Next-step suggestions for modern interactive data analysis platforms

Modern Interactive Data Analysis (IDA) platforms, such as Kibana, Splunk, and Tableau, are gradually replacing traditional OLAP/SQL tools, as they allow for easy-to-use data exploration, visualization, and mining, even for users lacking SQL and programming skills. Nevertheless, data analysis is still a difficult task, especially for non-expert users. To that end we present REACT, a recommender system designed for modern IDA platforms. In these platforms, analysis sessions interweave high-level actions of multiple types and operate over diverse datasets. REACT identifies and generalizes relevant (previous) sessions to generate personalized next-action suggestions to the user. We model the user's analysis context using a generic tree based model, where the edges represent the user's recent actions, and the nodes represent their result “screens”. A dedicated context-similarity metric is employed for efficient indexing and retrieval of relevant candidate next-actions. These are then generalized to abstract actions that convey common fragments, then adapted to the specific user context. To prove the utility of REACT we performed an extensive online and offline experimental evaluation over real-world analysis logs from the cyber security domain, which we also publish to serve as a benchmark dataset for future work.