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DataWarehousing and Knowledge Discovery

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Cover of 'DataWarehousing and Knowledge Discovery'

Table of Contents

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    Book Overview
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    Chapter 1 Dynamic Data Warehouse Design
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    Chapter 2 Star/Snow-Flake Schema Driven Object-Relational Data Warehouse Design and Query Processing Strategies
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    Chapter 3 The Design and Implementation of Modularized Wrappers/Monitors in a Data Warehouse
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    Chapter 4 Managing Meta Objects for Design of Warehouse Data
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    Chapter 5 Dealing with Complex Reports in OLAP Applications
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    Chapter 6 OLAP-based Scalable Profiling of Customer Behavior
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    Chapter 7 Compressed Datacubes for Fast OLAP Applications
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    Chapter 8 Compact Representation: An Approach to Efficient Implementation for the Data Warehouse Architecture
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    Chapter 9 On the Independence of Data Warehouse from Databases in Maintaining Join Views
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    Chapter 10 Heuristic Algorithms for Designing a Data Warehouse with SPJ Views
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    Chapter 11 POSSE: A Framework for Optimizing Incremental View Maintenance at Data Warehouses
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    Chapter 12 Genetic Algorithm for Materialized View Selection in Data Warehouse Environments
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    Chapter 13 Optimization of Sequences of Relational Queries in Decision-Support Environments
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    Chapter 14 Dynamic Data Warehousing
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    Chapter 15 Set-Derivability of Multidimensional Aggregates
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    Chapter 16 Using the Real Dimension of the Data
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    Chapter 17 On Schema Evolution in Multidimensional Databases
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    Chapter 18 Lazy Aggregates for Real-Time OLAP
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    Chapter 19 Incremental Refinement of Mining Queries
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    Chapter 20 The Item-Set Tree: A Data Structure for Data Mining
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    Chapter 21 A New Approach for the Discovery of Frequent Itemsets
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    Chapter 22 K-means Clustering Algorithm for Categorical Attributes
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    Chapter 23 Considering Main Memory in Mining Association Rules
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    Chapter 24 Discovery of Association Rule Meta-Patterns
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    Chapter 25 Fuzzy Functional Dependencies and Fuzzy Association Rules
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    Chapter 26 Performance Evaluation and Optimization of Join Queries for Association Rule Mining
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    Chapter 27 Efficient Bulk Loading of Large High-Dimensional Indexes
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    Chapter 28 Implementation of Multidimensional Index Structures for Knowledge Discovery in Relational Databases
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    Chapter 29 Similarity between Event Types in Sequences
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    Chapter 30 Mining Generalized Association Rule Using Parallel RDB Engine on PC Cluster
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    Chapter 31 Modeling KDD Processes within the Inductive Database Framework
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    Chapter 32 Research Issues in Web Data Mining
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    Chapter 33 DAMISYS: An Overview
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    Chapter 34 Mining Interval Time Series
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    Chapter 35 A New Modeling Technique Based on Markov Chains to Mine Behavioral Patterns in Event Based Time Series
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    Chapter 36 SQL/LPP+: A Cascading Query Language for Temporal Correlation Verification
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    Chapter 37 Temporal Structures in Data Warehousing
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    Chapter 38 Target Group Selection in Retail Banking through Neuro-Fuzzy Data Mining and Extensive Pre- and Postprocessing
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    Chapter 39 Using Data Mining Techniques in Fiscal Fraud Detection
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    Chapter 40 Analysis of Accuracy of Data Reduction Techniques
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    Chapter 41 Data Swapping: Balancing Privacy against Precision in Mining for Logic Rules
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Title
DataWarehousing and Knowledge Discovery
Published by
Springer, Berlin, Heidelberg, January 1999
DOI 10.1007/3-540-48298-9
ISBNs
978-3-54-066458-1, 978-3-54-048298-7
Editors

Mukesh Mohania, A Min Tjoa

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Professor 1 50%
Student > Ph. D. Student 1 50%
Readers by discipline Count As %
Computer Science 1 50%
Engineering 1 50%