11. DaWaK 2009: Linz, Austria

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

• C. 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, B. 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 D. 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