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.

With the rise of mobile technologies (e.g., smart phones, wearable technologies) and location-aware Internet browsers, a massive amount of spatial data is being collected since such tools allow users to geo-tag user content (e.g., photos, tweets). Meanwhile, cloud computing providers such as Amazon and Microsoft allow users to lease computing resources where users are charged based on the amount of time they reserve each server, with no consideration of utilization. One key factor that affects server utilization is partitioning method especially in data-driven location-based services. Because if the data partitions are not accessed, the servers storing them remain idle but the user is still charged. Whereas, existing spatial data partitioning techniques aim to 1) cluster spatially close data objects to minimize disk I/O and 2) create equi-sized partitions. On the contrary, the objective is different for cloud given the current pricing models. In this paper, we propose a novel cost-efficient partitioning method for spatial data where an increase in the servers' utilizations yields less number of servers to support the same workload, thus saving cost. Extensive experiments on Amazon EC2 infrastructure demonstrate that our approach is efficient and reduces the cost by up to 40%.

The widespread use of GPS-enabled devices have led to a number of emerging applications that require monitoring and querying a large number of moving objects, such as in location-based services, mobile phone social networking, UAV surveillance, and car navigation systems. In such applications, indexes for moving objects must support queries efficiently and also cope with frequent updates. In this paper, we propose a cloud-based throwaway index structure, dubbed ToSS-it, where we generate the index from scratch in a short period of time rather than updating it with every location change of the moving objects. ToSS-it employs inter-node and intra-node multi-core parallelism paradigm to rapidly construct a distributed Voronoi Diagram. ToSS-it scales out by using a voronoi partitioning technique that minimizes the network message exchanges between the nodes (i.e., The major overhead in parallel generation of Voronoi Diagrams), and scales up since it fully exploits the multi-core CPUs available on each server. As a comparison point, with the state-of-the-art cloud-based spatial index structure (RT-CAN), if at least 7% of the objects are moving and issue updates to the index, it is faster to recreate ToSS-it from scratch than updating RT-CAN.

Geospatial queries (GQ) have been used in a wide variety of applications such as decision support systems, profile-based marketing, bioinformatics and GIS. Most of the existing query-answering approaches assume centralized processing on a single machine although GQs are intrinsically parallelizable. There are some approaches that have been designed for parallel databases and cluster systems, however, these only apply to the systems with limited parallel processing capability, far from that of the cloud-based platforms. In this paper, we study the problem of parallel geospatial query processing with the MapReduce programming model. Our proposed approach creates a spatial index, Voronoi diagram, for given data points in 2D space and enables efficient processing of a wide range of GQs. We evaluated the performance of our proposed techniques and correspondingly compared them with their closest related work while varying the number of employed nodes.

Microsoft SQL Server spatial libraries contain several components that handle geometrical and geographical data types. With advances in geo-sensing technologies, there has been an increasing demand for geospatial streaming applications. Microsoft SQL Server StreamInsight ( StreamInsight , for brevity) is a platform for developing and deploying streaming applications that run continuous queries over high-rate streaming events. With its extensibility infrastructure, StreamInsight enables developers to integrate their domain expertise within the query pipeline in the form of user defined modules. This demo utilizes the extensibility infrastructure in Microsoft StreamInsight to leverage its continuous query processing capabilities in two directions. The first direction integrates SQL spatial libraries into the continuous query pipeline of StreamInsight. StreamInsight provides a well-defined temporal model over incoming events while SQL spatial libraries cover the spatial properties of events to deliver a solution for spatiotemporal stream query processing. The second direction extends the system with an analytical refinement and prediction layer. This layer analyzes historical data that has been accumulated and summarized over the years to refine, smooth and adjust the current query output as well as predict the output in the near future. The demo scenario is based on transportation data in Los Angeles County.