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Domains such as energy management rely heavily on dashboards and other related interfaces to manage the infrastructure and resources. The users of this domain use dashboards to manage the data and extensively perform periodic analysis to save energy and cost. Creating multiple dashboards for visualization of data is not user-friendly from a design perspective. This motivates the need of a single interface through which users can do data exploration, visualization, and summarizing. Combining this with features such as anomaly detection can identify various issues and assist in day-to-day monitoring of an energy management center. In this paper, we present the resource optimization center (ROC) Bot, a novel data exploration tool with a natural language interface. ROC Bot leverages recent advances in deep models to make query understanding more robust in the following ways: first, ROC Bot uses a deep model to translate natural language statements to SQL, making the translation process more robust to paraphrasing and other linguistic variations. Second, to support the users in automatically summarizing data, ROC Bot provides a machine learning model that helps in writing natural looking summaries in any given tabular data.

Research area: Deep learning and AI

Authors: Amit Sangroya, Rishi Tiwari, Mohammed Afaque, Mrinal Rawat

Conference/event: Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2021)

Conference date: May 2021