Data Warehousing and Knowledge Discovery

1. Pattern Mining

   1. Meta-learning for Post-processing of Association Rules

      • Petr Berka, Jan Rauch

      Pages 251-262

   2. A Relational Approach for Discovering Frequent Patterns with Disjunctions

      • Corrado Loglisci, Michelangelo Ceci, Donato Malerba

      Pages 263-274

   3. An Occurrence Based Approach to Mine Emerging Sequences

      • Kang Deng, Osmar R. Zaïane

      Pages 275-284

   4. Mining Closed Itemsets in Data Stream Using Formal Concept Analysis

      • Anamika Gupta, Vasudha Bhatnagar, Naveen Kumar

      Pages 285-296

2. Data Cleaning and Variable Selection

   1. XML Data Fusion

      • Frantchesco Cecchin, Cristina Dutra de Aguiar Ciferri, Carmem Satie Hara

      Pages 297-308

   2. An Efficient Duplicate Record Detection Using q-Grams Array Inverted Index

      • Alfredo Ferro, Rosalba Giugno, Piera Laura Puglisi, Alfredo Pulvirenti

      Pages 309-323

   3. Modelling Complex Data by Learning Which Variable to Construct

      • Françoise Fessant, Aurélie Le Cam, Marc Boullé, Raphaël Féraud

      Pages 324-335

3. Back Matter

   PDF

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.

• Business Intelligence
• algorithms
• business process intelligence
• clustering
• data cleansing
• data mining
• data warehouse
• database management
• knowledge discovery
• data structures

• Department of Computer Science, Aalborg University, Aalborg, Denmark

  Torben Bach Pedersen

• IBM India Research Lab, 4, Block C, Institutional Area, New Delhi, India

  Mukesh K. Mohania

• Institute of Software Technology, Vienna University of Technology, Vienna, Austria

  A Min Tjoa

Policies and ethics