Across many domains (e.g., media/entertainment, mobile apps, finance, IoT, cybersecurity), there is a growing need for stateful analytics over streams of events to meet key business outcomes. Stateful analytics over event streams entails carefully modeling the sequence, timing, and contextual correlations of events to dynamic attributes. Unfortunately, existing frameworks and languages (e.g., SQL, Flink, Spark) entail significant code complexity and expert effort to express such stateful analytics because of their dynamic and stateful nature. Our overarching goal is to simplify and democratize stateful analytics. Through an iterative design and evaluation process including a foundational user study and two rounds of formative evaluations with 15 industry practitioners, we created SEAM-EZ, a no-code visual programming platform for quickly creating and validating stateful metrics. SEAM-EZ features a node-graph editor, interactive tooltips, embedded data views, and auto-suggestion features to facilitate the creation and validation of stateful analytics. We then conducted three real-world case studies of SEAM-EZ with 20 additional practitioners. Our results suggest that practitioners who previously could not or had to spend significant effort to create stateful metrics using traditional tools such as SQL or Spark can now easily and quickly create and validate such metrics using SEAM-EZ.
https://doi.org/10.1145/3613904.3642055
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)