| Data Warehousing and Knowledge Discovery have been widely accepted as key technologies for enterprises and organizations as a means of improving 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 processed is becoming more and more complex in both structure and semantics. Consequently, the process of retrieval and knowledge discovery from this huge amount of heterogeneous complex data constitutes the reality check for research in the area.
During the past few years, the International Conference on Data Warehousing and Knowledge Discovery (DaWaK) has become one of the most important international scientific events to bring together researchers, developers and practitioners. The DaWaK conferences serve as a prominent forum for discussing the latest research issues and experiences in developing and deploying data warehousing and knowledge discovery systems, applications, and solutions. This year’s conference, the 10th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2008), continued the tradition of facilitating the cross-disciplinary exchange of ideas, experience and potential research directions. DaWaK 2008 sought to disseminate innovative principles, methods, algorithms and solutions to challenging problems faced in the development of data warehousing, knowledge discovery and data mining applications.
The papers presented covered a number of broad research areas on both theoretical and practical aspects of data warehousing and knowledge discovery. In the areas of data warehousing, the topics covered included advanced techniques in OLAP and multidimensional designing and modeling, advanced OLAP and Cube Processing, innovation of ETL processes and data integration problems, materialized view optimization, distributed and parallel processing in data warehousing, data warehouses and data mining applications integration, multidimensional analysis of text documents, and data warehousing for real-world applications such as medical applications, spatial applications, and bioinformatics data warehouses. In the areas of data mining and knowledge discovery, the topics covered included text mining and taxonomy, web information discovery, stream data analysis and mining, ontology-based data and nonstandard data mining techniques, machine learning, constraint-based mining, and traditional data mining topics such as mining frequent item sets, clustering, association, classification, ranking, and applications of data mining technologies to real-world problems. It is especially notable to see that some papers covered emerging realworld applications such as bioinformatics, geophysics, and terrorist networks, as well as integration of multiple technologies such as conceptual modeling of knowledge discovery process and results, integration of semantic web into data warehousing and OLAP technologies, OLAP mining, and imprecise or fuzzy OLAP. All these papers show that data warehousing and knowledge discovery technologies are maturing and making an impact on real-world applications. |