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The VLDB Journal
Erschienen in: The VLDB Journal 4/2015

01.08.2015 | Regular Paper

Profiling relational data: a survey

verfasst von: Ziawasch Abedjan, Lukasz Golab, Felix Naumann

Erschienen in: The VLDB Journal | Ausgabe 4/2015

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Abstract

Profiling data to determine metadata about a given dataset is an important and frequent activity of any IT professional and researcher and is necessary for various use-cases. It encompasses a vast array of methods to examine datasets and produce metadata. Among the simpler results are statistics, such as the number of null values and distinct values in a column, its data type, or the most frequent patterns of its data values. Metadata that are more difficult to compute involve multiple columns, namely correlations, unique column combinations, functional dependencies, and inclusion dependencies. Further techniques detect conditional properties of the dataset at hand. This survey provides a classification of data profiling tasks and comprehensively reviews the state of the art for each class. In addition, we review data profiling tools and systems from research and industry. We conclude with an outlook on the future of data profiling beyond traditional profiling tasks and beyond relational databases.
Vorheriger Artikel Finding top-k relevant groups of spatial web objects
Nächster Artikel Provenance-based analysis of data-centric processes

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Fußnoten
1
See Sect. 6 for a more comprehensive list of tools.
 
2
“Data gazing involves looking at the data and trying to reconstruct a story behind these data. [...] Data gazing mostly uses deduction and common sense.” [104]
 
3
A more detailed regular expression, taking into account different formatting options and different restrictions (e.g., phone numbers cannot begin with a 1), can easily reach 200 characters in length.
 
4
Differential dependencies also generalize matching dependencies [49] (if two tuples have close values of X, their A values must be exactly the same) and metric functional dependencies [89] (if two tuples have the same values of X, their A values must be close).
 
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Metadaten
Titel
Profiling relational data: a survey
verfasst von
Ziawasch Abedjan
Lukasz Golab
Felix Naumann
Publikationsdatum
01.08.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
The VLDB Journal / Ausgabe 4/2015
Print ISSN: 1066-8888
Elektronische ISSN: 0949-877X
DOI
https://doi.org/10.1007/s00778-015-0389-y

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