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SABID 2020 : SABID 2020: WORKSHOP ON SOLAR & STELLAR ASTRONOMY BIG DATA (online) | |||||||||||||||
Link: https://grid.cs.gsu.edu/rangryk/workshops/SABID20/default.html | |||||||||||||||
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Call For Papers | |||||||||||||||
Organizers of SABID 2020 workshop solicit high-quality original research papers in the 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::
A. 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 Information from Multiple Data Repositories for the purpose of Knowledge Discovery from these Databases B. Solar & Stellar Data Science, Informatics and Statistics 1. New Computational Models for Search, Retrieval, and Mining of Astronomy Data 2. Scalable Algorithms and Systems for Solar & Stellar Activity Recognition 3. Solar & Stellar Astronomy Data Search Architectures, their Scalability, Efficiency, and Real-life Usefulness 4. Visualization and Interaction Tools for Large Astronomy Data Bases 5. Computational Astrostatistics (e.g., irregularly sampled data, multivariate and survival analysis, nonlinear regression) 6. Hyperspectral Imaging: Technologies and Techniques 7. Cloud-, Distributed-, and Stream-Data Mining for High Velocity Astronomy Data 8. Image Processing for Unbiased Image, Spatial and Time Series Analysis 9. Multimedia, Multi-structured, and Spatiotemporal Astronomy Data Mining 10. Novel Data Mining Models, including new algorithms available through Hadoop, MapReduce, No-SQL, etc. C. Applications Related to Solar & Stellar Big Data Management and Mining 1. Complex Space Weather Applications in Science, Engineering, Education, Navigation, 2. Power Grids, and Telecommunication for Government, Public and Private Industry Sectors 3. New Real-life Case Studies of Big Solar Data Mining (e.g. Space Weather) 4. Experiences with Big Data Mining Project Deployments in Solar Physics Successful Crowdsourced Scientific Research (e.g. Zooniverse) 5. Solar and Stellar Astronomy Data and Knowledge Distribution in the Social Web D. 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. Flares, Planets and Supernovae – Identifying Transient/Periodic Events in Time Series Data 4. Solar and Stellar Dynamo Modeling Paper Submission: 1. Please submit a full-length paper (up to 10 page IEEE 2-column format) through the online submission system. Our system for SABID 2020 submissions is already available online. 2. Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines (search for "Paper Submission" section on IEEE Big Data 2020 CFP Website). 3. After being carefully reviewed by at least 3 PC members, all accepted papers will be included in the IEEE International Conference on Big Data Workshops Proceedings, published by the IEEE Computer Society Press (also included in the IEEE Digital Library), and made available at the conference. 4. 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 Elsevier’s journal on “Astronomy and Computing”, and best works from SABID’16 were published in a highly-ranked IOPScience's “Astrophysical Journal Supplement Series” (2017 Impact Factor: 8.561). |
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