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EDBT
2016
ACM

Data Wrangling for Big Data: Challenges and Opportunities

3 years 6 months ago
Data Wrangling for Big Data: Challenges and Opportunities
Data wrangling is the process by which the data required by an application is identified, extracted, cleaned and integrated, to yield a data 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...
Tim Furche, Georg Gottlob, Leonid Libkin, Giorgio
Added 02 Apr 2016
Updated 02 Apr 2016
Type Journal
Year 2016
Where EDBT
Authors Tim Furche, Georg Gottlob, Leonid Libkin, Giorgio Orsi, Norman W. Paton
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