This paper demonstrates Waffle, a self-driving grid indexing system for moving objects. We introduce system architecture, system workflow, and user scenarios. Waffle enables the management of moving objects with less human effort while automatically improving performance.

Location-based services for moving objects are close to our lives. For example, ride-sharing services, micro-mobility services, navigation and traffic management, delivery services, and autonomous driving are all based on moving objects. The efficient management of such moving objects is therefore getting more and more important. The main challenge is the handling of a large number of location-update queries with scan queries. To address this challenge, we propose a novel in-memory grid indexing system, Waffle, for moving objects. Waffle divides a geographical space into fixed-sized cells. For efficient query processing, Waffle forms chunks, each of which consists of neighboring cells. Such a Waffle index is defined by several configuration knobs. A knob configuration has a significant impact on the performance of Waffle, and an appropriate configuration may change as objects continuously move. Therefore, we propose an online configuration tuning system, WaffleMaker, that automatically determines not only knob values but also when to change knob values, as a part of Waffle. Using a configuration determined by WaffleMaker, Waffle rebuilds the current index without blocking user queries based on a concurrency control scheme. Through extensive experiments, we show that Waffle performed better than the existing methods, and WaffleMaker automatically tuned configuration knob values.

With the generation of large-scale spatial data in various fields, array databases that represent space as an array have become one of the means of managing spatial data. Each cell in an array tends to interact with one another; therefore, instead of considering a single cell, considering a concept of subarray is required in some applications. In addition, each cell has several attribute values to indicate its features. Based on the two observations, we propose a new type of query, subarray skyline, that provides a way to find meaningful subarrays or filter less meaningful subarrays considering attributes. We also introduce an efficient query processing method, ReSKY, in centralized and distributed settings. Through extensive experiments using an array database and real datasets, we show that ReSKY has better performance than the existing techniques.

Unprecedented amounts of multidimensional array data are currently being generated in many fields. These multidimensional array data naturally and efficiently fit into the array data model, and many array management systems based on the array data model have appeared. Accordingly, the requirement for data exploration methods for large multidimensional array data has also increased. In this paper, we propose a method for efficient top- k subarray query processing in array databases, which is one of the most important query types for exploring multidimensional data. First, we define novel top- k query models for array databases: overlap-allowing and disjoint top- k subarray queries. Second, we propose a suite of top- k subarray query processing methods, called PPTS and extend them to distributed processing. Finally, we present the results of extensive experiments using real datasets from an array database, which show that our proposed methods outperform existing naïve methods.