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DaWaK 2014 : 16th International Conference on Data Warehousing and Knowledge Discovery


Conference Series : Data Warehousing and Knowledge Discovery
When Sep 1, 2014 - Sep 5, 2014
Where Munich, Germany
Submission Deadline Mar 17, 2014
Categories    databases   big data   knowledge discovery   data warehousing

Call For Papers

Three Special Issues on the best papers from DAWAK '14 will be expanded and revised for possible inclusion in:

Knowledge and Information Systems: An International Journal, Springer. Impact Factor=2.225
Journal of Concurrency and Computation: Practice and Experience, Wiley. Impact Factor: 0.845
Transactions on Large-scale Data- and Knowledge Centered Systems - TLDKS, Springer

Keynote Speaker:
Professor Sanjay Madria
Director of the Web and Wireless Computing Lab., Missouri University of Science and Technology, USA

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 2014 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
Energy based design and deployment

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
Data Warehousing for real time queries
Integration of data warehousing and machine learning
Semantic Data warehouses

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:

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 at: DaWaK 2014 Submission site 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 2014 will be invited to submit the extended paper for a special issue of LNCS Transactions on Large-Scale Data and Knowledge-Centered Systems.

For further inquiries, contact the DaWaK 2014 PC chairs


Submission of abstracts: March 17, 2014
Submission of full papers: March 31, 2014
Notification of acceptance: May 19, 2014
Camera-ready copies due: June 09, 2014

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