Mining Chained Modules in Analytic Center Framework

NASA is building Analytic Center Framework (ACF) as a collaboration platform for community users to harmonize existing tools, data and computing environments. In the next 5-10-year timeframe, it can be anticipated many data analytics tools and models will be published onto the NASA ACF as reusable software modules. However, a large number of software modules will make it difficult for Earth scientists to choose from as components to build their new data analytics experiments (workflows). How to help Earth scientists find suitable software modules from a sea of available candidates and use them productively will soon become a big challenge for ACF thus demands a systematic study. Therefore, we propose this AIST project preparing for the next 5-10-year timeframe, aiming to build a workflow tool as a building block for ACF, which is capable of recommending multiple software modules, already chained together, based on their past usage history. We propose to study how software modules collaborated, or could collaborate, to serve various workflows. Based on such investigations, we aim to develop a recommend-as-you-go technique to assist Earth scientists in designing data analytics workflows to address previously intractable scientific questions. The ultimate goal is to reduce time-to-experiment, share expertise and avoid reinvention of wheels. It should be noted that in recent years, many Earth scientists have adopted the Jupyter Notebook to conduct interactive data analytics. Thus, we will leverage the Jupyter Notebook as the environment to focus on the following three objectives: (1) to develop algorithms to mine software module usage history and dependencies from Jupyter notebooks, and construct a knowledge network to store and retrieve data effectively; (2) to develop algorithms to explore and extract reusable chain of software modules from the knowledge network; and (3) to develop an intelligent service that provides personalized recommend-as-you-go support to help Earth scientists design workflows.

Advance Earth system science knowledge through the identification, development, and demonstration of innovative information systems technologies

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