Indexes are vital to enhance the lookup on single or multiple columns, and building proper indexes can significantly improve the database performance. Existing works focus on adding new indexes that can benefit the read queries, but they have several limitations. First, real-world workloads may have numerous queries and it is tricky to analyze their index requirements and find the most beneficial indexes within resource limit. Second, they fail to consider the update of existing indexes, which may be redundant or even have negative effects to current workload. Third, they cannot estimate the index maintenance costs, which are affected by multiple index utilization factors and can significantly affect the index benefits, especially for high-write-ratio workloads. To address those challenges, we propose an incremental index management system Autoindex for dynamic workloads. First, to support incremental index management, we map the incoming queries into query templates and efficiently generate promising candidate indexes from matched templates. And then we propose to utilize Monte Carlo Tree Search to incrementally add indexes from the candidate indexes or remove indexes from existing indexes, so as to ensure high workload performance. Besides, we propose a deep index estimation model, which integrates the practical experience to extract critical cost features and applies deep regression to estimate index benefits from historical index management data. We have implemented the modules like candidate index generation and index estimator in an open-sourced database system openGauss. Experimental re-sults showed that our method outperformed existing approaches on both testing and real-world workloads.

In this work, we present a self-driving data visualization system, calledDeepEye, that automatically generates and recommends visualizations based on the idea ofvisualization by examples.We propose effective visualization recognition techniques to decide which visualizations are meaningful and visualization ranking techniques to rank the good visualizations. Furthermore, a main challenge of automatic visualization system is that the users may be misled by blindly suggesting visualizations without knowing the user's intent. To this end, we extendDeepEyeto be easily steerable by allowing the user to usekeyword searchand providing click-basedfaceted navigation. Empirical results, using real-life data and use cases, verify the power of our proposed system.

Natural language (NL) is a promising interaction paradigm for data visualization (VIS). However, there are not any NL to VIS (NL2VIS) benchmarks available. Our goal is to provide the first NL2VIS benchmark to enable and push the field of NL2VIS, especially with deep learning technologies. In this paper, we propose a NL2VIS synthesizer (NL2SQL-to-NL2VIS) that synthesizes NL2VIS benchmarks by piggybacking NL2SQL benchmarks. The intuition is based on the semantic connection between SQL queries and VIS queries: SQL queries specify what data is needed and VIS queries additionally need to specify how to visualize. However, different from SQL that has well-defined syntax, VIS languages (e.g., Vega-Lite, VizQL, ggplot2) are syntactically very different. To provide NL2VIS benchmarks that can support many VIS languages, we use a unified intermediate representation, abstract syntax trees (ASTs), for both SQL and VIS queries. We can synthesize multiple VIS trees through adding/deleting nodes to/from an SQL tree. Each VIS tree can then be converted to (any) VIS language. The NL for VIS will be modified based on the NL for SQL to reflect corresponding tree edits. We produce the first NL2VIS benchmark (nvBench), by applying NL2SQL-to-NL2VIS on a popular NL2SQL benchmark Spider, which covers 105 domains, supports seven common types of visualizations, and contains 25,750 (NL, VIS) pairs. Our method reduces the man-hour to 5.7% of developing a NL2VIS benchmark from scratch (or building a NL2VIS benchmark from scratch takes 17.5× man-hours of our method). Extensive human validation, through 23 experts and 312 crowd workers, demonstrates the high-quality of nvBench. In order to verify that nvBench can enable learning-based approaches, we develop a SEQ2VIS model. Our experimental results show that SEQ2VIS works well and significantly outperforms the state-of-the-art methods of the NL2VIS task.

Although learning-based database optimization techniques have been studied from academia in recent years, they have not been widely deployed in commercial database systems. In this work, we build an autonomous database framework and integrate our proposed learning-based database techniques into an open-source database system openGauss. We propose effective learning-based models to build learned optimizers (including learned query rewrite, learned cost/cardinality estimation, learned join order selection and physical operator selection) and learned database advisors (including self-monitoring, self-diagnosis, self-configuration, and self-optimization). We devise an effective validation model to validate the effectiveness of learned models. We build effective training data management and model management platforms to easily deploy learned models. We have evaluated our techniques on real-world datasets and the experimental results validated the effectiveness of our techniques. We also provide our learnings of deploying learning-based techniques.

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.