  Natural language interfaces (NLIs) provide users with a convenient way to
interactively analyze data through natural language queries. Nevertheless,
interactive data analysis is a demanding process, especially for novice data
analysts. When exploring large and complex SQL databases from different
domains, data analysts do not necessarily have sufficient knowledge about
different data tables and application domains. It makes them unable to
systematically elicit a series of topically-related and meaningful queries for
insight discovery in target domains. We develop a NLI with a step-wise query
recommendation module to assist users in choosing appropriate next-step
exploration actions. The system adopts a data-driven approach to suggest
semantically relevant and context-aware queries for application domains of
users' interest based on their query logs. Also, the system helps users
organize query histories and results into a dashboard to communicate the
discovered data insights. With a comparative user study, we show that our
system can facilitate a more effective and systematic data analysis process
than a baseline without the recommendation module.
