Tempe: An Interactive Data Science Environment for Exploration of Temporal and Streaming Data

Over the last two decades, data scientists performed increasingly sophisticated analyses on larger data sets, yet their tools and workflows remain low-level. A typical analysis involves different tools for different stages of the work, requiring file transfers and considerable care to keep everything organized. Temporal data adds additional complexity: users typically must write queries offline before porting them to production systems. To address these problems, this paper introduces Tempe, a web application providing an integrated, collaborative environment for both real-time and offline temporal data analysis. Tempe's central concept is a persistent research notebook retaining data sources, analysis steps and results. Analysis steps are carried out in script editor that uses a live programming approach to display interactive, progressively updated visualizations. Tempe uses a temporal streaming engine, Trill [17], as its backend data processor. In the process of creating Tempe, we have discovered new interactivity and responsiveness requirements for Trill. Conversely, building around Trill has shaped the user experience for Tempe. We report on this cross-disciplinary design process to argue that end user experience can be an integral part of creating a data engine.