Work

Data X-Ray

Year: 2015

Type: conference-paper

Abstract: A lot of systems and applications are data-driven, and the correctness of their operation relies heavily on the correctness of their data. While existing data cleaning techniques can be quite effective at purging datasets of errors, they disregard the fact that a lot of errors are systematic, inherent to the process that produces the data, and thus will keep occurring unless the problem is corrected at its source. In contrast to traditional data cleaning, in this paper we focus on data diagnosis: explaining where and how the errors happen in a data generative process.

Source: ACM SIGMOD International Conference on Management of Data (SIGMOD)

DOI: 10.1145/2723372.2750549

Authors: Xiaolan Wang, Xin Luna Dong, Alexandra Meliou

Institutions: University of Massachusetts Amherst, Google (United States)

Language: English

Topic: Data Quality and Management

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: bronze

Funders: National Science Foundation, Google

Awards: EAGER: Data Debugging, III: Small: Collaborative Research: The Package Query Paradigm