Edit Event: DaWaK 2009

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<pre> 11th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2009), Linz, Austria. Aug. 31 to Sept. 4, 2009 http://www.dexa.org/dawak Data Warehousing and Knowledge Discovery has been widely accepted as a key technology for enterprises and organisations 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 becomes 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 builds the litmus-test for the research in the area. During the past 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 to discuss latest research issues and experiences in developing and deploying data warehousing and knowledge discovery systems, applications, and solutions. This year?s conference (DaWaK 2009), builds on this tradition of facilitating the cross-disciplinary exchange of ideas, experience and potential research directions. DaWaK 2009 seeks to introduce innovative principles, methods, algorithms and solutions to challenging problems faced in the development of data warehousing, knowledge discovery and data mining applications. Submissions presenting current research work on both theoretical and practical aspects of data warehousing and knowledge discovery are encouraged. Particularly, we strongly welcome submissions dealing with emerging real world applications such as real-time data warehousing, analysis of spatial and spatiotemporal data, OLAP mining, mobile OLAP, and mining science data (e.g. bioinformatics, geophysics) Major Tracks Topics of interest include in these tracks but are not limited to: Data Warehousing - Analytical front-end tools for DW and OLAP - Data warehouse architecture - Data extraction, cleansing, transforming and loading - Data warehouse design (conceptual, logical and physical) - Multidimensional modelling and queries - Data warehousing consistency and quality - Data warehouse maintenance and evolution - View maintenance/adaptation - Data warehouse schema evolution - Grid/Parallel/distributed data warehousing - Performance optimization and tuning - Implementation/compression techniques - Data warehouse metadata - Active and event-based data warehousing - Real-time/right-time data warehousing - DW and OLAP for stream and sensor data - Spatial and spatio-temporal data warehouses - Data warehousing and OLAP in mobile/wireless environments - Web/multimedia data warehouses - Data warehousing with unstructured data (e.g., text) and semi-structured data (e.g., XML) - Combined analysis (OLAP,etc) of structured, semi-structured, and unstructured data - Data warehousing and the semantic web - Data warehouse privacy, security, and reliability - Data warehousing applications: corporate, scientific, government, healthcare, bioinformatics, etc. - Business Process Intelligence (BPI) Knowledge Discovery - Data mining techniques: clustering, clas-sification, association rules, decision trees, etc. - Data and knowledge representation - Knowledge discovery framework and process - Integrating constraints and knowledge in the KDD process - Exploring data analysis, inference of causes, prediction - Evaluating, consolidating, and explaining discovered knowledge - Statistical techniques for generation a robust, consistent data model - Interactive data exploration/visualization and discovery - Languages and interfaces for data mining - Distributed and parallel data mining and knowledge discovery - Complexity and scalability in data mining - Pre-processing and post-processing in data mining - Security, privacy and social impact of data mining - Mining unstructured, semi-structured, and structured data - Mining temporal, spatial, spatio-temporal data - Mining data streams and sensor data - Mining multimedia data - Mining social network data - Data mining support for designing information systems - Integration of data warehousing, OLAP and data mining - Mining Trends, Opportunities and Risks - Mining from low-quality information sources - Data mining applications: bioinformatics, E-commerce, Web, intrusion/fraud detection, finance, healthcare, marketing, telecommunications, etc Paper Submission Details Authors are invited to submit research and application papers representing original, previously unpublished work. Papers should be submitted in PDF or Word format. Submissions must conform to Springer's LNCS format and should not exceed 12 pages (including all text, figures, references and appendices). Authors who want to buy extra pages may submit a paper up to 15 pages with the indication that the authors will purchase extra pages if the paper is accepted. Submissions which do not conform to the LNCS format and/or which do exceed 12 pages (or up to 15 pages with the extra page purchase commitment) will be rejected without reviews. Submitted papers will be carefully evaluated based on originality, significance, technical soundness, and clarity of exposition. All accepted papers will be published in Lecture Notes in Computer Science (LNCS) by Springer-Verlag. Duplicate submissions are not allowed. A submission is considered to be a duplicate submission if it is submitted to other conferences/workshops/journals or it has been already accepted to be published in other conferences/workshops/journals. Duplicate submissions thus will be automatically rejected without reviews. As the last year, authors of best papers selected from DaWaK2009 conference will be invited to submit an extension for a special issue of a journal. Authors are requested to send the abstract of their paper to be received by March 20 2009, due date of the full paper electronic submission is March 27, 2009. For further inquiries, contact the DaWaK 2009 PC Co-Chairpersons: Dr. Mukesh Mohania (mkmukesh@in.ibm.com), Prof. Torben Bach Pedersen (tbp@cs.aau.dk), or Prof. A Min Tjoa (amin.tjoa@ifs.tuwien.ac.at) IMPORTANT DATES - Submission of abstracts: March 20, 2009 - Submission of full papers: March 27, 2009 - Notification of acceptance: May 15, 2009 - Camera-ready copies due: June 10, 2009 Program Chairs - Torben Bach Pedersen, Aalborg University, Denmark - Mukesh Mohania, IBM India Research Lab, India - A Min Tjoa, Vienna University of Technology, Austria </pre>This CfP was obtained from [http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=4421&amp;copyownerid=2 WikiCFP][[Category:Knowledge discovery]] [[Category:Data mining]]

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