Data Warehousing and Knowledge Discovery [electronic resource] : 12th International Conference, DaWaK 2010, Bilbao, Spain, August 30 - September 2, 2010, Proceedings /
DaWaK 2010Corporate Author
DaWaK 2010(2010 :Bilbao, Spain)
DaWaK 2010
Mohania, Mukesh K.editor.
Tjoa, A Min.editor.
Data warehousing and knowledge discovery has been widely accepted as a key te- nology for enterprises and organizations to improve their abilities in data analysis, decision support, and the automatic extraction of knowledge from data. With the exponentially growing amount of information to be included in the decision-making process, the data to be considered become more and more complex in both structure and semantics. New developments such as cloud computing add to the challenges with massive scaling, a new computing infrastructure, and new types of data. Consequently, the process of retrieval and knowledge discovery from this huge amount of heterogeneous complex data forms the litmus test for research in the area. In the last decade, the International Conference on Data Warehousing and Kno- edge Discovery (DaWaK) has become one of the most important international sci- tific events bringing together researchers, developers, and practitioners to discuss the latest research issues and experiences in developing and deploying data warehousing and knowledge discovery systems, applications, and solutions. th This year's conference, the 12 International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2010), continued the tradition by discussing and disseminating innovative principles, methods, algorithms, and solutions to challe- ing problems faced in the development of data warehousing, knowledge discovery, the emerging area of "cloud intelligence," and applications within these areas. In order to better reflect novel trends and the diversity of topics, the conference was organized in four tracks: Cloud Intelligence, Data Warehousing, Knowledge Discovery, and Industry and Applications.
Bibliographic Level Mode of Issuance: Monograph
Includes bibliographical references and index.
Data Warehouse Modeling and Spatial Data Warehouses -- Logic Programming for Data Warehouse Conceptual Schema Validation -- A Model-Driven Heuristic Approach for Detecting Multidimensional Facts in Relational Data Sources -- Physical Design and Implementation of Spatial Data Warehouses Supporting Continuous Fields -- Benchmarking Spatial Data Warehouses -- Mining Social Networks and Graphs -- Discovering Community-Oriented Roles of Nodes in a Social Network -- A Graph-Based Clustering Scheme for Identifying Related Tags in Folksonomies -- Frequent Sub-graph Mining on Edge Weighted Graphs -- Physical Data Warehouse Design -- & : A Methodology for Effectively and Efficiently Designing Parallel Relational Data Warehouses on Heterogenous Database Clusters -- Yet Another Algorithms for Selecting Bitmap Join Indexes -- Speeding Up Queries in Column Stores -- Dependency Mining -- Mining Non-redundant Information-Theoretic Dependencies between Itemsets -- Discovery and Application of Functional Dependencies in Conjunctive Query Mining -- Using Transitivity to Increase the Accuracy of Sample-Based Pearson Correlation Coefficients -- Business Intelligence and Analytics -- The NOX Framework: Native Language Queries for Business Intelligence Applications -- Experience in Extending Query Engine for Continuous Analytics -- Development of a Business Intelligence Environment for e-Gov Using Open Source Technologies -- Outlier and Image Mining -- A Fast Randomized Method for Local Density-Based Outlier Detection in High Dimensional Data -- Specialty Mining -- Region of Interest Based Image Categorization -- Pattern Mining -- Meta-learning for Post-processing of Association Rules -- A Relational Approach for Discovering Frequent Patterns with Disjunctions -- An Occurrence Based Approach to Mine Emerging Sequences -- Mining Closed Itemsets in Data Stream Using Formal Concept Analysis -- Data Cleaning and Variable Selection -- XML Data Fusion -- An Efficient Duplicate Record Detection Using q-Grams Array Inverted Index -- Modelling Complex Data by Learning Which Variable to Construct.
Data warehousing and knowledge discovery has been widely accepted as a key te- nology for enterprises and organizations to improve their abilities in data analysis, decision support, and the automatic extraction of knowledge from data. With the exponentially growing amount of information to be included in the decision-making process, the data to be considered become more and more complex in both structure and semantics. New developments such as cloud computing add to the challenges with massive scaling, a new computing infrastructure, and new types of data. Consequently, the process of retrieval and knowledge discovery from this huge amount of heterogeneous complex data forms the litmus test for research in the area. In the last decade, the International Conference on Data Warehousing and Kno- edge Discovery (DaWaK) has become one of the most important international sci- tific events bringing together researchers, developers, and practitioners to discuss the latest research issues and experiences in developing and deploying data warehousing and knowledge discovery systems, applications, and solutions. th This year's conference, the 12 International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2010), continued the tradition by discussing and disseminating innovative principles, methods, algorithms, and solutions to challe- ing problems faced in the development of data warehousing, knowledge discovery, the emerging area of "cloud intelligence," and applications within these areas. In order to better reflect novel trends and the diversity of topics, the conference was organized in four tracks: Cloud Intelligence, Data Warehousing, Knowledge Discovery, and Industry and Applications.
Database management.
Computer communication systems.
Computer programming.
Data mining.
Application software.
Data structures (Computer science).
Database Management.
Computer Communication Networks.
Programming Techniques.
Data Mining and Knowledge Discovery.
Information Systems Applications (incl. Internet).
Data Structures.

Lecture Notes in Computer Science