Rheemstudio: Cross-platform data analytics made easy

Ji Lucas, Yasser Idris, Bertty Contreras-Rojas, Jorge Arnulfo Quiane Ruiz, Sanjay Chawla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Many of today's applications need several data processing platforms for complex analytics. Thus, recent systems have taken steps towards supporting cross-platform data analytics. Yet, current cross-platform systems lack of ease-of-use, which is crucial for their adoption. This demo presents RheemStudio, a visual IDE on top of Rheem. It allows users to easily specify their cross-platform data analytic tasks. In this demo, we will demonstrate five main features of RheemStudio: drag-And-drop, declarative, interactive, and customized specification of data analytic tasks as well as easy monitoring of tasks. With this in mind, we will consider two real use cases, one from the machine learning world and the second one based on data discovery. During all the demo, the audience will be able to take part and create their own data analytic tasks too.

Original languageEnglish
Title of host publicationProceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1553-1556
Number of pages4
ISBN (Electronic)9781538655207
DOIs
Publication statusPublished - 24 Oct 2018
Event34th IEEE International Conference on Data Engineering, ICDE 2018 - Paris, France
Duration: 16 Apr 201819 Apr 2018

Other

Other34th IEEE International Conference on Data Engineering, ICDE 2018
CountryFrance
CityParis
Period16/4/1819/4/18

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Keywords

  • Cross platform data processing
  • Data Analytics
  • Query Processing

ASJC Scopus subject areas

  • Information Systems
  • Information Systems and Management
  • Hardware and Architecture

Cite this

Lucas, J., Idris, Y., Contreras-Rojas, B., Quiane Ruiz, J. A., & Chawla, S. (2018). Rheemstudio: Cross-platform data analytics made easy. In Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018 (pp. 1553-1556). [8509400] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDE.2018.00179