Data Warehousing and Knowledge Discovery de Mukesh Mohania

Data Warehousing and Knowledge Discovery: First International Conference, DaWaK'99 Florence, Italy, August 30 - September 1, 1999 Proceedings: Lecture Notes in Computer Science, cartea 1676

Mukesh Mohania

A Min Tjoa

en Limba Engleză Paperback – 20 aug 1999

This book constitutes the refereed proceedings of the First International Conference on Data Warehousing and Knowledge Discovery, DaWaK'99, held in Florence, Italy in August/September 1999. The 31 revised full papers and nine short papers presented were carefully reviewed and selected from 88 submissions. The book is divided in topical sections on data warehouse design; online analytical processing; view synthesis, selection, and optimization; multidimensional databases; knowledge discovery; association rules; inexing and object similarities; generalized association rules and data and web mining; time series data bases; data mining applications and data analysis.

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Cuprins

Data Warehouse Design.- Dynamic Data Warehouse Design.- Star/Snow-Flake Schema Driven Object-Relational Data Warehouse Design and Query Processing Strategies.- The Design and Implementation of Modularized Wrappers/Monitors in a Data Warehouse.- Managing Meta Objects for Design of Warehouse Data.- On-Line Analytical Processing.- Dealing with Complex Reports in OLAP Applications.- OLAP-based Scalable Profiling of Customer Behavior.- Compressed Datacubes for Fast OLAP Applications.- Compact Representation: An Approach to Efficient Implementation for the Data Warehouse Architecture.- View Maintenance, Selection and Optimisation.- On the Independence of Data Warehouse from Databases in Maintaining Join Views.- Heuristic Algorithms for Designing a Data Warehouse with SPJ Views.- POSSE: A Framework for Optimizing Incremental View Maintenance at Data Warehouses.- Genetic Algorithm for Materialized View Selection in Data Warehouse Environments.- Optimization of Sequences of Relational Queries in Decision-Support Environments.- Invited Talk.- Dynamic Data Warehousing.- Multidimensional Databases.- Set-Derivability of Multidimensional Aggregates.- Using the Real Dimension of the Data.- On Schema Evolution in Multidimensional Databases.- Lazy Aggregates for Real-Time OLAP.- Knowledge Discovery.- Incremental Refinement of Mining Queries.- The Item-Set Tree: A Data Structure for Data Mining.- A New Approach for the Discovery of Frequent Itemsets.- K-means Clustering Algorithm for Categorical Attributes.- Association Rules.- Considering Main Memory in Mining Association Rules.- Discovery of Association Rule Meta-Patterns.- Fuzzy Functional Dependencies and Fuzzy Association Rules.- Performance Evaluation and Optimization of Join Queries for Association Rule Mining.- Indexing and Object Similarities.- Efficient Bulk Loading of Large High-Dimensional Indexes.- Implementation of Multidimensional Index Structures for Knowledge Discovery in Relational Databases.- Similarity between Event Types in Sequences.- Generalised Association Rules and Data & Web Mining.- Mining Generalized Association Rule Using Parallel RDB Engine on PC Cluster.- Modeling KDD Processes within the Inductive Database Framework.- Research Issues in Web Data Mining.- DAMISYS: An Overview.- Time Series Databases.- Mining Interval Time Series.- A New Modeling Technique Based on Markov Chains to Mine Behavioral Patterns in Event Based Time Series.- SQL/LPP+: A Cascading Query Language for Temporal Correlation Verification.- Temporal Structures in Data Warehousing.- Data Mining Applications and Data Analysis.- Target Group Selection in Retail Banking through Neuro-Fuzzy Data Mining and Extensive Pre- and Postprocessing.- Using Data Mining Techniques in Fiscal Fraud Detection.- Analysis of Accuracy of Data Reduction Techniques.- Data Swapping: Balancing Privacy against Precision in Mining for Logic Rules.