Reference number : GC-2017-Q1-02133, amendment number : , agreement type : G, recipient legal name : Osgood, Nathaniel (University of Saskatchewan), recipient country : CA, recipient province : nan, recipient city : Saskatchewan, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : Despite the rapid growing demand for -- and our contributions demonstrating the great potential of -- integration of ABMs with big data, existing simulation infrastructures provide poor support for model integration with incoming data. While our work has demonstrated that online computational statistics techniques such as Particle Filtering (PF) and Particle Markov Chain Monte Carlo (PMCMC) can form highly effective tools for integrating simulation modeling and incoming big data -- such as that from our popular iEpi system -- application of such techniques is at best awkward, and is often infeasible because of the high computational costs or high degree of implementation effort involved. To address this application area in which interdisciplinary teamwork is of central importance, we have secured strong success with our existing Frabjous domain-specific functional reactive ABM programming platform to significantly enhance ABM transparency, concision, and modularity. Despite these contributions, current ABMs commonly lack publishable specifications, are typically quite opaque to non-technical stakeholders and often confusing even to technical team members, raise significant performance barriers to scenario-based exploration by policy makers and analysts, and poor support for collaborative interaction and sharing across teams. We propose here to address these challenges using a multi-pronged strategy that starts with a port of Frabjous to the Scala language (FrabjouS) -- a language which offers strong support for modularity, parallelization, domain-specific language design and interoperability, and which we have used for many other tools. Work following this port is divided into four relatively autonomous streams. The first focuses on integrating language support for key computational statistics algorithms PF, PMCMC, and MCMC and for streaming interfaces using the streaming component of the popular Spark data science platform. The second seeks to greatly enhance ABM performance via multi-level parallelization (exploiting both distributing computing, multi-cores and GPUs at a finer-grained level), including via integration with the Spark platform. The third interface uses monadic composition to both ease common modeling tasks, and to empower model end-users to undertake analysis tasks traditionally requiring programmer support. Finally, in collaborations with a leading researcher in this the area of Human Computer Interaction (HCI) and Computer Supported Cooperative Work (CSCW), we will adapt techniques successfully used in our existing collaborative model mapping tools to implement a graphical specification language for FrabjouS as well as a collaborative tool supporting multi-user exploration, running and modification of FrabjouS models. Finally, across each phase of the work, we will evaluate model success with user studies., program purpose french : Despite the rapid growing demand for -- and our contributions demonstrating the great potential of -- integration of ABMs with big data, existing simulation infrastructures provide poor support for model integration with incoming data. While our work has demonstrated that online computational statistics techniques such as Particle Filtering (PF) and Particle Markov Chain Monte Carlo (PMCMC) can form highly effective tools for integrating simulation modeling and incoming big data -- such as that from our popular iEpi system -- application of such techniques is at best awkward, and is often infeasible because of the high computational costs or high degree of implementation effort involved. To address this application area in which interdisciplinary teamwork is of central importance, we have secured strong success with our existing Frabjous domain-specific functional reactive ABM programming platform to significantly enhance ABM transparency, concision, and modularity. Despite these contributions, current ABMs commonly lack publishable specifications, are typically quite opaque to non-technical stakeholders and often confusing even to technical team members, raise significant performance barriers to scenario-based exploration by policy makers and analysts, and poor support for collaborative interaction and sharing across teams. We propose here to address these challenges using a multi-pronged strategy that starts with a port of Frabjous to the Scala language (FrabjouS) -- a language which offers strong support for modularity, parallelization, domain-specific language design and interoperability, and which we have used for many other tools. Work following this port is divided into four relatively autonomous streams. The first focuses on integrating language support for key computational statistics algorithms PF, PMCMC, and MCMC and for streaming interfaces using the streaming component of the popular Spark data science platform. The second seeks to greatly enhance ABM performance via multi-level parallelization (exploiting both distributing computing, multi-cores and GPUs at a finer-grained level), including via integration with the Spark platform. The third interface uses monadic composition to both ease common modeling tasks, and to empower model end-users to undertake analysis tasks traditionally requiring programmer support. Finally, in collaborations with a leading researcher in this the area of Human Computer Interaction (HCI) and Computer Supported Cooperative Work (CSCW), we will adapt techniques successfully used in our existing collaborative model mapping tools to implement a graphical specification language for FrabjouS as well as a collaborative tool supporting multi-user exploration, running and modification of FrabjouS models. Finally, across each phase of the work, we will evaluate model success with user studies., agreement title english : Cross-Leveraging Computational, System and Data Science in Support of Computational Epidemiology in the Era of Big Data, agreement title french : Cross-Leveraging Computational, System and Data Science in Support of Computational Epidemiology in the Era of Big Data, agreement number : RGPIN, agreement value : 100000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada