Cross-Leveraging Computational, System and Data Science in Support of Computational Epidemiology in the Era of Big Data

交叉利用计算、系统和数据科学支持大数据时代的计算流行病学

• 批准号: RGPIN-2017-04647
• 负责人: Osgood, Nathaniel
• 金额: $ 1.46万
• 依托单位: University of Saskatchewan
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=711094
• 关键词: Cross; Leveraging; Computational; System; Data

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.

尽管对ABM与大数据集成的需求快速增长，我们的贡献也展示了ABM与大数据集成的巨大潜力，但现有的仿真基础设施对模型与传入数据集成的支持很差。虽然我们的工作已经证明，在线计算统计技术，如粒子滤波（PF）和粒子马尔可夫链蒙特卡罗（PMCMC）可以形成高度有效的工具，用于集成模拟建模和传入的大数据-例如来自我们流行的iEpi系统的数据-这些技术的应用充其量是尴尬的，并且由于所涉及的高计算成本或高程度的实现努力而常常是不可行的。 为了解决这个跨学科团队合作至关重要的应用领域，我们已经通过我们现有的Frabjous特定领域的功能反应式ABM编程平台取得了巨大的成功，以显着提高ABM的透明度，简洁性和模块化。 尽管有这些贡献，目前的ABM通常缺乏可验证的规范，通常对非技术利益相关者非常不透明，甚至对技术团队成员也经常感到困惑，对政策制定者和分析师基于XML的探索提出了重大的性能障碍，并且对团队之间的协作交互和共享的支持不足。 我们在这里建议使用多管齐下的策略来解决这些挑战，从Frabjous到Scala语言（FrabjouS）的端口开始-Scala语言为模块化，并行化，特定领域的语言设计和互操作性提供了强大的支持，我们已经用于许多其他工具。 这个端口之后的工作被分为四个相对自治的流。 第一个重点是集成关键计算统计算法PF、PMCMC和MCMC的语言支持，以及使用流行的Spark数据科学平台的流组件的流接口。 第二个目标是通过多级并行化（在更细粒度的级别上利用分布式计算，多核和GPU），包括通过与Spark平台的集成，大大提高ABM的性能。 第三个接口使用一元组合来简化常见的建模任务，并使模型最终用户能够承担传统上需要程序员支持的分析任务。 最后，在合作与领先的研究人员在这一领域的人机交互（HCI）和计算机支持的协同工作（CSCW），我们将适应技术成功地使用在我们现有的协作模型映射工具，以实现一个图形化的规范语言FrabjouS以及协作工具，支持多用户的探索，运行和修改的FrabjouS模型。 最后，在工作的每个阶段，我们将通过用户研究来评估模型的成功。