用于可视化分析的时空来源数据聚合

R. Splechtna, Silvana Podaras, Michael Beham, D. Gračanin, K. Matkovič
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引用次数: 0

摘要

我们描述了我们对2017年VAST挑战赛Mini-Challenge 2数据的分析方法。这项挑战涉及空气采样站的读数。为了回答主要问题,即在采样站测量的化学物质的来源,我们通过聚合时空来源数据扩展了提供的数据集。这些数据是根据所提供的气象数据和位置图生成的,并将其作为粒子示踪器的输入,该示踪器计算从排放者(工厂)到达收集器(采样站的位置)的粒子的来源。我们使用ComVis[3],一个协调的多视图(CMV)系统,通过应用以传感器为中心的数据模型来分析整个数据集(提供和生成的数据)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MC2 — Spatio-Temporal Provenance Data Aggregation for Visual Analysis
We describe our approach to the analysis of 2017 VAST Challenge Mini-Challenge 2 data. The challenge deals with readings from air sampler stations. To answer the main question, the provenance of the chemicals measured at the sampler stations, we extend the provided data set by aggregated spatio-temporal provenance data. This data is generated from the provided meteorological data and locations map by using it as input for a particle tracer which calculates the provenance of the particles arriving from the emitters (factories) at the collectors (the locations of sampler stations). We use ComVis [3], a coordinated multiple views (CMV) system, to analyze the whole data set (the provided and generated data) by applying a sensor centric data model.
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