Beast: Scalable Exploratory Analytics on Spatiooral Data

Ahmed Eldawy, Vagelis Hristidis, Saheli Ghosh, Majid Saeedan, Akil Sevim, A. B. Siddique, Samriddhi Singla, Ganesh Sivaram, Tin Vu, Yaming Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

This paper introduces the open-source Beast system for scalable exploratory data science on big spatiooral data. Beast is based on well-established research and has been released to assist the research community with analyzing big spatiooral 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.

Original languageEnglish
Title of host publicationCIKM 2021 - Proceedings of the 30th ACM International Conference on Information and Knowledge Management
Pages3796-3807
Number of pages12
ISBN (Electronic)9781450384469
DOIs
StatePublished - Oct 26 2021
Event30th ACM International Conference on Information and Knowledge Management, CIKM 2021 - Virtual, Online, Australia
Duration: Nov 1 2021Nov 5 2021

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference30th ACM International Conference on Information and Knowledge Management, CIKM 2021
Country/TerritoryAustralia
CityVirtual, Online
Period11/1/2111/5/21

Bibliographical note

Publisher Copyright:
© 2021 Owner/Author.

Keywords

  • data science
  • exploration
  • geospatial data
  • spatiooral data
  • visualization

ASJC Scopus subject areas

  • General Business, Management and Accounting
  • General Decision Sciences

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