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Exploring Big Data with Helix: Finding Needles in a Big Haystack

Authors:

Achille Fokoue,

Oktie Hassanzadeh,

Anastasios Kementsietsidis,

Kavitha Srinivas,

Michael J. WardAuthors Info & Claims

ACM SIGMOD Record, Volume 43, Issue 4

Pages 43 - 54

https://doi.org/10.1145/2737817.2737829

Published: 18 February 2015 Publication History

Abstract

While much work has focused on efficient processing of Big Data, little work considers how to understand them. In this paper, we describe Helix, a system for guided exploration of Big Data. Helix provides a unified view of sources, ranging from spreadsheets and XML files with no schema, all the way to RDF graphs and relational data with well-defined schemas. Helix users explore these heterogeneous data sources through a combination of keyword searches and navigation of linked web pages that include information about the schemas, as well as data and semantic links within and across sources. At a technical level, the paper describes the research challenges involved in developing Helix, along with a set of real-world usage scenarios and the lessons learned.

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Cited By

Mansour ESrinivas KHose K(2022)Federated Data Science to Break Down Silos [Vision]ACM SIGMOD Record10.1145/3516431.351643550:4(16-22)Online publication date: 31-Jan-2022
https://dl.acm.org/doi/10.1145/3516431.3516435
Sargolzaei Javan MAkbari M(2019)SmartData 4.0The Journal of Supercomputing10.1007/s11227-018-2705-y75:7(3585-3620)Online publication date: 31-Jul-2019
https://dl.acm.org/doi/10.1007/s11227-018-2705-y
Abolhassani NPuri CParthasarathy SWang ZKujawinski MTung TRamaswamy L(2018)Universal Metadata Repository: Integrating Data Profiles Across an Organization2018 IEEE International Conference on Information Reuse and Integration (IRI)10.1109/IRI.2018.00072(452-459)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1109/IRI.2018.00072
Show More Cited By

Index Terms

Exploring Big Data with Helix: Finding Needles in a Big Haystack
1. Information systems
  1. Data management systems
    1. Database design and models
    2. Database management system engines
  2. Information retrieval

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image ACM SIGMOD Record

ACM SIGMOD Record Volume 43, Issue 4

December 2014

54 pages

ISSN:0163-5808

DOI:10.1145/2737817

Editors:
Yanlei Diao
University of Massachusetts Amherst
,
Pablo Barceló
Universidad de Chile
,
Vanessa Braganholo
Universidade Federal Fluminense
,
Marco Brambilla
Politecnico di Milano
,
Chee Yong Chan
National University of Singapore
,
Rada Chirkova
North Carolina State University
,
Anish Das Sarma
Google Research
,
Alkis Simitsis
HP Labs
,
Nesime Tatbul
ETH Zurich
,
Marianne Winslett
University of Illinois

Issue’s Table of Contents

Copyright © 2015 Authors.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 February 2015

Published in SIGMOD Volume 43, Issue 4

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Cited By

Mansour ESrinivas KHose K(2022)Federated Data Science to Break Down Silos [Vision]ACM SIGMOD Record10.1145/3516431.351643550:4(16-22)Online publication date: 31-Jan-2022
https://dl.acm.org/doi/10.1145/3516431.3516435
Sargolzaei Javan MAkbari M(2019)SmartData 4.0The Journal of Supercomputing10.1007/s11227-018-2705-y75:7(3585-3620)Online publication date: 31-Jul-2019
https://dl.acm.org/doi/10.1007/s11227-018-2705-y
Abolhassani NPuri CParthasarathy SWang ZKujawinski MTung TRamaswamy L(2018)Universal Metadata Repository: Integrating Data Profiles Across an Organization2018 IEEE International Conference on Information Reuse and Integration (IRI)10.1109/IRI.2018.00072(452-459)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1109/IRI.2018.00072
Castro Fernandez RMansour EQahtan AElmagarmid AIlyas IMadden SOuzzani MStonebraker MTang N(2018)Seeping Semantics: Linking Datasets Using Word Embeddings for Data Discovery2018 IEEE 34th International Conference on Data Engineering (ICDE)10.1109/ICDE.2018.00093(989-1000)Online publication date: Apr-2018
https://doi.org/10.1109/ICDE.2018.00093
Lehmberg OBizer C(2017)Stitching web tables for improving matching qualityProceedings of the VLDB Endowment10.14778/3137628.313765710:11(1502-1513)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.14778/3137628.3137657
Wahl A(2016)A minimally-intrusive approach for query-driven data integration systems2016 IEEE 32nd International Conference on Data Engineering Workshops (ICDEW)10.1109/ICDEW.2016.7495654(231-235)Online publication date: May-2016
https://doi.org/10.1109/ICDEW.2016.7495654
Chen YChen HGorkhali ALu YMa YLi L(2016)Big data analytics and big data science: a surveyJournal of Management Analytics10.1080/23270012.2016.11413323:1(1-42)Online publication date: 26-Feb-2016
https://doi.org/10.1080/23270012.2016.1141332

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