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2007.

System Details:

text file PDF

Summary:

Data Warehousing and Knowledge Discovery have been widely accepted as key te- nologies for enterprises and organizations to improve 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 become more and more complex in both structure and semantics. Consequently, the process of retrieval and knowledge discovery 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 bringing together researchers, developers and practitioners. The DaWaK conferences served as a prominent forum for discussing latest research issues and experiences in developing and deploying data warehousing and knowledge d- covery systems, applications, and solutions. This year's conference, the Ninth Inter- tional Conference on Data Warehousing and Knowledge Discovery (DaWaK 2007), built on this tradition of facilitating the cross-disciplinary exchange of ideas, expe- ence and potential research directions. DaWaK 2007 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.

Contents:

Data Warehouse Architecture

A Hilbert Space Compression Architecture for Data Warehouse Environments

Evolution of Data Warehouses' Optimization: A Workload Perspective

What-If Analysis for Data Warehouse Evolution

Data Warehouse Quality

An Extensible Metadata Framework for Data Quality Assessment of Composite Structures

Automating the Schema Matching Process for Heterogeneous Data Warehouses

A Dynamic View Materialization Scheme for Sequences of Query and Update Statements

Multidimensional Database

Spatio-temporal Aggregations in Trajectory Data Warehouses

Computing Join Aggregates over Private Tables

An Annotation Management System for Multidimensional Databases

Data Warehouse and OLAP

On the Need of a Reference Algebra for OLAP

OLAP Technology for Business Process Intelligence: Challenges and Solutions

Built-In Indicators to Automatically Detect Interesting Cells in a Cube

Emerging Cubes for Trends Analysis in Olap Databases

Query Optimization

Domination Mining and Querying

Semantic Knowledge Integration to Support Inductive Query Optimization

A Clustered Dwarf Structure to Speed Up Queries on Data Cubes

Data Warehousing and Data Mining

An OLAM-Based Framework for Complex Knowledge Pattern Discovery in Distributed-and-Heterogeneous-Data-Sources and Cooperative Information Systems

Integrating Clustering Data Mining into the Multidimensional Modeling of Data Warehouses with UML Profiles

A UML Profile for Representing Business Object States in a Data Warehouse

Selection and Pruning Algorithms for Bitmap Index Selection Problem Using Data Mining

Clustering

MOSAIC: A Proximity Graph Approach for Agglomerative Clustering

A Hybrid Particle Swarm Optimization Algorithm for Clustering Analysis

Clustering Transactions with an Unbalanced Hierarchical Product Structure

Constrained Graph b-Coloring Based Clustering Approach

Association Rules

An Efficient Algorithm for Identifying the Most Contributory Substring

Mining High Utility Quantitative Association Rules

Extraction of Association Rules Based on Literalsets

Healthcare and Biomedical Applications

Cost-Sensitive Decision Trees Applied to Medical Data

Utilization of Global Ranking Information in Graph- Based Biomedical Literature Clustering

Ontology-Based Information Extraction and Information Retrieval in Health Care Domain

Classification

Fuzzy Classifier Based Feature Reduction for Better Gene Selection

Two Way Focused Classification

A Markov Blanket Based Strategy to Optimize the Induction of Bayesian Classifiers When Using Conditional Independence Learning Algorithms

Learning of Semantic Sibling Group Hierarchies - K-Means vs. Bi-secting-K-Means

Partitioning

Mining Top-K Multidimensional Gradients

A Novel Similarity-Based Modularity Function for Graph Partitioning

Dual Dimensionality Reduction for Efficient Video Similarity Search

Privacy and Crytography

Privacy-Preserving Genetic Algorithms for Rule Discovery

Fast Cryptographic Multi-party Protocols for Computing Boolean Scalar Products with Applications to Privacy-Preserving Association Rule Mining in Vertically Partitioned Data

Privacy-Preserving Self-Organizing Map

Miscellaneous Knowledge Discovery Techniques

DWFIST: Leveraging Calendar-Based Pattern Mining in Data Streams

Expectation Propagation in GenSpace Graphs for Summarization

Mining First-Order Temporal Interval Patterns with Regular Expression Constraints

Mining Trajectory Patterns Using Hidden Markov Models.

Other Format:

Printed edition:

ISBN:

978-3-540-74553-2

9783540745532

Access Restriction:

Restricted for use by site license.