Beast: Scalable Exploratory Analytics on Spatio-temporal Data
Beast: Scalable Exploratory Analytics on Spatio-temporal Data
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DOI：
10.1145/3459637.3481897
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发表时间：
2021-10
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通讯作者：
Ahmed Eldawy;Vagelis Hristidis;Saheli Ghosh;Majid Saeedan;Akil Sevim;A.B. Siddique;Samriddhi Singla;Ganeshram Sivaram;Tin Vu;Yaming Zhang
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作者：
Ahmed Eldawy;Vagelis Hristidis;Saheli Ghosh;Majid Saeedan;Akil Sevim;A.B. Siddique;Samriddhi Singla;Ganeshram Sivaram;Tin Vu;Yaming Zhang
本文介绍了面向时空大数据的可扩展探索性数据科学的开源Beast系统。Beast基于成熟的研究，已发布以帮助研究界分析大数据的时空数据。Beast提供了一组可扩展的组件，这些组件与Spark自然集成，以构建探索性数据科学管道。Beast可以在不到一分钟的时间内安装到现有的Spark集群上，并提供广泛的功能，包括加载以标准文件格式表示的矢量和栅格数据、用于基准测试的合成数据生成、负载平衡空间分区、数据汇总、交互式可视化等。Beast建立在几个研究项目的基础上；它的目标是让研究人员在一个整合和连贯的系统中广泛使用所有这些研究。
This paper introduces the open-source Beast system for scalable exploratory data science on big spatio-temporal data. Beast is based on well-established research and has been released to assist the research community with analyzing big spatio-temporal data. Beast provides a set of extensible components that naturally integrate with Spark to build exploratory data science pipelines. Beast can install in less than a minute on an existing Spark cluster and provides a wide array of features including loading vector and raster data represented in standard file formats, synthetic data generation for benchmarking, load-balanced spatial partitioning, data summarization, interactive visualization, and more. Beast builds on several research projects; its goal is to make all this research widely available to researchers in one integrative and coherent system.