Data Warehousing and Knowledge Discovery [electronic resource] :11th International Conference, DaWaK 2009 Linz, Austria, August 31–September 2, 2009 Proceedings /
Contributor(s): Pedersen, Torben Bach [editor.] | Mohania, Mukesh K [editor.] | Tjoa, A Min [editor.] | SpringerLink (Online service).Material type: BookSeries: Lecture Notes in Computer Science: 5691Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2009.Description: XIV, 480 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642037306.Subject(s): Computer science | Computers | Database management | Data mining | Information storage and retrieval | Pattern recognition | Computer Science | Database Management | Data Mining and Knowledge Discovery | Information Storage and Retrieval | Information Systems Applications (incl. Internet) | Information Systems and Communication Service | Pattern RecognitionOnline resources: Click here to access online
Invited Talk -- New Challenges in Information Integration -- Data Warehouse Modeling -- What Is Spatio-Temporal Data Warehousing? -- Towards a Modernization Process for Secure Data Warehouses -- Visual Modelling of Data Warehousing Flows with UML Profiles -- Data Streams -- CAMS: OLAPing Multidimensional Data Streams Efficiently -- Data Stream Prediction Using Incremental Hidden Markov Models -- History Guided Low-Cost Change Detection in Streams -- Physical Design -- HOBI: Hierarchically Organized Bitmap Index for Indexing Dimensional Data -- A Joint Design Approach of Partitioning and Allocation in Parallel Data Warehouses -- Fast Loads and Fast Queries -- Pattern Mining -- TidFP: Mining Frequent Patterns in Different Databases with Transaction ID -- Non-Derivable Item Set and Non-Derivable Literal Set Representations of Patterns Admitting Negation -- Which Is Better for Frequent Pattern Mining: Approximate Counting or Sampling? -- A Fast Feature-Based Method to Detect Unusual Patterns in Multidimensional Datasets -- Data Cubes -- Efficient Online Aggregates in Dense-Region-Based Data Cube Representations -- BitCube: A Bottom-Up Cubing Engineering -- Exact and Approximate Sizes of Convex Datacubes -- Data Mining Applications -- Finding Clothing That Fit through Cluster Analysis and Objective Interestingness Measures -- Customer Churn Prediction for Broadband Internet Services -- Mining High-Correlation Association Rules for Inferring Gene Regulation Networks -- Analytics -- Extend UDF Technology for Integrated Analytics -- High Performance Analytics with the R3-Cache -- Open Source BI Platforms: A Functional and Architectural Comparison -- Ontology-Based Exchange and Immediate Application of Business Calculation Definitions for Online Analytical Processing -- Data Mining -- Skyline View: Efficient Distributed Subspace Skyline Computation -- HDB-Subdue: A Scalable Approach to Graph Mining -- Mining Violations to Relax Relational Database Constraints -- Arguing from Experience to Classifying Noisy Data -- Clustering -- Dynamic Clustering-Based Estimation of Missing Values in Mixed Type Data -- The PDG-Mixture Model for Clustering -- Clustering for Video Retrieval -- Spatio-Temporal Mining -- Trends Analysis of Topics Based on Temporal Segmentation -- Finding N-Most Prevalent Colocated Event Sets -- Rule Mining -- Rule Learning with Probabilistic Smoothing -- Missing Values: Proposition of a Typology and Characterization with an Association Rule-Based Model -- Olap Recommendation -- Recommending Multidimensional Queries -- Preference-Based Recommendations for OLAP Analysis.
This book constitutes the refereed proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery, DaWak 2009 held in Linz, Austria in August/September 2009. The 36 revised full papers presented were carefully reviewed and selected from 124 submissions. The papers are organized in topical sections on data warehouse modeling, data streams, physical design, pattern mining, data cubes, data mining applications, analytics, data mining, clustering, spatio-temporal mining, rule mining, and OLAP recommendation.