DaWaK 2013 - 15th International Conference on Data Warehousing and Knowledge Discovery

15th International Conference on Data Warehousing and Knowledge Discovery - DaWaK 2013
Prague, Czech Republic
August 26 - 29, 2013
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 and Big Data 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.
Submissions presenting current research work on both theoretical and practical aspects of Big Data, Data Warehousing and Knowledge Discovery are encouraged. DaWaK 2013 is organized into 4 tracks as follows:
Big Data and Cloud Intelligence Track:
Big Data Storage
Big Data Query Languages and Optimization
Big Data Analytics and User Interfaces
Big Indexes
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 Track:
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:
Big Data Analytics Applications
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)
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. Submission Online through ConfDriver via www.dexa.org
Submissions must conform to Springer's LNCS format and should not exceed 12 pages. All accepted papers will be published in LNCS by Springer-Verlag. Authors of selected best papers from DaWaK 2013 will be invited to submit the extended paper for a special issue of LNCS Transactions on Large-Scale Data and Knowledge-Centered Systems.