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Smart city article details

Title	Big Spatio-Temporal Network Data Analytics For Smart Cities: Research Needs
ID_Doc	12159
Authors	Gunturi V.M.V.; Shekhar S.
Year	2017
Published	Springer Geography
DOI	http://dx.doi.org/10.1007/978-3-319-40902-3_8
Abstract	Increasingly, location-aware sensors in urban transportation networks are generating a wide variety of data which has spatio-temporal network semantics. Examples include temporally detailed roadmaps, GPS tracks, traffic signal timings, and vehicle measurements. These datasets, which we collectively call Big Spatio-Temporal Network (BSTN) Data, have value addition potential for several smart-city use-cases including navigation services which recommend eco-friendly routes. However, BSTN data pose significant computational challenges regarding the assumptions of the current state-of-the-art analytic-techniques used in these services. This article attempts to put forth some potential research directions towards addressing the challenges of scalable analytics on BSTN data. Two kinds of BSTN data are considered here, viz., the vehicle measurement big data and the travel-time big data. © 2017, Springer International Publishing Switzerland.
Author Keywords	Road networks; Routing; Spatial data mining; Spatial databases; Spatio-temporal networks

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