Many database optimization problems, e.g., slow SQL diagnosis, database testing, optimizer tuning, require a large volume of SQL queries. Due to privacy issues, it is hard to obtain real SQL queries, and thus SQL generation is a very important task in database optimization. Existing SQL generation methods either randomly generate SQL queries or rely on human-crafted SQL templates to generate SQL queries, but they cannot meet various user specific requirements, e.g., slow SQL queries, SQL queries with large result sizes. To address this problem, this paper studies the problem of constraint-aware SQL generation, which, given a constraint (e.g., cardinality within [1k,2k]), generates SQL queries satisfying the constraint. This problem is rather challenging, because it is rather hard to capture the relationship from query constraint (e.g., cardinality and cost) to SQL queries and thus it is hard to guide a generation method to explore the SQL generation direction towards meeting the constraint. To address this challenge, we propose a reinforcement learning (RL) based framework LearnedSQLGen, for generating queries satisfying the constraint. LearnedSQLGen adopts an exploration-exploitation strategy that exploits the generation direction following the query constraint, which is learned from query execution feedback. We judiciously design the reward function in RL to guide the generation process accurately. We integrate a finite-state machine to generate valid SQL queries. Experimental results on three benchmarks showed that LearnedSQLGen significantly outperformed the baselines in terms of both accuracy (30% better) and efficiency (10-35 times).

Spatio-temporal data analysis is very important in many time-critical applications. We take Coronavirus disease (COVID-19) as an example, and the key questions that everyone will ask every day are: how does Coronavirus spread? where are the high-risk areas? where have confirmed cases around me? Interactive data analytics, which allows general users to easily monitor and explore such events, plays a key role. However, some emerging cases, such as COVID-19, bring many new challenges: (C1) New information may come with different formats: basic structured data such as confirmed/suspected/serious/death/recovered cases, unstructured data from newspapers for travel history of confirmed cases, and so on. (C2) Discovering new insights: data visualization is widely used for storytelling; however, the challenge here is how to automatically find "interesting stories", which might be different from day to day. We propose DeepTrack, a system that monitors spatio-temporal data, using the case of COVID-19. For (C1), we describe (a) how we integrate and clean data from different sources by existing modules. For (C2), we discuss (b) how to build new modules for ad-hoc data sources and requirements, (c) what are the basic (or static) charts used; and (d) how to generate recommended (or dynamic) charts that are based on new incoming data. The attendees can use DeepTrack to interactively explore various COVID-19 cases.