Abstract
Data quality is a key to success for all kinds of businesses that have information applications involved, such as data integration for data warehouses, text and web mining, information retrieval, search engine for web applications, etc. In such applications, matching strings is one of the popular tasks. There are a number of approximate string matching techniques available. However, there is still a problem that remains unanswered: for a given dataset, how to select an appropriate technique and a threshold value required by this technique for the purpose of string matching. To challenge this question, this paper analyses and evaluates a set of popular token-based string matching techniques on several carefully designed different datasets. A thorough experimental comparison confirms the statement that there is no clear overall best technique. However, some techniques do perform significantly better in some cases. Some suggestions have been presented, which can be used as guidance for researchers and practitioners to select an appropriate string matching technique and a corresponding threshold value for a given dataset.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
