International Journal of Data Warehousing and Mining · 2014 · 39 citations · 18 references
ComputingGeographyGeoinformaticsBig Data AnalyticsData MiningGeomaticsSpatial Data AnalyticsSpatial Data MiningSpatial DatabasesSpatial Knowledge RepresentationGeospatial Big DataGeospatial Big Data AnalyticsGeospatial Big Data ProcessingGeospatial Data MiningSpatial Knowledge DiscoverySpatial Pattern Discovery
Spatial big data mining offers opportunities and challenges, attracting attention from academia, industry, and governments to address pressing social, economic, and environmental issues while confronting pollution and data utilization difficulties. The paper presents spatial big data mining under the characteristics of geomatics and big data. It integrates geomatics principles with big data techniques. Spatial big data mining enables population‑level insights and transforms discovered knowledge into actionable data intelligence.
Big data brings the opportunities and challenges into spatial data mining. In this paper, spatial big data mining is presented under the characteristics of geomatics and big data. First, spatial big data attracts much attention from the academic community, business industry, and administrative governments, for it is playing a primary role in addressing social, economic, and environmental issues of pressing importance. Second, humanity is submerged by spatial big data, such as much garbage, heavy pollution and its difficulties in utilization. Third, the value in spatial big data is dissected. As one of the fundamental resources, it may help people to recognize the world with population instead of sample, along with the potential effectiveness. Finally, knowledge discovery from spatial big data refers to the basic technologies to realize the value of big data, and relocate data assets. And the uncovered knowledge may be further transformed into data intelligences.
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Detecting Novel Associations in Large Data Sets
David N. Reshef, Yakir Reshef, Hilary K. Finucane et al. · Science · 2011 · 3.4K citations
Xindong Wu, Xingquan Zhu, Gongqing Wu et al. · IEEE Transactions on Knowledge and Data Engineering · 2013 · 2.4K citations