The visual analysis of large multidimensional spatiotemporal datasets poses challenging questions regarding storage requirements and query performance. Several data structures have recently been proposed to address these problems that rely on indexes that pre-compute different aggregations from a known-a-priori dataset. Consider now the problem of handling streaming datasets, in which data arrive as one or more continuous data streams. Such datasets introduce challenges to the data structure, which now has to support dynamic updates (insertions/deletions) and rebalancing operations to perform selfreorganizations. In this work, we present the Packed-Memory Quadtree (PMQ), a novel data structure designed to support visual exploration of streaming spatiotemporal datasets. PMQ is cache-oblivious to perform well under different cache configurations. We store streaming data in an internal index that keeps a spatiotemporal ordering over the data following a quadtree representation, with support for real-time insertions and deletions. We validate our data structure under different dynamic scenarios and compare to competing strategies. We demonstrate how PMQ could be used to answer different types of visual spatiotemporal range queries of streaming datasets.
Spatial hashing is an efficient technique to speed up proximity queries on moving objects in the space domain, suitable for computer entertainment applications and simulations. This paper presents an efficient three-step algorithm for building a 1D hash table for spatial hashing needed to perform fast queries on objects for location and proximity detection. In contrast to existing solutions, this algorithm uses fixed-size vectors and pivots instead of dynamic data structures to deal with collisions in the hash table. This also enables iterating through entities and performing proximity queries in a linear memory. Experiments conducted shows that the proposed algorithm is, on average, at least 3 times faster than existing solutions based on dynamic data structures. This contributes to realizing interactive frame rates with massive number of moving entities.
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