Data Cleaning: Problems and Current Approaches.

852 indexed citations

Abstract

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About

This paper, published in 2000, received 852 indexed citations. Written by Erhard Rahm and Hai Hong covering the research area of Computer Networks and Communications, Information Systems and Management Science and Operations Research. It is primarily cited by scholars working on Management Science and Operations Research (445 citations), Artificial Intelligence (357 citations) and Information Systems (336 citations). Published in Qucosa (Saxon State and University Library Dresden).

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Countries where authors are citing Data Cleaning: Problems and Current Approaches.

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This map shows the geographic impact of Data Cleaning: Problems and Current Approaches.. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Data Cleaning: Problems and Current Approaches. with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Data Cleaning: Problems and Current Approaches. more than expected).

Fields of papers citing Data Cleaning: Problems and Current Approaches.

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Data Cleaning: Problems and Current Approaches.. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Data Cleaning: Problems and Current Approaches..

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/w3528093.

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