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Given the increasing prevalence of streaming spatially-referenced datasets resulting from sensor networks usually consisting of text objects of varying length (termed labels) as well as streaming spatially oriented queries leads to closer scrutiny of mapping interfaces to present the data to users. These interfaces must cope with the fact that the labels associated with each location are constantly changing and that there are too many objects to display clearly within the interface. An algorithm meeting these challenges is presented. It differs from classical methods by avoiding expensive pre-computation steps, thereby allowing different labels to be associated with locations without needing to completely recompute the layout. In other words, we are addressing a write-many read-many setting instead of the conventional write-once read-many setting. Our experiments show consistent sub-second query times for query windows that contain as many as 11 million data objects, with only slight differences in the set of displayed labels when compared to an exhaustive baseline algorithm. This enables the algorithm to be used in a mapping application that involves both streaming data and streaming queries such as windowing realized by real-time, continuous zooming and panning operations.

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Large analytic applications on road networks including simulations, logistics, location-based advertisement, and transportation planning require shortest distance/time methods that provide high throughput (i.e., distance/time computations per second). Our previous work discussed how to process graph distance computations in a PostgreSQL database on a large road network, e.g., 60K distance computations per second per machine, how to "scale out" by using a Spark cluster to achieve 73.8K distance computations per second per machine, and how to obtain a extremely high-throughput solution in memory for city-sized road networks, e.g., 6.7M distance computations per second. However, there is no solution that could achieve more than 1M throughput for large road networks. In an industrial setting, most state-of-the-art solutions yield 5K − 10K shortest distance computations per second per machine even with multi-threads. In this paper, we propose a new distance oracle system (DOS) for large road networks. It can solve most spatial analytic queries, and its throughput achieves 5M distance computations per second even on the whole USA road network. For example, a 10K × 10K origin-distance (OD) matrix can be computed in 20 seconds.

Spatial analytical queries on road networks typically perform hundreds of thousands to several millions of shortest distance computations in the process of producing results. These queries require architectures that can compute a large number of network distances. Two architectures are evaluated on a variety of spatial analytical queries on road networks. The first architecture is a widely used hybrid architecture that uses a database to store spatial datasets, a road network distance computing module, and an analysis tool to tie them together into a single query processing pipeline. The second architecture uses of a distance oracle representation of a road network. This architecture stores the spatial datasets and the distance oracle inside the database, and the query processing is completely handled by the database. A detailed evaluation of the two architectures for a variety of analytical query processing tasks such as region, KNN, distance matrix and trajectory queries is presented and the lessons learned are discussed.