TY - JOUR
T1 - Detecting data errors
T2 - 42nd International Conference on Very Large Data Bases, VLDB 2016
AU - Abedjan, Ziawasch
AU - Chu, Xu
AU - Deng, Dong
AU - Fernandez, Raul Castro
AU - Ilyas, Ihab F.
AU - Ouzzani, Mourad
AU - Papotti, Paolo
AU - Stonebraker, Michael
AU - Tang, Nan
N1 - Publisher Copyright:
© 2016 VLDB Endowment 2150-8097/16/08.
PY - 2016
Y1 - 2016
N2 - Data cleaning has played a critical role in ensuring data quality for enterprise applications. Naturally, there has been extensive research in this area, and many data cleaning algorithms have been translated into tools to detect and to possibly repair certain classes of errors such as outliers, duplicates, missing values, and violations of integrity constraints. Since different types of errors may coexist in the same data set, we often need to run more than one kind of tool. In this paper, we investigate two pragmatic questions: (1) are these tools robust enough to capture most errors in real-world data sets? and (2) what is the best strategy to holistically run multiple tools to optimize the detection effort? To answer these two questions, we obtained multiple data cleaning tools that utilize a variety of error detection techniques. We also collected five real-world data sets, for which we could obtain both the raw data and the ground truth on existing errors. In this paper, we report our experimental findings on the errors detected by the tools we tested. First, we show that the coverage of each tool is well below 100%. Second, we show that the order in which multiple tools are run makes a big difference. Hence, we propose a holistic multi-tool strategy that orders the invocations of the available tools to maximize their benefit, while minimizing human effort in verifying results. Third, since this holistic approach still does not lead to acceptable error coverage, we discuss two simple strategies that have the potential to improve the situation, namely domain specific tools and data enrichment. We close this paper by reasoning about the errors that are not detectable by any of the tools we tested.
AB - Data cleaning has played a critical role in ensuring data quality for enterprise applications. Naturally, there has been extensive research in this area, and many data cleaning algorithms have been translated into tools to detect and to possibly repair certain classes of errors such as outliers, duplicates, missing values, and violations of integrity constraints. Since different types of errors may coexist in the same data set, we often need to run more than one kind of tool. In this paper, we investigate two pragmatic questions: (1) are these tools robust enough to capture most errors in real-world data sets? and (2) what is the best strategy to holistically run multiple tools to optimize the detection effort? To answer these two questions, we obtained multiple data cleaning tools that utilize a variety of error detection techniques. We also collected five real-world data sets, for which we could obtain both the raw data and the ground truth on existing errors. In this paper, we report our experimental findings on the errors detected by the tools we tested. First, we show that the coverage of each tool is well below 100%. Second, we show that the order in which multiple tools are run makes a big difference. Hence, we propose a holistic multi-tool strategy that orders the invocations of the available tools to maximize their benefit, while minimizing human effort in verifying results. Third, since this holistic approach still does not lead to acceptable error coverage, we discuss two simple strategies that have the potential to improve the situation, namely domain specific tools and data enrichment. We close this paper by reasoning about the errors that are not detectable by any of the tools we tested.
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U2 - 10.14778/2994509.2994518
DO - 10.14778/2994509.2994518
M3 - Conference article
AN - SCOPUS:85013662261
SN - 2150-8097
VL - 9
SP - 993
EP - 1004
JO - Proceedings of the VLDB Endowment
JF - Proceedings of the VLDB Endowment
IS - 12
Y2 - 5 September 2016 through 9 September 2016
ER -