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.

Creating an animated data video with audio narration is a time-consuming and complex task that requires expertise. It involves designing complex animations, turning written scripts into audio narrations, and synchronizing visual changes with the narrations. This paper presents WonderFlow, an interactive authoring tool, that facilitates narration-centric design of animated data videos. WonderFlow allows authors to easily specify semantic links between text and the corresponding chart elements. Then it automatically generates audio narration by leveraging text-to-speech techniques and aligns the narration with an animation. WonderFlow provides a structure-aware animation library designed to ease chart animation creation, enabling authors to apply pre-designed animation effects to common visualization components. Additionally, authors can preview and refine their data videos within the same system, without having to switch between different creation tools. A series of evaluation results confirmed that WonderFlow is easy to use and simplifies the creation of data videos with narration-animation interplay.

Distributed query processing systems such as Apache Hive and Spark are widely-used in many organizations for large-scale data analytics. Analyzing and understanding the query execution process of these systems are daily routines for engineers and crucial for identifying performance problems, optimizing system configurations, and rectifying errors. However, existing visualization tools for distributed query execution are insufficient because (i) most of them (if not all) do not provide fine-grained visualization (i.e., the atomic task level), which can be crucial for understanding query performance and reasoning about the underlying execution anomalies, and (ii) they do not support proper linkages between system status and query execution, which makes it difficult to identify the causes of execution problems. To tackle these limitations, we propose QEVIS, which visualizes distributed query execution process with multiple views that focus on different granularities and complement each other. Specifically, we first devise a query logical plan layout algorithm to visualize the overall query execution progress compactly and clearly. We then propose two novel scoring methods to summarize the anomaly degrees of the jobs and machines during query execution, and visualize the anomaly scores intuitively, which allow users to easily identify the components that are worth paying attention to. Moreover, we devise a scatter plot-based task view to show a massive number of atomic tasks, where task distribution patterns are informative for execution problems. We also equip QEVIS with a suite of auxiliary views and interaction methods to support easy and effective cross-view exploration, which makes it convenient to track the causes of execution problems. QEVIS has been used in the production environment of our industry partner, and we present three use cases from real-world applications and user interview to demonstrate its effectiveness. QEVIS is open-source at https://github.com/DBGroup-SUSTech/QEVIS.

Model watermarking has become an important solution to protect the intellectual property right (IPR) of deep neural networks (DNN) models. However, there are few researches on the IPR protection of generative adversarial networks (GAN), which are widely used to generate photorealistic images. In the current backdoor-based watermarking method for GAN models, the trigger pattern of the watermark is easy to be detected and invalidated by the adversary, which may fail to achieve IPR protection. To address this drawback, we propose a new GAN model watermarking method, where an invisible backdoor based on steganography is injected into the target GAN model as a watermark. Experimentally, the proposed method effectively trades off the performance of GAN on original task and the robustness of watermarking for removal attacks. Moreover, the generated triggers can effectively resist being detected by attackers.

As a popular distributed data warehouse system, Apache Hive has been widely used for big data analytics in many organizations. Meanwhile, exploiting the massive parallelism of GPU to accelerate online analytical processing (OLAP) has been extensively explored in the database community. In this paper, we present GHive, which enhances CPU-based Hive via CPU-GPU heterogeneous computing. GHive is designed for the business intelligence applications and provides the same API as Hive for compatibility. To run SQL queries jointly on both CPU and GPU, GHive comes with three key techniques: (i) a novel data model gTable, which is column-based and enables efficient data movement between CPU memory and GPU memory; (ii) a GPU-based operator library Panda, which provides a complete set of SQL operators with extensively optimized GPU implementations; (iii) a hardware-aware MapReduce job placement scheme, which puts jobs judiciously on either GPU or CPU via a cost-based approach. In the experiments, we observe that GHive outperforms Hive in both query processing speed and operating expense on the Star Schema Benchmark (SSB).

Online social networks have stimulated communications over the Internet more than ever, making it possible for secret message transmission over such noisy channels. In this paper, we propose a Coverless Image Steganography Network, called CIS-Net, that synthesizes a high-quality image directly conditioned on the secret message to transfer. CIS-Net is composed of four modules, namely, the Generation, Adversarial, Extraction, and Noise Module. The receiver can extract the hidden message without any loss even the images have been distorted by JPEG compression attacks. To disguise the behaviour of steganography, we collected images in the context of profile photos and stickers and train our network accordingly. As such, the generated images are more inclined to escape from malicious detection and attack. The distinctions from previous image steganography methods are majorly the robustness and losslessness against diverse attacks. Experiments over diverse public datasets have manifested the superior ability of anti-steganalysis.

Face anonymization with generative models have become increasingly prevalent since they sanitize private information by generating virtual face images, ensuring both privacy and image utility. Such virtual face images are usually not identifiable after the removal or protection of the original identity. In this paper, we formalize and tackle the problem of generating identifiable virtual face images. Our virtual face images are visually different from the original ones for privacy protection. In addition, they are bound with new virtual identities, which can be directly used for face recognition. We propose an Identifiable Virtual Face Generator (IVFG) to generate the virtual face images. The IVFG projects the latent vectors of the original face images into virtual ones according to a user specific key, based on which the virtual face images are generated. To make the virtual face images identifiable, we propose a multi-task learning objective as well as a triplet styled training strategy to learn the IVFG. We evaluate the performance of our virtual face images using different face recognizers on diffident face image datasets, all of which demonstrate the effectiveness of the IVFG for generate identifiable virtual face images.

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.

In this paper, we propose a novel reversible face privacy-preserving scheme. Before uploading facial images onto the cloud, we first cover the facial region with mosaic and train an encoder to generate protected images with original facial information embedded. We train another classifier with protected images for facial expression recognition and a decoder for recovering original facial images. On the cloud service, protected images provide little identity information to malicious attackers. For low-privileged users, they can use the provided classifier to do computer vision tasks with protected images. For authorized users, after content recovery, the nor-mal usage of the facial images will not be affected. Experimental results show that the proposed method is effective in facial images recovery. In addition, the protected images can maintain similar accuracy on typical computer vision tasks compared to the original images.