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An Introduction to Data Profiling

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Business Intelligence and Big Data (eBISS 2017)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 324))

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Abstract

One of the crucial requirements before consuming datasets for any application is to understand the dataset at hand and its metadata. The process of metadata discovery is known as data profiling. Profiling activities range from ad-hoc approaches, such as eye-balling random subsets of the data or formulating aggregation queries, to systematic inference of metadata via profiling algorithms. In this course, we will discuss the importance of data profiling as part of any data-related use-case, and shed light on the area of data profiling by classifying data profiling tasks and reviewing the state-of-the-art data profiling systems and techniques. In particular, we discuss hard problems in data profiling, such as algorithms for dependency discovery and their application in data management and data analytics. We conclude with directions for future research in the area of data profiling.

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Notes

  1. 1.

    https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/#2c61c6c56f63.

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  1. TU Berlin, Berlin, Germany

    Ziawasch Abedjan

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  1. Université Libre de Bruxelles, Brussels, Belgium

    Esteban Zimányi

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Abedjan, Z. (2018). An Introduction to Data Profiling. In: Zimányi, E. (eds) Business Intelligence and Big Data. eBISS 2017. Lecture Notes in Business Information Processing, vol 324. Springer, Cham. https://doi.org/10.1007/978-3-319-96655-7_1

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