Road to freedom in big data analytics

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

16 Citations (Scopus)

Abstract

The world is fast moving towards a data-driven society where data is the most valuable asset. Organizations need to perform very diverse analytic tasks using various data processing platforms. In doing so, they face many challenges; chiefly, platform dependence, poor interoperability, and poor performance when using multiple platforms. We present Rheem, our vision for big data analytics over diverse data processing platforms. Rheem provides a threelayer data processing and storage abstraction to achieve both platform independence and interoperability across multiple platforms. In this paper, we discuss our vision as well as present multiple research challenges that we need to address to achieve it. As a case in point, we present a data cleaning application built using some of the ideas of Rheem. We show how it achieves platform independence and the performance benefits of following such an approach.

Original languageEnglish
Title of host publicationAdvances in Database Technology - EDBT 2016
Subtitle of host publication19th International Conference on Extending Database Technology, Proceedings
PublisherOpenProceedings.org
Pages479-484
Number of pages6
Volume2016-March
ISBN (Electronic)9783893180707
DOIs
Publication statusPublished - 1 Jan 2016
Event19th International Conference on Extending Database Technology, EDBT 2016 - Bordeaux, France
Duration: 15 Mar 201618 Mar 2016

Other

Other19th International Conference on Extending Database Technology, EDBT 2016
CountryFrance
CityBordeaux
Period15/3/1618/3/16

Fingerprint

Interoperability
Cleaning
Big data

ASJC Scopus subject areas

  • Information Systems
  • Software
  • Computer Science Applications

Cite this

Agrawal, D., Chawla, S., Elmagarmid, A., Kaoudi, Z., Ouzzani, M., Papotti, P., ... Zaki, M. J. (2016). Road to freedom in big data analytics. In Advances in Database Technology - EDBT 2016: 19th International Conference on Extending Database Technology, Proceedings (Vol. 2016-March, pp. 479-484). OpenProceedings.org. https://doi.org/10.5441/002/edbt.2016.45

Road to freedom in big data analytics. / Agrawal, Divy; Chawla, Sanjay; Elmagarmid, Ahmed; Kaoudi, Zoi; Ouzzani, Mourad; Papotti, Paolo; Quiane Ruiz, Jorge Arnulfo; Tang, Nan; Zaki, Mohammed J.

Advances in Database Technology - EDBT 2016: 19th International Conference on Extending Database Technology, Proceedings. Vol. 2016-March OpenProceedings.org, 2016. p. 479-484.

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

Agrawal, D, Chawla, S, Elmagarmid, A, Kaoudi, Z, Ouzzani, M, Papotti, P, Quiane Ruiz, JA, Tang, N & Zaki, MJ 2016, Road to freedom in big data analytics. in Advances in Database Technology - EDBT 2016: 19th International Conference on Extending Database Technology, Proceedings. vol. 2016-March, OpenProceedings.org, pp. 479-484, 19th International Conference on Extending Database Technology, EDBT 2016, Bordeaux, France, 15/3/16. https://doi.org/10.5441/002/edbt.2016.45
Agrawal D, Chawla S, Elmagarmid A, Kaoudi Z, Ouzzani M, Papotti P et al. Road to freedom in big data analytics. In Advances in Database Technology - EDBT 2016: 19th International Conference on Extending Database Technology, Proceedings. Vol. 2016-March. OpenProceedings.org. 2016. p. 479-484 https://doi.org/10.5441/002/edbt.2016.45
Agrawal, Divy ; Chawla, Sanjay ; Elmagarmid, Ahmed ; Kaoudi, Zoi ; Ouzzani, Mourad ; Papotti, Paolo ; Quiane Ruiz, Jorge Arnulfo ; Tang, Nan ; Zaki, Mohammed J. / Road to freedom in big data analytics. Advances in Database Technology - EDBT 2016: 19th International Conference on Extending Database Technology, Proceedings. Vol. 2016-March OpenProceedings.org, 2016. pp. 479-484
@inproceedings{0b610f15ac924529ac366ad666293eac,
title = "Road to freedom in big data analytics",
abstract = "The world is fast moving towards a data-driven society where data is the most valuable asset. Organizations need to perform very diverse analytic tasks using various data processing platforms. In doing so, they face many challenges; chiefly, platform dependence, poor interoperability, and poor performance when using multiple platforms. We present Rheem, our vision for big data analytics over diverse data processing platforms. Rheem provides a threelayer data processing and storage abstraction to achieve both platform independence and interoperability across multiple platforms. In this paper, we discuss our vision as well as present multiple research challenges that we need to address to achieve it. As a case in point, we present a data cleaning application built using some of the ideas of Rheem. We show how it achieves platform independence and the performance benefits of following such an approach.",
author = "Divy Agrawal and Sanjay Chawla and Ahmed Elmagarmid and Zoi Kaoudi and Mourad Ouzzani and Paolo Papotti and {Quiane Ruiz}, {Jorge Arnulfo} and Nan Tang and Zaki, {Mohammed J.}",
year = "2016",
month = "1",
day = "1",
doi = "10.5441/002/edbt.2016.45",
language = "English",
volume = "2016-March",
pages = "479--484",
booktitle = "Advances in Database Technology - EDBT 2016",
publisher = "OpenProceedings.org",

}

TY - GEN

T1 - Road to freedom in big data analytics

AU - Agrawal, Divy

AU - Chawla, Sanjay

AU - Elmagarmid, Ahmed

AU - Kaoudi, Zoi

AU - Ouzzani, Mourad

AU - Papotti, Paolo

AU - Quiane Ruiz, Jorge Arnulfo

AU - Tang, Nan

AU - Zaki, Mohammed J.

PY - 2016/1/1

Y1 - 2016/1/1

N2 - The world is fast moving towards a data-driven society where data is the most valuable asset. Organizations need to perform very diverse analytic tasks using various data processing platforms. In doing so, they face many challenges; chiefly, platform dependence, poor interoperability, and poor performance when using multiple platforms. We present Rheem, our vision for big data analytics over diverse data processing platforms. Rheem provides a threelayer data processing and storage abstraction to achieve both platform independence and interoperability across multiple platforms. In this paper, we discuss our vision as well as present multiple research challenges that we need to address to achieve it. As a case in point, we present a data cleaning application built using some of the ideas of Rheem. We show how it achieves platform independence and the performance benefits of following such an approach.

AB - The world is fast moving towards a data-driven society where data is the most valuable asset. Organizations need to perform very diverse analytic tasks using various data processing platforms. In doing so, they face many challenges; chiefly, platform dependence, poor interoperability, and poor performance when using multiple platforms. We present Rheem, our vision for big data analytics over diverse data processing platforms. Rheem provides a threelayer data processing and storage abstraction to achieve both platform independence and interoperability across multiple platforms. In this paper, we discuss our vision as well as present multiple research challenges that we need to address to achieve it. As a case in point, we present a data cleaning application built using some of the ideas of Rheem. We show how it achieves platform independence and the performance benefits of following such an approach.

UR - http://www.scopus.com/inward/record.url?scp=85013257681&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85013257681&partnerID=8YFLogxK

U2 - 10.5441/002/edbt.2016.45

DO - 10.5441/002/edbt.2016.45

M3 - Conference contribution

VL - 2016-March

SP - 479

EP - 484

BT - Advances in Database Technology - EDBT 2016

PB - OpenProceedings.org

ER -