D-ToSS: A Distributed Throwaway Spatial Index Structure for Dynamic Location Data

Many applications deal with moving object datasets, e.g., mobile phone social networking, scientific simulations, and ride-sharing services. These applications need to handle a tremendous number of spatial objects that continuously move and execute spatial queries to explore their surroundings. To manage such update-heavy workloads, several throwaway index structures have recently been proposed, where a static index is rebuilt periodically from scratch rather than updated incrementally. It has been shown that throwaway indices outperform specialized moving-object indices that maintain location updates incrementally. However, throwaway indices suffer from scalability due to their single-server design and the only distributed throwaway index (D-MOVIES), extension of a centralized approach, does not scale out as the number of servers increases, especially during query processing phase. We propose a distributed throwaway spatial index structure (D-ToSS) that not only scales out to multiple servers by using an intelligent partitioning technique but also scales up since it fully exploits the multi-core CPUs available on each server. D-ToSS rapidly constructs a Voronoi Diagram, which has a flat structure making it a perfect fit for parallel processing. For example, we experimentally show a 25 x speedup in query processing compared to D-MOVIES and this gap gets larger as the number of servers increases.