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Data Wrangling for Big Data: Challenges and Opportunities

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Original languageEnglish
Title of host publicationAdvances in Database Technology — EDBT 2016
Subtitle of host publicationProceedings of the 19th International Conference on Extending Database Technology
Number of pages6
Publication statusPublished - 2016
Event19th International Conference on Extending Database Technology - Bordeaux, France
Duration: 15 Mar 201618 Mar 2016

Publication series

NameAdvances in Database Technology
PublisherUniversity of Konstanz
ISSN (Print)2367-2005


Conference19th International Conference on Extending Database Technology
Abbreviated titleEDBT 2016
Internet address


Data wrangling is the process by which the data required by an application is identified, extracted, cleaned and integrated, to yield adata set that is suitable for exploration and analysis. Although there are widely used Extract, Transform and Load (ETL) techniques and platforms, they often require manual work from technical and domain experts at different stages of the process. When confronted with the 4 V’s of big data (volume, velocity, variety and veracity),manual intervention may make ETL prohibitively expensive. This paper argues that providing cost-effective, highly-automated approaches to data wrangling involves significant research challenges,requiring fundamental changes to established areas such as data extraction,integration and cleaning, and to the ways in which these areas are brought together. Specifically, the paper discusses the importance of comprehensive support for context awareness within data wrangling, and the need for adaptive, pay-as-you-go solutions that automatically tune the wrangling process to the requirements and resources of the specific application.


19th International Conference on Extending Database Technology


Bordeaux, France

Event: Conference

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