SQLCheck: Automated Detection and Diagnosis of SQL Anti-Patterns

The emergence of database-as-a-service platforms has made deploying database applications easier than before. Now, developers can quickly create scalable applications. However, designing performant, maintainable, and accurate applications is challenging. Developers may unknowingly introduce anti-patterns in the application's SQL statements. These anti-patterns are design decisions that are intended to solve a problem, but often lead to other problems by violating fundamental design principles. In this paper, we present SQLCheck, a holistic toolchain for automatically finding and fixing anti-patterns in database applications. We introduce techniques for automatically (1) detecting anti-patterns with high precision and recall, (2) ranking the anti-patterns based on their impact on performance, maintainability, and accuracy of applications, and (3) suggesting alternative queries and changes to the database design to fix these anti-patterns. We demonstrate the prevalence of these anti-patterns in a large collection of queries and databases collected from open-source repositories. We introduce an anti-pattern detection algorithm that augments query analysis with data analysis. We present a ranking model for characterizing the impact of frequently occurring anti-patterns. We discuss how SQLCheck suggests fixes for high-impact anti-patterns using rule-based query refactoring techniques. Our experiments demonstrate that SQLCheck enables developers to create more performant, maintainable, and accurate applications.Comment: 18 pages (14 page paper, 1 page references, 2 page Appendix), 12 figures, Conference: SIGMOD'2

SketchQL Demonstration: Zero-shot Video Moment Querying with Sketches

In this paper, we will present SketchQL, a video database management system (VDBMS) for retrieving video moments with a sketch-based query interface. This novel interface allows users to specify object trajectory events with simple mouse drag-and-drop operations. Users can use trajectories of single objects as building blocks to compose complex events. Using a pre-trained model that encodes trajectory similarity, SketchQL achieves zero-shot video moments retrieval by performing similarity searches over the video to identify clips that are the most similar to the visual query. In this demonstration, we introduce the graphic user interface of SketchQL and detail its functionalities and interaction mechanisms. We also demonstrate the end-to-end usage of SketchQL from query composition to video moments retrieval using real-world scenarios

Accelerating Video Analytics

MOTIVATION. The advent of inexpensive, high-quality cameras has led to a rapid increase in the volume of generated video data [19, 16]. It is now feasible to automatically analyze these video datasets at scale due to two developments over the last decade. First, researchers have designed complex, computationally-intensive deep learning (DL) models that capture the contents of a given set of video frames (e.g., objects present in a particular frame [11]) [15]. Second, the computational capabilities of hardware accelerators for evaluating these DL models have increased over the last decade (e.g., TPUs) [8]. We anticipate that automated analysis of videos will reduce the labor cost of analyzing video</jats:p