dc.contributorFGV
dc.creatorMoussalli, Roger
dc.creatorAbsalyamov, Ildar
dc.creatorVieira, Marcos R.
dc.creatorNajjar, Walid
dc.creatorTsotras, Vassilis J.
dc.date.accessioned2018-05-10T13:36:46Z
dc.date.accessioned2019-05-22T14:04:20Z
dc.date.available2018-05-10T13:36:46Z
dc.date.available2019-05-22T14:04:20Z
dc.date.created2018-05-10T13:36:46Z
dc.date.issued2015-04
dc.identifier1384-6175
dc.identifierhttp://hdl.handle.net/10438/23459
dc.identifier10.1007/s10707-014-0217-3
dc.identifier000351538800008
dc.identifierNajjar, Walid/0000-0001-6761-6801
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/2689786
dc.description.abstractThe wide and increasing availability of collected data in the form of trajectories has led to research advances in behavioral aspects of the monitored subjects (e.g., wild animals, people, and vehicles). Using trajectory data harvested by devices, such as GPS, RFID and mobile devices, complex pattern queries can be posed to select trajectories based on specific events of interest. In this paper, we present a study on FPGA- and GPU-based architectures processing complex patterns on streams of spatio-temporal data. Complex patterns are described as regular expressions over a spatial alphabet that can be implicitly or explicitly anchored to the time domain. More importantly, variables can be used to substantially enhance the flexibility and expressive power of pattern queries. Here we explore the challenges in handling several constructs of the assumed pattern query language, with a study on the trade-offs between expressiveness, scalability and matching accuracy. We show an extensive performance evaluation where FPGA and GPU setups outperform the current state-of-the-art (single-threaded) CPU-based approaches, by over three orders of magnitude for FPGAs (for expressive queries) and up to two orders of magnitude for certain datasets on GPUs (and in some cases slowdown). Unlike software-based approaches, the performance of the proposed FPGA and GPU solutions is only minimally affected by the increased pattern complexity.
dc.languageeng
dc.publisherSpringer
dc.relationGeoinformatica
dc.rightsrestrictedAccess
dc.sourceWeb of Science
dc.subjectSpatio-temporal
dc.subjectSpatial
dc.subjectTemporal
dc.subjectDatabase
dc.subjectFPGA
dc.subjectGPU
dc.subjectAcceleration
dc.subjectPattern
dc.subjectMatching
dc.titleHigh performance FPGA and GPU complex pattern matching over spatio-temporal streams
dc.typeArticle (Journal/Review)


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