Data Warehousing and Knowledge Discovery

Table of Contents
Conceptual Design and Modeling
UML-Based Modeling for What-If Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Matteo Golfarelli and Stefano Rizzi
Model-Driven Metadata for OLAP Cubes from the Conceptual
Modelling of Data Warehouses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Jes´us Pardillo, Jose-Norberto Maz´on, and Juan Trujillo
An MDA Approach for the Development of Spatial Data Warehouses . . . 23
Octavio Glorio and Juan Trujillo
OLAP and Cube Processing
Built-In Indicators to Discover Interesting Drill Paths in a Cube . . . . . . . 33
V´eronique Cariou, J´erˆome Cubill´e, Christian Derquenne,
Sabine Goutier, Fran¸coise Guisnel, and Henri Klajnmic
Upper Borders for Emerging Cubes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
S´ebastien Nedjar, Alain Casali, Rosine Cicchetti, and Lotfi Lakhal
Top Keyword: An Aggregation Function for Textual Document
OLAP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
Franck Ravat, Olivier Teste, Ronan Tournier, and Gilles Zurfluh
Distributed Data Warehouse
Summarizing Distributed Data Streams for Storage in Data
Warehouses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
Raja Chiky and Georges H´ebrail
Efficient Data Distribution for DWS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
Raquel Almeida, Jorge Vieira, Marco Vieira,
Henrique Madeira, and Jorge Bernardino
Data Partitioning in Data Warehouses: Hardness Study, Heuristics and
ORACLE Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
Ladjel Bellatreche, Kamel Boukhalfa, and Pascal Richard
Data Privacy in Data Warehouse
A Robust Sampling-Based Framework for Privacy Preserving OLAP . . . . 97
Alfredo Cuzzocrea, Vincenzo Russo, and Domenico Sacc`a
Generalization-Based Privacy-Preserving Data Collection . . . . . . . . . . . . . 115
Lijie Zhang and Weining Zhang
Processing Aggregate Queries on Spatial OLAP Data . . . . . . . . . . . . . . . . . 125
Kenneth Choi and Wo-Shun Luk
Data Warehouse and Data Mining
Efficient Incremental Maintenance of Derived Relations and BLAST
Computations in Bioinformatics Data Warehouses . . . . . . . . . . . . . . . . . . . . 135
Gabriela Turcu, Svetlozar Nestorov, and Ian Foster
Mining Conditional Cardinality Patterns for Data Warehouse Query
Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
Miko laj Morzy and Marcin Krystek
Up and Down: Mining Multidimensional Sequential Patterns Using
Hierarchies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
Marc Plantevit, Anne Laurent, and Maguelonne Teisseire
Clustering I
Efficient K-Means Clustering Using Accelerated Graphics Processors . . . 166
S.A. Arul Shalom, Manoranjan Dash, and Minh Tue
Extracting Knowledge from Life Courses: Clustering and
Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
Nicolas S. M¨uller, Alexis Gabadinho, Gilbert Ritschard, and
Matthias Studer
A Hybrid Clustering Algorithm Based on Multi-swarm Constriction
PSO and GRASP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186
Yannis Marinakis, Magdalene Marinaki, and Nikolaos Matsatsinis
Clustering II
Personalizing Navigation in Folksonomies Using Hierarchical Tag
Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196
Jonathan Gemmell, Andriy Shepitsen, Bamshad Mobasher, and
Robin Burke
Clustered Dynamic Conditional Correlation Multivariate GARCH
Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206
Tu Zhou and Laiwan Chan
Document Clustering by Semantic Smoothing and Dynamic Growing
Cell Structure (DynGCS) for Biomedical Literature . . . . . . . . . . . . . . . . . . 217
Min Song, Xiaohua Hu, Illhoi Yoo, and Eric Koppel
Mining Data Streams
Mining Serial Episode Rules with Time Lags over Multiple Data
Streams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227
Tung-Ying Lee, En Tzu Wang, and Arbee L.P. Chen
Efficient Approximate Mining of Frequent Patterns over Transactional
Data Streams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241
Willie Ng and Manoranjan Dash
Continuous Trend-Based Clustering in Data Streams . . . . . . . . . . . . . . . . . 251
Maria Kontaki, Apostolos N. Papadopoulos, and
Yannis Manolopoulos
Mining Multidimensional Sequential Patterns over Data Streams . . . . . . . 263
Chedy Ra¨ıssi and Marc Plantevit
Classification
Towards a Model Independent Method for Explaining Classification for
Individual Instances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273
Erik
ˇ
Strumbelj and Igor Kononenko
Selective Pre-processing of Imbalanced Data for Improving
Classification Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283
Jerzy Stefanowski and Szymon Wilk
A Parameter-Free Associative Classification Method . . . . . . . . . . . . . . . . . . 293
Lo¨ıc Cerf, Dominique Gay, Nazha Selmaoui, and
Jean-Fran¸cois Boulicaut
Text Mining and Taxonomy I
The Evaluation of Sentence Similarity Measures . . . . . . . . . . . . . . . . . . . . . 305
Palakorn Achananuparp, Xiaohua Hu, and Xiajiong Shen
Labeling Nodes of Automatically Generated Taxonomy for Multi-type
Relational Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317
Tao Li and Sarabjot S. Anand
Towards the Automatic Construction of Conceptual Taxonomies . . . . . . . 327
Dino Ienco and Rosa Meo
Text Mining and Taxonomy II
Adapting LDA Model to Discover Author-Topic Relations for Email
Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337
Liqiang Geng, Hao Wang, Xin Wang, and Larry Korba
A New Semantic Representation for Short Texts . . . . . . . . . . . . . . . . . . . . . 347
M.J. Mart´ın-Bautista, S. Mart´ınez-Folgoso, and M.A. Vila
Document-Base Extraction for Single-Label Text Classification . . . . . . . . 357
Yanbo J. Wang, Robert Sanderson, Frans Coenen, and Paul Leng
Machine Learning Techniques
How an Ensemble Method Can Compute a Comprehensible Model . . . . . 368
Jos´e L. Trivi˜no-Rodriguez, Amparo Ruiz-Sep´ulveda, and
Rafael Morales-Bueno
Empirical Analysis of Reliability Estimates for Individual Regression
Predictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379
Zoran Bosni´c and Igor Kononenko
User Defined Partitioning - Group Data Based on Computation
Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 389
Qiming Chen and Meichun Hsu
Data Mining Applications
Workload-Aware Histograms for Remote Applications . . . . . . . . . . . . . . . . 402
Tanu Malik and Randal Burns
Is a Voting Approach Accurate for Opinion Mining? . . . . . . . . . . . . . . . . . . 413
Michel Planti´e, Mathieu Roche, G´erard Dray, and Pascal Poncelet
Mining Sequential Patterns with Negative Conclusions . . . . . . . . . . . . . . . . 423
Przemys law Kazienko
Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433



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