Scipediacontent: Scipediacontent moved page Draft Content 339375064 to Nikitopoulos et al 2019a

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Scipediacontent moved page Draft Content 339375064 to Nikitopoulos et al 2019a

Scipediacontent: Created page with " == Abstract == Hot spot analysis is the problem of identifying statistically significant spatial clusters from an underlying data set. In this paper, we study the problem of..."

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Created page with " == Abstract == Hot spot analysis is the problem of identifying statistically significant spatial clusters from an underlying data set. In this paper, we study the problem of..."

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== Abstract ==

Hot spot analysis is the problem of identifying statistically significant spatial clusters from an underlying data set. In this paper, we study the problem of hot spot analysis for massive trajectory data of moving objects, which has many real-life applications in different domains, especially in the analysis of vast repositories of historical traces of spatio-temporal data (cars, vessels, aircrafts). In order to identify hot spots, we propose an approach that relies on the Getis-Ord statistic, which has been used successfully in the past for point data. Since trajectory data is more than just a collection of individual points, we formulate the problem of trajectory hot spot analysis, using the Getis-Ord statistic. We propose a parallel and scalable algorithm for this problem, called THS, which provides an exact solution and can operate on vast-sized data sets. Moreover, we introduce an approximate algorithm (aTHS) that avoids exhaustive computation and trades-off accuracy for efficiency in a controlled manner. In essence, we provide a method that quantifies the maximum induced error in the approximation, in relation with the achieved computational savings. We develop our algorithms in Apache Spark and demonstrate the scalability and efficiency of our approach using a large, historical, real-life trajectory data set of vessels sailing in the Eastern Mediterranean for a period of three years.

Document type: Conference object

== Full document ==
<pdf>Media:Draft_Content_339375064-beopen6469-8013-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [http://dx.doi.org/10.1109/bigdata.2018.8622376 http://dx.doi.org/10.1109/bigdata.2018.8622376] under the license https://creativecommons.org/licenses/by

* [https://zenodo.org/record/2730650 https://zenodo.org/record/2730650]

* [https://zenodo.org/record/2730650/files/BIG%20DATA%20Nikitopoulos.pdf https://zenodo.org/record/2730650/files/BIG%20DATA%20Nikitopoulos.pdf] under the license http://creativecommons.org/licenses/by/4.0/legalcode

* [http://xplorestaging.ieee.org/ielx7/8610059/8621858/08622376.pdf?arnumber=8622376 http://xplorestaging.ieee.org/ielx7/8610059/8621858/08622376.pdf?arnumber=8622376],
: [http://dx.doi.org/10.1109/bigdata.2018.8622376 http://dx.doi.org/10.1109/bigdata.2018.8622376] under the license cc-by

* [https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2018.html#NikitopoulosPDP18 https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2018.html#NikitopoulosPDP18],
: [https://academic.microsoft.com/#/detail/2912458136 https://academic.microsoft.com/#/detail/2912458136]

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Scipediacontent: Scipediacontent moved page Draft Content 339375064 to Nikitopoulos et al 2019a

Scipediacontent: Created page with " == Abstract == Hot spot analysis is the problem of identifying statistically significant spatial clusters from an underlying data set. In this paper, we study the problem of..."