Antares: A Scalable, Real-Time, Fault Tolerant Data Store for Spatial Analysis

Abstract

The growth of mobile devices has significantly increased the velocity and volume of location-based data. Whilst there is enormous potential for applications that exploit this data in real-time, storing and querying it in real-time creates significant challenges. Traditional RDBMS systems are not sufficiently scalable, while typical cloud-based solutions such as map-reduce do not possess the capabilities required for real-time, spatial-data processing. Therefore, new approaches are needed. In this paper we explore the use of NoSQL technologies. These offer scalability, availability and fault tolerance, but -- as we show -- do not perform well with spatial data. Therefore, in this paper we address this challenge by enhancing existing spatial indexing structures with novel algorithms for inserting and searching spatial data. We have implemented this in a NoSQL solution (Antares), and evaluated it against two other NoSQL solutions, and a range of indexing structures: Kd-Tree, Quad Tree and Geohashing. The results show that Antares significantly outperforms the other approaches.