The COVID-19 pandemic has spread all over the globe. In the absence of a vaccine, a small number of countries have managed to control the diffusion of viruses by early detection and early quarantine. South Korea, one of the countries which have kept the epidemics well-controlled, has opened the infected patients’ trajectory to the public. Such a reaction has been regarded as an effective method, however, serious privacy breach cases have been issued in South Korea. Furthermore, some suspected contacts have refused to take infection tests because they are afraid of being exposed. To solve this problem, we propose a privacy-preserving contact tracing method based on spatio-temporal trajectory which can be practically used in many quarantine systems. In addition, we develop a system to visualize the contact tracing workflow.
In recent times, the production of multidimensional data in various domains and their storage in array databases has witnessed a sharp increase; this rapid growth in data volumes necessitates compression in array databases. However, existing compression schemes used in array databases are general-purpose and not designed specifically for the databases. They could degrade query performance with complex analytical tasks, which incur huge computing costs. Thus, a compression scheme that considers the workflow of array databases is required. This study presents a compression scheme, SEACOW, for storing and querying multidimensional array data. The scheme is specially designed to be efficient for both dimension-based and value-based exploration. It considers data access patterns for exploration queries and embeds a synopsis, which can be utilized as an index, in the compressed array. In addition, we implement an array storage system, namely MSDB, to perform experiments. We evaluate query performance on real scientific datasets and compared it with those of existing compression schemes. Finally, our experiments demonstrate that SEACOW provides high compression rates compared to existing compression schemes, and the synopsis improves analytical query processing 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.
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