MIRACLE (mining relationships among variables in large datasets from complex systems) is a Digging into Data (DiD) project that aims to build a cloud-based community platform for reproducible data analysis, visualization and management for agent-based model (ABM) output data.
Waterloo Institute for Complexity and Innovation (WICI) core member and director Dawn Parker has received new grant funding from the Social Sciences and Humanities Research Council (SSHRC) via the Digging into Data challenge. The international DiD program was established to advance the use of computational methods to explore, analyze and visualize the rapidly expanding pool of crowdsourced and remotely sensed “big data” from real-world systems. Unique among this year’s awards, Parker’s research team is developing tools to analyze output from computerized simulation models and compare that output to real-world “big data.”
The University of Waterloo is the lead institution for the larger DiD $567,000 (U.S.) project titled, "Mining relationships among variables in large datasets from complex systems (MIRACLE)." Local WICI team members include post-doc Xiongbing Jin and graduate student Kirsten Robinson. The project will be hosted through the Waterloo Institute for Complexity and Innovation. The international team includes participants from:
- Arizona State University, U.S.A. (principal investigator: C. Michael Barton),
- University of Twente, The Netherlands (principal investigator: Tatiana Filatova),
- University of Dundee, U.K. (principal investigator: Terence P. Dawson), and
- James Hutton Institute, U.K. (collaborator: J. Gary Pohill).
MIRACLE will be a community platform that will support complex systems research across research communities. Our research group is very appreciative of SSHRC’s top dollar support for our innovative new venture to create community infrastructure that will be available to local stakeholders, university researchers, and the international community to support complex systems research.
- GitHub repository for the MIRACLE platform
- GitHub repository for the example projects
- Demo server (please contact the development team if you would like a test account. Alternatively, you can setup a local testing server using the instructions provided in our GitHub repository)
- Download a draft paper on how to use the prototype cloud-based MIRACLE platform (PDF).
- You can download an overview of the technical details of this research project (PDF).
- Call for participation: IEMSS 2016 workshop A4: “The MIRACLE Prototype”
- Download the Call for Participation (PDF) to the related workshop at the 8th International Congress on Environmental Modelling and Software (iEMSs 2014) conference.
Publications and presentations
- Jin, Xiongbing; Robinson, Kirsten; Lee, Allen; Polhill, Gary; Pritchard, Calvin; Parker, Dawn. MIRACLE: A prototype cloud-based reproducible data analysis and visualization platform for outputs of agent-based models. Submitted for review in 2016.
- Parker, Dawn; Filatova, Tatiana; Barton, Michael; Dawson, Terry; Polhill, Gary; Milazzo, Lorenzo; Lee, Allen; Lee, Ju-Sung; Voinov, Alexey; Robinson, Kirsten; Jin, Xiongbing; Pritchard, Calvin.MIRACLE. 2016 "Round Three" Digging into Data Challenge Conference, Glasgow, U.K., 2016.
Parker, Dawn; Barton, Michael; Dawson, Terry; Filatova, Tatiana; Jin, Xiongbing; Lee, Allen; Lee, Ju-Sung; Milazzo, Lorenzo; Pritchard, Calvin; Polhill, Gary; Robinson, Kirsten; Voinov, Alexey. The MIRACLE project: Cyber infrastructure for visualizing model outputs. 28 September 2015.
Principal investigator (PI):
- Professor Dawn Parker, School of Planning, University of Waterloo
- Professor Michael Barton, Center for Social Dynamics & Complexity, Arizona State University
- Professor Tatiana Filatova, University of Twente
- Professor Terence Dawson, Centre for Environmental Change and Human Resilience, University of Dundee
- Gary Polhill
- Xiongbing Jin
- Allen Lee
- Calvin Pritchard
- Lorenzo Milazzo
- Ju-Sung Lee
- June 6, 2016 – Call for participation: IEMSS 2016 workshop A4: “The MIRACLE Prototype”
- January 16, 2014 – National Science Foundation contributes to four international projects in data-intensive social science and humanities research
- January 17, 2014 – Waterloo researcher uses big data to see LRT impact on housing market
- January 23, 2014 – ASU professor wins ‘Digging Into Data’ challenge