Workshop on Data and Computational Science Technologies for Earth Science Research
October 29, 2015 – November 1, 2015
Santa Clara, CA
Workshop Description
Currently, the analysis of large data collections from earth science research is executed through traditional computational and data analysis approaches, which require users to bring data to their desktops and perform local data analysis. Future earth science remote sensing missions, which historically assume that all data can be collected, transmitted, processed, and archived, may not scale as more capable instruments stress existing architectural approaches and systems. A new paradigm is needed in order to increase the productivity and effectiveness of scientific data analysis. This paradigm must recognize that architectural and analytical choices are interrelated, and must be carefully coordinated in any system that aims to allow efficient, interactive scientific exploration and discovery to exploit massive data collections, from point of collection (e.g., onboard) to analysis and decision support. Both future observational systems, including satellite and airborne experiments, and research in climate modeling will significantly increase the size of the data requiring new approaches across the entire data lifecycle from capture to generation, management, and analysis of the data.The workshop seeks computational and data science experts to present on their research and discuss Big Data roadmaps, architectures, technologies, and methodologies for future Earth Science data challenges emerging from both observational systems and climate studies.
Technical Focus
I. Architectural considerations/tradeoffs for integrating the entire data lifecycle from observational systems to climate modeling and research
• Approaches for scaling observing systems for satellite, airborne and ground-based sensors
• Integration of computational methods with observing systems
• New concepts for data intensive missions
• Integration of data, computing/HPC/cloud, and algorithms
• Cloud computing, software as a service
• New technologies (e.g., distributed frameworks, database technologies, search, etc) for scaling the architecture
II. Onboard/Sensor-based Computing
• Embedded and real-time data reduction and triage/analytics methods
• Managing bandwidth constraints for high volume instruments
III. Scalable Data Management and Computation for ground-based systems
• Capturing well-architected and curated data repositories
• Data and semantic information architectures
• Architecting automated pipelines for data capture
• Enabling analytics on data pipelines for computation, data discovery, event detection, reduction, etc
• Open source data science frameworks
• Cloud computing
IV. Scalable Data Analytics for Massively Distributed Data
• Access and integration of highly distributed, heterogeneous data
• Novel statistical approaches for data integration and fusion
• Sampling strategies from massive data repositories
• Uncertainty in scientific inferences
• In situ analysis for High Performance Computing
• Computation applied at the data sources
• Automated Machine Learning methods for identifying and extracting interesting features and patterns
• Methods for visualizing massive observational and model data
Papers
Extended abstracts, up to 4 pages, are solicited for the workshop on any of the topics below.Papers should be submitted through the IEEE Big Data Workshop online submission page. Please see Important Dates for the timing of submissions.
Topics:
I. Architectural considerations/tradeoffs for integrating the entire data lifecycle from observational systems to climate modeling and research
• Approaches for scaling observing systems for satellite, airborne and ground-based sensors
• Integration of computational methods with observing systems
• New concepts for data intensive missions
• Integration of data, computing/HPC/cloud, and algorithms
• Cloud computing, software as a service
• New technologies (e.g., distributed frameworks, database technologies, search, etc) for scaling the architecture
II. Onboard/Sensor-based Computing
• Embedded and real-time data reduction and triage/analytics methods
• Managing bandwidth constraints for high volume instruments
III. Scalable Data Management and Computation for ground-based systems
• Capturing well-architected and curated data repositories
• Data and semantic information architectures
• Architecting automated pipelines for data capture
• Enabling analytics on data pipelines for computation, data discovery, event detection, reduction, etc
• Open source data science frameworks
• Cloud computing
IV. Scalable Data Analytics for Massively Distributed Data
• Access and integration of highly distributed, heterogeneous data
• Novel statistical approaches for data integration and fusion
• Sampling strategies from massive data repositories
• Uncertainty in scientific inferences
• In situ analysis for High Performance Computing
• Computation applied at the data sources
• Automated Machine Learning methods for identifying and extracting interesting features and patterns
• Methods for visualizing massive observational and model data
Target Audience
Data and computational science technologists, earth science research community, government program managers in data science and computation.
Papers
Extended abstracts are solicited that cover the research areas described in this workshop. Speakers will be chosen from the abstracts. Go here for information on abstract and paper submissions.
Important Dates
- August 30, 2015 Due date for full workshop papers submission
- September 20, 2015 Notification of paper acceptance to authors
- October 5, 2015 Camera-ready of accepted papers
- October 29–November 1, 2015 Workshops
Workshop Report
A workshop report will be produced highlighting the roadmap and technologies presented.
More Information
Contact Dan.Crichton (at) jpl.nasa.gov
This opportunity has expired. It was originally published here:
http://ieee-bigdata-earthscience.jpl.nasa.gov/