Organizers of the 4th SABID workshop solicit high-quality original research papers in the closely related areas of solar and stellar astronomy big data. Innovative data mining techniques in these fields are poised to address open research questions ranging from solar weather predictability to our place in the Universe. The topics include but are not limited to the following:

* Managing Solar & Stellar Astronomy Big Data
1. New Models and Scientific Standards for Storage, Distribution, Processing and Retrieval of Astronomy Data
2. Evaluation of Information Quality of Data coming from Telescopes and Derived Data Products (Meta-Data)
3. System Architectures, Design and Deployment of Solar and Stellar Astronomy Data Archives and Portals, as well as Retrieval, Visualization and Online Analytical Services
4. Big Data Management and Stream Data Processing in Cloud and Distributed Environments
5. Integration of Heterogeneous Information from Multiple Data Repositories
6. Information Retrieval Architectures, their Scalability, Real-life Usefulness and Benefits to Science
* Solar & Stellar Data Science, Mining and Astrostatistics
1. New Computational Models for Search, Retrieval, and Analysis of Solar & Stellar Astronomy Data
2. Scalable Algorithms and Systems for Pattern Recognition (e.g., Computer Vision) and Machine Learning
3. Novel Visualization and Interaction Tools for Big Data Science (e.g., for Hyperspectral Image, Spatial and Time Series Data Analyses)
4. Computational Astrostatistics (e.g., irregularly sampled data, survival analysis, nonlinear regression)
5. Cloud-, Distributed-, and Stream-Data Mining for High Velocity Astronomy Data
6. Novel Data Mining Models, including new algorithms available through Hadoop, MapReduce, No-SQL, etc.
* Applications Related to Space Weather Predictions and Impacts
1. Complex Space Weather Applications in Science, Engineering, Education, Navigation, Telecommunication and Power Grids for Government, Public and Private Sectors
2. New Real-life Big Data-Driven Case Studies and Their Implications for Decision Making
3. Solar and Stellar Astronomy Data and Knowledge Distribution in the Social Networks
* Enhancing the Solar-Stellar Connection with Big Data
1. Surveys of Millions of Suns – Is the Sun a Typical Sun-like Star?
2. Helioseismology and Asteroseismology
3. Identifying Transient or Periodic Events from Data (e.g. Flares, Exoplanets and Supernovae)
4. Solar and Stellar Dynamo Modeling

PAPER SUBMISSION
Please submit a full-length paper (up to 10 page IEEE 2-column format) through the online submission system (for more info see SABID 2017 website). After being carefully reviewed by at least 3 PC members, all papers accepted for our workshop will be included in the IEEE Int. Conf. Big Data Workshops 2017 Proceedings published by the IEEE Computer Society Press (also included in the IEEE Digital Library), and made available at the conference. Selected papers (after their significant expansion and another round of independent reviews) will be invited to a special journal issue. Best papers from SABID‘14 were published in “Astronomy and Computing” and best works from SABID’16 are currently in press of “Astrophysical Journal Supplement”.

FURTHER INFORMATION
The full call-for-papers, and more details on the SABID-2016 workshop, visit our web site: http://www.cs.gsu.edu/rangryk/workshops/SABID17/. This workshop is organized in conjunction with the IEEE International Conference on Big Data 2017 (http://cci.drexel.edu/bigdata/bigdata2017/).

The SABID Workshop Organizers:
* Rafal A. Angryk
angryk@cs.gsu.edu
Computer Scientist, Georgia State University
* Piet C. Martens
martens@astro.gsu.edu
Solar Physicist, Georgia State University
* Russel J. White
white@astro.gsu.edu
Astronomer, Georgia State University

IMPORTANT DATES
October 10, 2017: Due date for workshop papers submission
November 1, 2017: Notification of paper acceptance to authors
November 15, 2017: Camera-ready of accepted papers
December 11 (or 14), 2017: Workshop during IEEE International Conference on Big Data 2017 (http://cci.drexel.edu/bigdata/bigdata2017/)