Egemen Tanin
ABSTRACT
Counts of objects are important for big data analytics. However, spatial objects do not work well with counts. We present the latest developments on distinct counting problem. In particular, we explain Euler Histograms, which are a category of spatial data structures that address the distinct counting challenges. Euler histograms support traditional counting queries as well as other query types.
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Digital Library
- M. Fanaeepour, L. Kulik, E. Tanin, and B. I. Rubinstein. The CASE histogram: privacy-aware processing of trajectory data using aggregates. GeoInformatica, 2015. To appear.Google ScholarDigital Library
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- H. Xie, L. Kulik, and E. Tanin. Privacy-aware traffic monitoring. IEEE TITS, 11(1):61--70, 2010. Google ScholarDigital Library
- H. Xie, E. Tanin, and L. Kulik. Distributed histograms for processing aggregate data from moving objects. In IEEE MDM, Mannheim, Germany, 2007. Google ScholarDigital Library
- H. Xie, E. Tanin, L. Kulik, P. Scheuermann, G. Trajcevski, and M. Fanaeepour. Euler histogram tree: a spatial data structure for aggregate range queries on vehicle trajectories. In ACM SIGSPATIAL IWCTS, pages 18--24, Dallas, TX, 2014. Google ScholarDigital Library
Index Terms
- Count or Not to Count: Counting Challenges for Big Spatial Data Analytics
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