19th International Conference on
Big Data Analytics and Knowledge Discovery - DaWaK 2017
August 28 - 31, 2017
The best papers from DAWAK '17 will be expanded and revised for possible inclusion in:
- Journal of Concurrency and Computation: Practice and Experience, Wiley
- Distributed and Parallel Databases, Springer
- Transactions on Large-scale Data- and Knowledge Centered Systems - TLDKS
Submissions presenting current research work on both theoretical and practical aspects of Big Data, Knowledge Discovery are encouraged. Submissions on industrial applications and experiences are also highly encouraged.
Track 1 - Research:
- Big Data Query Languages and Optimization
- Big Data Analytics and User Interfaces
- Big Data Indexing and Storing
- Big Data Analytics: algorithms, techniques, and systems
- Big Data Quality and Provenance Control
- Social Networking Data Graph Management
- Graph mining, analysis, and querying.
- Visualization Analytics for Big Data
- Big Data Searching and Discovery
- Big Data Management for Mobile Applications
- Scalability and Parallelization using Mapreduce and beyond
- Analytics for the Cloud Infrastructure
- Analytics for Unstructured, Semi-structured, and Structured Data
- Semantic for Big Data Intelligence
- Analytics for Temporal, Spatial, Spatio-temporal, and Mobile Data
- Analytics for Data Streams and Sensor Data
- Analytics for Big 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
- Big Data Application Design and Deployment
- 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
- Cost Models for advanced applications and programming paradigms
- Reproduction of Big Data Experiments
- Big Data Data Mining Trends, Opportunities and Risks
- Big Data Mining from Low-quality Information Sources
Track 2 - Industry and Application:
- Big Data Analytics and Knowledge Discovery Tools
- Big Data Deployment Industrial Experiences
- Big Data Applications in Scientific, Government, Healthcare, Bioinformatics, Smart City, etc.
- Big Data Analytics Applications in E-commerce and Web Technology for Finance, Healthcare, Marketing, Telecommunications, etc.
- Big Data for Intrusion/Fraud Detection
- Big Data and Business Process Intelligence (BPI)
- Big Data in Enterprise Management Models and Practices
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 2017 Submission site starting in January 2017.
Submissions must conform to Springer's LNCS format and should not exceed 14 pages. All accepted papers will be published in LNCS by Springer-Verlag.
Authors of selected best papers from DaWaK 2017 will be invited to submit the extended paper for special issues of the following journals: Distributed and Parallel Databases, LNCS Transactions on Large-Scale Data and Knowledge-Centered Systems and Journal of Concurrency and Computation: Practice and Experience, Wiley.
For further inquiries, contact the DaWaK 2017 PC chairs
- Submission of abstracts: March 13, 2017
- Submission of full papers: March 20, 2017
- Notification of acceptance: May 15, 2017
- Camera-ready copies due: June 05, 2017
- Ladjel Bellatreche, LIAS/ISAE-ENSMA, Poitiers, France <Bellatreche@ensma.fr>
- Chakravarthy, Sharma, The University of Texas at Arlington, United States < email@example.com>