Big Data Mining
4th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (BigMine-15)
Conference Dates: August 10-13, 2015
Workshop Date: Aug 10, 2015
Sidney, Australia
Key dates:
Papers due: June 12th 23:59PM (Extended)
Acceptance notification: July 2, 2015
Workshop Final Paper Due: July 15, 2015
Proceedings will be published as a dedicated volume of the JMLR: Workshop and Conference Proceedings. Authors should consult the submission site (https://bigmine.github.io/bigmine15/submission/) 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.
Following KDD main conference tradition, reviews are not double-blind, and author names and affiliations should be listed.
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 topic 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.