SQL query performance is critical in database applications, and query rewriting is a technique that transforms an original query into an equivalent query with a better performance. In a wide range of database-supported systems, there is a unique problem where both the application and database layer are black boxes, and the developers need to use their knowledge about the data and domain to rewrite queries sent from the application to the database for better performance. Unfortunately, existing solutions do not give the users enough freedom to express their rewriting needs. To address this problem, we propose QueryBooster, a novel middleware-based service architecture for human-centered query rewriting, where users can use its expressive and easy-to-use rule language (called VarSQL) to formulate rewriting rules based on their needs. It also allows users to express rewriting intentions by providing examples of the original query and its rewritten query. QueryBooster automatically generalizes them to rewriting rules and suggests high-quality ones. We conduct a user study to show the benefits of VarSQL to formulate rewriting rules. Our experiments on real and synthetic workloads show the effectiveness of the rule-suggesting framework and the significant advantages of using QueryBooster for human-centered query rewriting to improve the end-to-end query performance.

Query rewriting is an important technique to optimize SQL performance in databases. With the prevalent use of business intelligence systems and object-relational mapping frameworks, existing rewriting capabilities inside databases are insufficient to optimize machine-generated queries. In this paper, we propose a novel system called "QueryBooster," to support SQL query rewriting as a cloud service. It provides a powerful and easy-to-use Web interface for users to formulate rewriting rules via a language or express rewriting intentions by providing example query pairs. It allows multiple users to share rewriting knowledge and automatically suggests shared rewriting rules for users. It requires no modifications or plugin installations to applications or databases. In this demonstration, we use real-world applications and datasets to show the user experience of QueryBooster to rewrite their application queries and share rewriting knowledge.

With the growing popularity of social networks, it becomes increasingly important to analyze binary relationships between entities such as users or online posts. These relationships are particularly useful when the entities are location-based. The spatial dimension provides more insights on influences in social networks across different regions. In this paper, we study how to visualize geospatial relationships on a large social network for queries with ad hoc conditions (such as keyword search) that retrieves a subnetwork. We focus on a main efficiency challenge to support responsive visualization, and present a middleware-based system called GSViz that progressively answers requests and computes results incrementally. GSViz minimizes visual clutter by clustering the spatial points while considering the edges among them. It further minimizes the clutter by incrementally bundling the edges, i.e., grouping similar edges in a bundle to increase the screen's white space. The system allows user interactions such as zooming and panning. We conducted an extensive computational study on real data sets and a user study, which evaluated the system's performance and the quality of its visualization results.

We study the problem of efficient spatial visualization on a large data set stored in a database using SQL queries with ad-hoc range conditions on numerical attributes, for example, a spatial scatterplot of taxi pickup events in New York between 1/1/2015 and 3/10/2015. We present a novel middleware-based technique called Marviq. It divides the selection-attribute domain into intervals, and precomputes and stores a visualization for each interval. These results are called MVS and stored as tables in the database. We can compute an exact visualization for a request by accessing MVS and retrieving additional records from the base table. To further reduce the latter time, we present algorithms for using MVS to compute an approximate visualization that satisfies a user-specified similarity threshold. We show a family of functions with certain properties that can use this technique. We present an improvement by dividing the MVS intervals into smaller intervals and materializing low-resolution visualization for these intervals. We report the results of an extensive evaluation of Marviq, including a user study, and show its high performance in both space and time.

Keyword-based heat maps are a natural way to explore and analyze the spatial properties of social media data. Dealing with large datasets, there may be many different keywords, making offline pre-computations very hard. Interactive frameworks that exploit database sampling can address this challenge. We present a novel middleware technique called Heatflip, which issues diametrically opposed samples into the temporal and spatial dimensions of the data stored in an external database. Spatial samples provide insights into the temporal distribution and vice versa. The progressive exploration approach benefits from adaptive indexing and combines the retrieval and visualization of the data in a middleware layer. Without any a priori knowledge of the underlying data, the middleware can generate accurate heat maps in 85% shorter processing times than conventional systems. In this paper, we discuss the analytical background of Heatflip, showcase its scalability, and validate its performance when visualizing large amounts of social media data.

The Earth Mover's Distance, or EMD for short, has been proven to be effective for content-based image retrieval. However, due to the cubic complexity of EMD computation, it remains difficult to use EMD in applications with stringent requirement for efficiency. In this paper, we present our new system, called EUDEMON, which utilizes new techniques to support fast Online Video Frame Copy Detection based on the EMD. Given a group of registered frames as queries and a set of targeted detection videos, EUDEMON is capable of identifying relevant frames from the video stream in real time. The significant improvement on efficiency mainly relies on the primal-dual theory in linear programming and well-designed B+tree filters for adaptive candidate pruning. Generally speaking, our system includes a variety of new features crucial to the deployment of EUDEMON in real applications. First, EUDEMON achieves high throughput even when a large number of queries are registered in the system. Second, EUDEMON contains self-optimization component to automatically enhance the effectiveness of the filters based on the recent content of the video stream. Finally, EUDEMON provides a user-friendly visualization interface, named EMD Flow Chart, to help the users to better understand the alarm with the perspective of the EMD.