Computation of PDFs on Big Spatial Data: Problem & Architecture

Ji Liu (1) , Noel Lemus (2) , Esther Pacitti (1) , Fábio Porto (2) , Patrick Valduriez (1)

1 ZENITH - Scientific Data Management
2 LNCC / MCT - Laboratorio Nacional de Computação Cientifica [Rio de Janeiro]

Big spatial data can be produced by observation or numerical simulation programs and correspond to points that represent a 3D soil cube area. However, errors in signal processing and modeling create some uncertainty, and thus a lack of accuracy in identifying geological or seismic phenomenons. To analyze uncertainty, the main solution is to compute a Probability Density Function (PDF) of each point in the spatial cube area, which can be very time consuming. In this paper, we analyze the problem and discuss the use of Spark to efficiently compute PDFs.

Domaines: Base de données [cs.DB]; Calcul parallèle, distribué et partagé [cs.DC]

Ji Liu, Noel Lemus, Esther Pacitti, Fábio Porto, Patrick Valduriez. Computation of PDFs on Big Spatial Data: Problem & Architecture. Latin America Data Science Workshop (LADaS 2018), Aug 2018, Rio de Janeiro, Brazil. pp.80-83. ⟨lirmm-01867758⟩