Big Data Mining (KDD BigMine-16)
5th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (BigMine-16) - a KDD2016 Workshop
KDD2016 Conference Dates: August 13-17, 2016
Workshop Date: Aug 14, 2016
San Francisco, California
Papers due: May 27th 23:59PM Pacific Standard Time
Acceptance notification: June 13, 2016
Workshop Final Paper Due: July 1, 2016
Proceedings will be published as a dedicated volume of the JMLR: Workshop and Conference Proceedings.
This year we are accepting papers in two formats. Either 16 pages (i.e., as standard KDD papers) for regular papers, or 4-6 pages for short papers. See the submission site for full details regarding paper preparation and submission guidelines.
The goal of the workshop is to provide a forum to discuss important research questions and practical challenges in big data mining and related areas. Novel ideas, controversial issues, open problems and comparisons of competing approaches are strongly encouraged. Representation of alternative viewpoints and discussions are also particularly encouraged.
We invite submission of papers describing innovative research on all aspects of big data mining. Work-in-progress papers, demos, and visionary papers are also welcome.
Examples of topics of interest include
- Scalable, Distributed and Parallel Algorithms
- New Programming Model for Large Data beyond Hadoop/MapReduce, STORM, streaming languages
- Mining Algorithms of Data in non-traditional formats (unstructured, semi-structured)
- Applications: social media, Internet of Things, Smart Grid, Smart Transportation Systems
- Streaming Data Processing
- Heterogeneous Sources and Format Mining
- Systems Issues related to large datasets: clouds, streaming system, architecture, and issues beyond cloud and streams.
- Interfaces to database systems and analytics.
- Evaluation Technologies
- Visualization for Big Data
- Applications: Large scale recommendation systems, social media systems, social network systems, scientific data mining, environmental, urban and other large data mining applications.
Papers emphasizing theoretical foundations, algorithms, systems, applications, language issues, data storage and access, architecture are particularly encouraged.
We welcome submissions by authors who are new to the data mining research community.
Submitted papers will be assessed based on their novelty, technical quality, potential impact, and clarity of writing. For papers that rely heavily on empirical evaluations, the experimental methods and results should be clear, well executed, and repeatable. Authors are strongly encouraged to make data and code publicly available whenever possible.