A B S T R A C T
With the increasing availability of locating and navigation technologies on portable wireless
devices, huge amounts of location data are being captured at ever growing rates. Spatial and
temporal aggregations in an Online Analytical Processing (OLAP) setting for the large-scale
ubiquitous urban sensing data play an important role in understanding urban dynamics and
facilitating decision making. Unfortunately, existing spatial, temporal and spatiotemporal OLAP
techniques are mostly based on traditional computing frameworks, i.e., disk-resident systems
on uniprocessors based on serial algorithms, which makes them incapable of handling largescale
data on parallel hardware architectures that have already been equipped with commodity
computers. In this study, we report our designs, implementations and experiments on
developing a data management platform and a set of parallel techniques to support highperformance
online spatial and temporal aggregations on multi-core CPUs and many-core
Graphics Processing Units (GPUs). Our experiment results show that we are able to spatially
associate nearly 170 million taxi pickup location points with their nearest street segments
among 147,011 candidates in about 5–25s on both an Nvidia Quadro 6000 GPU device and
dual Intel Xeon E5405 quad-core CPUs when their Vector Processing Units (VPUs) are utilized
for computing intensive tasks. After spatially associating points with road segments, spatial,
temporal and spatiotemporal aggregations are reduced to relational aggregations and can be
processed in the order of a fraction of a second on both GPUs and multi-core CPUs. In addition
to demonstrating the feasibility of building a high-performance OLAP system for processing
large-scale taxi trip data for real-time, interactive data explorations, our work also opens the
paths to achieving even higher OLAP query efficiency for large-scale applications through
integrating domain-specific data management platforms, novel parallel data structures and
algorithm designs, and hardware architecture friendly implementations.
& 2014 Elsevier Ltd. All rights reserved.
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کد محصول : شماره 112
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