posted by user: tartar || 3522 views || tracked by 5 users: [display]

SABID 2016 : Workshop on Solar & Stellar Astronomy BIg Data


When Dec 5, 2016 - Dec 8, 2016
Where Washington, DC, USA
Submission Deadline Sep 30, 2016
Notification Due Oct 14, 2016
Final Version Due Nov 10, 2016
Categories    data mining   big data   astronomy   solar

Call For Papers

Organizers of the 3rd SABID 2016 workshop solicit high-quality original research papers related to solar & stellar astronomy big data mining, which through innovative data mining techniques contribute to the open stellar astronomy and solar physics research questions. The SABID workshop provides exposure to the current interdisciplinary research of computer scientists, electrical and computer engineers, statisticians, astronomers and solar physicists conducted on astronomy data. It is intended to engage astronomers, solar physicists, big data researchers and data miners in new collaborations by presenting massive solar & stellar datasets, and current challenges with data-driven knowledge discovery from solar & stellar astronomy big data. The workshop will take place in conjunction with the IEEE International Conference on Big Data 2016.

Organizers of SABID 2016 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 the Flood of Solar & Stellar Astronomy Big Data
1. New Computational Models for Storage, Distribution, Processing and Mining of Astronomy Data
2. Evaluation of Information Quality for Astronomy Data from Telescopes, as well as Derived Data Products (Meta-Data)
3. New Scientific Standards for Information Processing and Mining, and their Quality Evaluation
4. System Architectures, Design and Deployment of Solar and Stellar Astronomy Data Archives, Portals and Analytical Services
5. Data Management and Stream Mining for Astronomy Data in Cloud and Distributed Environments
6. Integration of Heterogeneous Solar Information from Multiple Data Repositories for the purpose of Knowledge Discovery from these Databases

* Solar & Stellar Astroinformatics and Astrostatistics
1. New Computational Models for Search, Retrieval, and Mining of Astronomy Data
2. Scalable Algorithms and Systems for Solar Activity Recognition (e.g. Computer Vision) from Solar Data Repositories
3. Efficient Data Selection, Machine-Learning and Triage Techniques
4. Solar & Stellar Astronomy Data Search Architectures, their Scalability, Efficiency, and Real-life Usefulness
5. Visualization and Interaction Tools for Large Astronomy Data Bases
6. Computational Astrostatistics (e.g. irregularly sampled data, multivariate and survival analysis, nonlinear regression, etc.)
7. Hyperspectral Imaging: Technologies and Techniques
8. Image Processing for Unbiased Image, Spatial and Time Series Analysis
9. Cloud-, Distributed-, and Stream-Data Mining for High Velocity Astronomy Data
10. Semantic-based Data Mining from Heterogeneous Solar & Stellar Data Repositories
11. Multimedia, Multi-structured, and Spatiotemporal Astronomy Data Mining
12. Novel Solar & Stellar Data Mining Models, including new algorithms available through Hadoop, MapReduce, No-SQL and similar technologies

* Computer Applications related to Solar Astronomy Big Data Mining
1. Complex Solar Weather Applications in Science, Engineering, Education, Navigation, Power Grids, and Telecommunication for Government, Public and Private Industry Sectors
2. New Real-life Case Studies of Big Solar Data Mining (e.g. Space Weather)
3. Experiences with Big Data Mining Project Deployments in Solar Physics
4. Solar Astronomy Data and Knowledge Distribution in the Social Web

* Seeing the Sun as a Star Using Astronomical Big Data
1. Surveys of Millions of Suns – Is the Sun a Typical Sun-like Star?
2. Helioseismology versus Asteroseismology
3. Flares, Planets and Supernovae – Identifying Transient/Periodic Events in Time Series Data


Please submit a full-length paper (up to 10 page IEEE 2-column format) through our online submission system (for more info, please visit SABID 2016 website).

Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines.
Accepted papers (after being carefully reviewed by at least 3 PC members and accepted for our workshop) will be included in the 2016 IEEE International Conference on Big Data Workshop Proceedings.
Selected papers (after their expansion and second round of independent review) will be invited into a special journal issue. We contacted the editorial board of Elsevier’s journal on “Astronomy and Computing” and received encouraging response about the possibility of offering a special issue of this journal based on the works presented during the SABID 2016 workshop.


The full call-for-papers, and more details on the SABID-2016 workshop, visit our web site:

September 30, 2016: Due date for workshop papers submission
October 14, 2016: Notification of paper acceptance to authors
November 10, 2016: Camera-ready of accepted papers
December 5 (or 8), 2016: Workshop during IEEE International Conference on Big Data 2016

Related Resources

DSAA 2017   The 4th IEEE International Conference on Data Science and Advanced Analytics 2017
ECML-PKDD 2017   European Conference on Machine Learning and Principles and Practice of Knowledge Discovery
ICDM 2017   IEEE International Conference on Data Mining 2017
CEWIT 2017   13th International Conference & Expo on Emerging Technologies for a Smarter World
C3S2E 2017   C3S2E 2017 Tenth International C* Conference on Computer Science & Software Engineering
INIT/AERFAISummerSchoolML 2017   INIT/AERFAI Summer School on Machine Learning
Big Data- ADDS 2017   Special Issue on Big Data Analytics & Data-Driven Science
KDD 2017   Call for Research Papers
ISMIS 2017   23rd International Symposium on Methodologies for Intelligent Systems
SI - IoT and Big Data 2017   IoT and Big Data: An Extraordinary Synergy