Special Session on Big Data Analytics and Stream Data Mining
When : 2017-06-26
Where : Warsaw, Poland
Submission Deadline : 2017-01-22
Categories : Data Mining , Machine Learning
Special Session on Big Data Analytics and Stream Data Mining(BDASD 2017)
June 26-29, 2017, Warsaw, Poland
Call for Papers :
In the world of today, modern information systems are able to collect very large data with inherent and increasing complex structure and dimensionality. Furthermore, new data sources often provide various heterogeneous representations and also time changing characteristics with respect to the data. This is particular visible in the rapidly developing field of Big Data Analytics. Although machine learning and data mining researchers had already studied mining massive and complex data, there are significant differences between earlier efforts and the current trends opening up new problems and challenges. Indeed, Big Data Analytics opens up new research problems which were only considered within a limited range. Applications of Big Data Analytics may also influence human behavior and society in a significantly higher degree than before – which also requires new types of research. Furthermore, new Big Data challenges are particularly relevant in emerging applications where data are continuously generated at a high rate in the form of data streams, whose characteristics may also change with time (concept drifting data). Compared to static, standard environments, processing data streams implies new computational challenges and requirements for algorithms and their ability to adapt to such dynamic and complex contexts.
Topics of Interest
- Learning from high-dimensional datasets
- Mining non-standard data representations
- Large-scale link and graph mining
- Scaling up learning algorithms
- Distributed data mining approaches
- Knowledge discovery from ubiquitous environments
- Analysis of data from sensors and social media
- Online learning algorithms.
- Detection and adaptation to concept drift
- Evaluation issues of models learned from evolving data streams
- Classification and clustering in data streams
- Privacy in big and stream data analytics
- Societal aspects of applying Big Data
- Applications, especially in scientific data analysis, computational social science, medicine, text processing, web mining, image or multimedia analysis, sensor networks, industrial contexts, bio-informatics, energy management, and related domains.
Important Dates
| Paper submission due: | January 22, 2017 |
| Notification of review results: | March 14, 2017 |
| Camera ready papers due: | April 3, 2017 |
User Name : jerish
Posted 30-12-2016 on 09:50:48 AEDT