Please use this identifier to cite or link to this item: https://idr.nitk.ac.in/jspui/handle/123456789/7431
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dc.contributor.authorKnoblock, C.A.
dc.contributor.authorJoshi, A.R.
dc.contributor.authorMegotia, A.
dc.contributor.authorPham, M.
dc.contributor.authorUrsaner, C.
dc.date.accessioned2020-03-30T09:59:05Z-
dc.date.available2020-03-30T09:59:05Z-
dc.date.issued2017
dc.identifier.citationProceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, UrbanGIS 2017, 2017, Vol.2017-January, , pp.-en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/7431-
dc.description.abstractOrganizations are awash in data. In many cases, they do not know what data exists within the organization and much information is not available when needed, or worse, information gets recreated from other sources. In this paper, we present an automatic approach to spatio-temporal indexing of the datasets within an organization. The indexing process automatically identifies the spatial and temporal fields, normalizes and cleans those fields, and then loads them into a big data store where the information can be efficiently searched, queried, and analyzed. We evaluated our approach on 600 datasets published by the City of Los Angeles and show that we can automatically process their data and can efficiently access and analyze the indexed data. � 2017 Copyright held by the owner/author(s).en_US
dc.titleAutomatic spatio-temporal indexing to integrate and analyze the data of an organizationen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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