Scalable Spatio-temporal Indexing and Querying over a Document-oriented NoSQL Store

Abstract

In this paper, we provide an in-depth study of the performance of spatio-temporal queries in document-oriented NoSQL stores. Existing NoSQL stores provide limited support for spatial data and (quite often) no native support for spatio-temporal data. As a result, the performance of query execution over large collections of spatio-temporal data is often suboptimal. We present an approach for indexing spatio-temporal data, which is applicable to any NoSQL store that provides key-based access to data without modifications to its code, and we show how to generate data partitions that preserve data locality. Moreover, we show the impact of indexing and partitioning on the number of cluster nodes that serve a query, and we discuss the advantages and disadvantages for different applications. We adopt a methodology for the evaluation of spatio-temporal range queries, which can serve as a benchmark. In our experiments, we focus on MongoDB (as a representative NoSQL store that provides spatial support) and we study the impact of indexing spatio-temporal data on performance, using both real-life and synthetic data sets in a medium-sized cluster.