Understanding and diagnosing query optimizers is crucial to guarantee the correctness and efficiency of query processing in database systems. However, achieving this is non-trivial as there are three technical challenges: (i) hundreds and thousands of query plans are generated for each query during the query optimization procedure; (ii) the transformation logic among query plans is not easy to investigate even for expert database system developers; and (iii) navigating users to the root causes of the bugs/errors is inherently hard as the changes of the operators among query plans are missing in the query processing log. In this work, we propose QOVIS to overcome these challenges, which identifies the query optimization bugs/issues and investigates their root causes via a visualization-assisted approach. Specifically, QOVIS consists of data preprocessing layer, transformation logic computation layer, and visual analysis layer. We conduct extensive experimental studies (e.g., user study, case study, and performance study) to evaluate the efficiency and effectiveness of QOVIS. In particular, our user study (on 24 database developers and researchers) confirms that QOVIS significantly reduces the time required to investigate the bugs/errors in the query optimizer. Moreover, the generality of QOVIS is verified by utilizing it to understand and diagnose the real-world reported bugs/errors in different query optimizers of three widely-used systems: Apache Spark, Apache Hive, and DuckDB.

Visualizing large-scale trajectory dataset is a core subroutine for many applications. However, rendering all trajectories could result in severe visual clutter and incur long visualization delays due to large data volume. Naively sampling the trajectories reduces visualization time but usually harms visual quality, i.e., the generated visualizations may look substantially different from the exact ones without sampling. In this paper, we propose$\mathsf {CheetahTraj}$, a principled sampling framework that achieves both high visualization quality and low visualization latency. We first define thevisual quality functionmeasuring the similarity between two visualizations, based on which we formulate the quality optimal sampling problem (${\sf QOSP}$). To solve${\sf QOSP}$, we design theVisualQualityGuaranteedSampling algorithms, which reduce visual clutter while guaranteeing visual quality by considering both trajectory data distribution and human perception properties. We also develop a quad-tree-based index ($\mathsf {InvQuad}$) that allows using trajectory samples computed offline for interactive online visualization. Extensive experiments including case-, user-, and quantitative-studies are conducted on three real-world trajectory datasets, and the results show that$\mathsf {CheetahTraj}$consistently provides higher visual quality and better efficiency than baseline methods. Compared with visualizing all trajectories,$\mathsf {CheetahTraj}$reduces the visualization latency by up to 3 orders of magnitude while avoiding visual clutter.

Nowadays, Apache Hive has been widely used for large-scale data analysis applications in many organizations. Various visual analytical tools are developed to help Hive users quickly analyze the query execution process and identify the performance bottleneck of executed queries. However, existing tools mostly focus on showing the time usage of query sub-components (jobs and operators) but fail to provide enough evidence to analyze the root reasons for the slow execution progress. To tackle this problem, we develop a visual analytical system DHive to visualize and analyze the query execution progress via dataflow analysis. DHive shows the dataflow during query execution at multiple levels: query level, job level and task level, which enable users to identify the key jobs/tasks and explain their time usage by linking them to the auxiliary information such as the system configuration and hardware status. We demonstrate the effectiveness of DHive by two cases in a production cluster. DHive is open-source at https://github.com/DBGroup-SUSTech/DHive.git.

Knowledge graphs capture the complex relationships among various entities, which can be found in various real world applications, e.g., Amazon product graph, Freebase, and COVID-19. To facilitate the knowledge graph analytical tasks, a system that supports interactive and efficient query processing is always in demand. In this demonstration, we develop a prototype system, CheetahKG, that embeds with our state-of-the-art query processing engine for the top-$k$frequent pattern discovery. Such discovered patterns can be used for two purposes, (i) identifying related patterns and (ii) guiding knowledge exploration. In the demonstration sessions, the attendees will be invited to test the efficiency and effectiveness of the query engine and use the discovered patterns to analyze knowledge graphs on CheetahKG.

As a distributed, fault-tolerant data warehouse system for large-scale data analytics, Apache Hive has been used for various applications in many organizations (e.g., Facebook, Amazon, and Huawei). Exploiting the large degrees of parallelism of GPU to improve the performance of online analytical processing (OLAP) in database system is a common practice in the industry. Meanwhile, it is a common practice to exploit the large degrees of parallelism of GPU to improve the performance of online analytical processing (OLAP) in database systems. This demo presents GHive, which enables Apache Hive to accelerate OLAP queries by jointly utilizing CPU and GPU in intelligent and efficient ways. The takeaways for SIGMOD attendees include: (1) the superior performance of GHive compared with vanilla Hive that only uses CPU; (2) intuitive visualizations of execution statistics for Hive and GHive to understand where the acceleration of GHive comes from; (3) detailed profiling of the time taken by each operator on CPU and GPU to show the advantages of GPU execution.

Recently, the spatial-temporal data of urban moving objects, e.g., cars and buses, are collected and widely used in urban trajectory exploratory analysis. Urban bus service is one of the most common public transportation services. Urban bus data analysis plays an important role in smart city applications. For example, data analysts in bus companies use the urban bus data to optimize their bus scheduling plan. Map services providers, e.g., Google map, Ten-cent map, take urban bus data into account to improve their service quality (e.g., broadcast road update instantly). Unlike urban moving cars or pedestrians, urban buses travel on known bus routes. The operating buses form the "bus flows" in a city. Efficient analyzing urban bus flows has many challenges, e.g., how to analyze the dynamics of given bus routes? How to help users to identify traffic flow of interests easily? In this work, we present CheetahVIS, a visual analytical system for efficient massive urban bus data analysis. CheetahVIS builds upon Spark and provides a visual analytical platform for the stakeholders (e.g., city planner, data analysts in bus company) to conduct effective and efficient analytical tasks. In the demonstration, demo visitors will be invited to experience our proposed CheetahVIS system with different urban bus data analytical functions, e.g., bus route analysis, public bus flow overview, multiple region analysis, in a real-world dataset. We also will present a case study, which compares different regions in a city, to demonstrate the effectiveness of CheetahVIS.