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

doi:10.1145/3459637.3481897

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 proceeding › Conference contribution › peer-review

15 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 language	English
Title of host publication	CIKM 2021 - Proceedings of the 30th ACM International Conference on Information and Knowledge Management
Pages	3796-3807
Number of pages	12
ISBN (Electronic)	9781450384469
DOIs	https://doi.org/10.1145/3459637.3481897
State	Published - Oct 26 2021
Event	30th ACM International Conference on Information and Knowledge Management, CIKM 2021 - Virtual, Online, Australia Duration: Nov 1 2021 → Nov 5 2021

Publication series

Name	International Conference on Information and Knowledge Management, Proceedings

Conference

Conference	30th ACM International Conference on Information and Knowledge Management, CIKM 2021
Country/Territory	Australia
City	Virtual, Online
Period	11/1/21 → 11/5/21

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

Access to Document

10.1145/3459637.3481897

Cite this

Eldawy, A., Hristidis, V., Ghosh, S., Saeedan, M., Sevim, A., Siddique, A. B., Singla, S., Sivaram, G., Vu, T., & Zhang, Y. (2021). Beast: Scalable Exploratory Analytics on Spatiooral Data. In CIKM 2021 - Proceedings of the 30th ACM International Conference on Information and Knowledge Management (pp. 3796-3807). (International Conference on Information and Knowledge Management, Proceedings). https://doi.org/10.1145/3459637.3481897

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Eldawy, A, Hristidis, V, Ghosh, S, Saeedan, M, Sevim, A, Siddique, AB, Singla, S, Sivaram, G, Vu, T & Zhang, Y 2021, Beast: Scalable Exploratory Analytics on Spatiooral Data. in CIKM 2021 - Proceedings of the 30th ACM International Conference on Information and Knowledge Management. International Conference on Information and Knowledge Management, Proceedings, pp. 3796-3807, 30th ACM International Conference on Information and Knowledge Management, CIKM 2021, Virtual, Online, Australia, 11/1/21. https://doi.org/10.1145/3459637.3481897

Beast: Scalable Exploratory Analytics on Spatiooral Data. / Eldawy, Ahmed; Hristidis, Vagelis; Ghosh, Saheli et al.
CIKM 2021 - Proceedings of the 30th ACM International Conference on Information and Knowledge Management. 2021. p. 3796-3807 (International Conference on Information and Knowledge Management, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Siddique, A. B.

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Beast: Scalable Exploratory Analytics on Spatiooral Data

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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