In recent years, spatial-keyword queries have attracted much attention with the fast development of location-based services. However, current spatial-keyword techniques are disk-based, which cannot fulfill the requirements of high throughput and low response time. With the surging data size, people tend to process data in distributed in-memory environments to achieve low latency. In this paper, we present the distributed solution, i.e., Skia (Spatial-Keyword In-memory Analytics), to provide a scalable backend for spatial-textual analytics. Skia introduces a two-level index framework for big spatial-textual data including: (1) efficient and scalable global index, which prunes the candidate partitions a lot while achieving small space budget; and (2) four novel local indexes, that further support low latency services for exact and approximate spatial-keyword queries. Skia can support common spatial-keyword queries via traditional SQL programming interfaces. The experiments conducted on large-scale real datasets have demonstrated the promising performance of the proposed indexes and our distributed solution.

中文翻译：

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Skia：用于大空间文本数据的可扩展且高效的内存分析

近年来，随着基于位置的服务的快速发展，空间关键字查询引起了广泛关注。然而，目前的空间关键字技术是基于磁盘的，不能满足高吞吐量和低响应时间的要求。随着数据量的激增，人们倾向于在分布式内存环境中处理数据以实现低延迟。在本文中，我们提出了分布式解决方案，即 Skia（空间关键字内存分析），为空间文本分析提供可扩展的后端。Skia 为大空间文本数据引入了两级索引框架，包括： (1) 高效且可扩展的全局索引，在实现小空间预算的同时大量​​修剪候选分区；(2) 四个新的地方指标，进一步支持精确和近似空间关键字查询的低延迟服务。Skia 可以通过传统的 SQL 编程接口支持常见的空间关键字查询。在大规模真实数据集上进行的实验证明了所提出的索引和我们的分布式解决方案的良好性能。