DaWaK 2012 - 14th International Conference on Data Warehousing and Knowledge Discovery

Data Warehousing and Knowledge Discovery has been widely accepted as a key technology 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 becomes 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 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 2012), builds on this tradition of facilitating the cross-disciplinary exchange of ideas, experience and potential research directions. DaWaK 2012 seeks to introduce innovative principles, methods, algorithms and solutions to challenging problems faced in the development of data warehousing, knowledge discovery, data mining applications, and the emerging area of "cloud intelligence".

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
DaWaK 2012 is again organized into 4 tracks, each with a distinct focus. The four tracks, and their main topics are as follows:

Cloud Intelligence Track:
Massive data analytics: algorithms, techniques, and systems
Scalability and parallelization for cloud intelligence: map-reduce and beyond
Analytics for the cloud infrastructure
Analytics for unstructured, semi-structured, and structured data
Semantic web intelligence
Analytics for temporal, spatial, spatio-temporal, and mobile data
Analytics for data streams and sensor data
Analytics for multimedia data
Analytics for social networks
Real-time/right-time and event-based analytics
Privacy and security in cloud intelligence
Reliability and fault tolerance in cloud intelligence
Data Warehousing Track:
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
Performance optimization and tuning
Implementation/compression techniques
Data warehouse metadata
Knowledge Discovery:
Data mining techniques: clustering, classification, association rules, decision trees, etc.
Data and knowledge representation
Knowledge discovery framework and process, including pre- and post-processing
Integration of data warehousing, OLAP and data mining
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
Mining Trends, Opportunities and Risks
Mining from low-quality information sources
Industry and Applications Track:
Data warehousing tools
OLAP and analytics tools
Data mining tools
Industry experiences
Data warehousing applications: corporate, scientific, government, healthcare, bioinformatics, etc.
Data mining applications: bioinformatics, E-commerce, Web, intrusion/fraud detection, finance, healthcare, marketing, telecommunications, etc
Data mining support for designing information systems
Business Process Intelligence (BPI)