Abstract
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.
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