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ORCID11th DaWaK 2009: Linz, Austria
- Torben Bach Pedersen, Mukesh K. Mohania, A Min Tjoa:
Data Warehousing and Knowledge Discovery, 11th International Conference, DaWaK 2009, Linz, Austria, August 31 - September 2, 2009, Proceedings. Lecture Notes in Computer Science 5691, Springer 2009, ISBN 978-3-642-03729-0
Invited Talk
- Laura M. Haas, Aya Soffer:
New Challenges in Information Integration. 1-8
Data Warehouse Modeling
- Alejandro A. Vaisman, Esteban Zimányi:
What Is Spatio-Temporal Data Warehousing? 9-23 - Carlos Blanco
, Ricardo Pérez-Castillo, Arnulfo Hernández, Eduardo Fernández-Medina, Juan Trujillo:
Towards a Modernization Process for Secure Data Warehouses. 24-35 - Jesús Pardillo, Matteo Golfarelli, Stefano Rizzi, Juan Trujillo:
Visual Modelling of Data Warehousing Flows with UML Profiles. 36-47
Data Streams
- Alfredo Cuzzocrea:
CAMS: OLAPing Multidimensional Data Streams Efficiently. 48-62 - Kei Wakabayashi, Takao Miura:
Data Stream Prediction Using Incremental Hidden Markov Models. 63-74 - Weiyun Huang, Edward Omiecinski, Leo Mark, Minh Quoc Nguyen:
History Guided Low-Cost Change Detection in Streams. 75-86
Physical Design
- Jan Chmiel, Tadeusz Morzy, Robert Wrembel:
HOBI: Hierarchically Organized Bitmap Index for Indexing Dimensional Data. 87-98 - Ladjel Bellatreche, Soumia Benkrid:
A Joint Design Approach of Partitioning and Allocation in Parallel Data Warehouses. 99-110 - Goetz Graefe:
Fast Loads and Fast Queries. 111-124
Pattern Mining
- Christie I. Ezeife, Dan Zhang:
TidFP: Mining Frequent Patterns in Different Databases with Transaction ID. 125-137 - Marzena Kryszkiewicz:
Non-Derivable Item Set and Non-Derivable Literal Set Representations of Patterns Admitting Negation. 138-150 - Willie Ng, Manoranjan Dash:
Which Is Better for Frequent Pattern Mining: Approximate Counting or Sampling?. 151-162 - Minh Quoc Nguyen, Edward Omiecinski, Leo Mark:
A Fast Feature-Based Method to Detect Unusual Patterns in Multidimensional Datasets. 163-176
Data Cubes
- Kais Haddadin, Tobias Lauer:
Efficient Online Aggregates in Dense-Region-Based Data Cube Representations. 177-188 - Alfredo Ferro, Rosalba Giugno, Piera Laura Puglisi, Alfredo Pulvirenti:
BitCube: A Bottom-Up Cubing Engineering. 189-203 - Sébastien Nedjar:
Exact and Approximate Sizes of Convex Datacubes. 204-215
Data Mining Applications
- Isis Peña, Herna L. Viktor, Eric Paquet:
Finding Clothing That Fit through Cluster Analysis and Objective Interestingness Measures. 216-228 - Bing Quan Huang, M. Tahar Kechadi, Brian Buckley:
Customer Churn Prediction for Broadband Internet Services. 229-243 - Xuequn Shang, Qian Zhao, Zhanhuai Li:
Mining High-Correlation Association Rules for Inferring Gene Regulation Networks. 244-255
Analytics
- Qiming Chen, Meichun Hsu, Rui Liu:
Extend UDF Technology for Integrated Analytics. 256-270 - Todd Eavis, Ruhan Sayeed:
High Performance Analytics with the R3-Cache. 271-286 - Matteo Golfarelli:
Open Source BI Platforms: A Functional and Architectural Comparison. 287-297 - Matthias Kehlenbeck, Michael H. Breitner:
Ontology-Based Exchange and Immediate Application of Business Calculation Definitions for Online Analytical Processing. 298-311
Data Mining
- Jinhan Kim, Jongwuk Lee, Seung-won Hwang:
Skyline View: Efficient Distributed Subspace Skyline Computation. 312-324 - Srihari Padmanabhan, Sharma Chakravarthy:
HDB-Subdue: A Scalable Approach to Graph Mining. 325-338 - Mirjana Mazuran, Elisa Quintarelli, Rosalba Rossato, Letizia Tanca:
Mining Violations to Relax Relational Database Constraints. 339-353 - Maya Wardeh, Frans Coenen, Trevor J. M. Bench-Capon:
Arguing from Experience to Classifying Noisy Data. 354-365
Clustering
- Vadim V. Ayuyev, Joseph Jupin, Philip W. Harris, Zoran Obradovic:
Dynamic Clustering-Based Estimation of Missing Values in Mixed Type Data. 366-377 - M. Julia Flores, José A. Gámez, Jens Dalgaard Nielsen:
The PDG-Mixture Model for Clustering. 378-389 - Petr Chmelar, Ivana Rudolfova, Jaroslav Zendulka:
Clustering for Video Retrieval. 390-401
Spatio-Temporal Mining
- Wei Chen, Parvathi Chundi:
Trends Analysis of Topics Based on Temporal Segmentation. 402-414 - Jin Soung Yoo, Mark Bow:
Finding N-Most Prevalent Colocated Event Sets. 415-427
Rule Mining
- Gianni Costa, Massimo Guarascio, Giuseppe Manco, Riccardo Ortale, Ettore Ritacco:
Rule Learning with Probabilistic Smoothing. 428-440 - Leila Ben Othman, François Rioult, Sadok Ben Yahia, Bruno Crémilleux:
Missing Values: Proposition of a Typology and Characterization with an Association Rule-Based Model. 441-452
Olap Recommendation
- Arnaud Giacometti, Patrick Marcel, Elsa Negre:
Recommending Multidimensional Queries. 453-466 - Houssem Jerbi, Franck Ravat, Olivier Teste, Gilles Zurfluh:
Preference-Based Recommendations for OLAP Analysis. 467-478
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