Approximate String Matching Techniques

Taoxin Peng, Calum Mackay


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 problem, 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.


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Paper Citation

in Harvard Style

Peng T. and Mackay C. (2014). Approximate String Matching Techniques . In Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-027-7, pages 217-224. DOI: 10.5220/0004892802170224

in Bibtex Style

author={Taoxin Peng and Calum Mackay},
title={Approximate String Matching Techniques},
booktitle={Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},

in EndNote Style

JO - Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Approximate String Matching Techniques
SN - 978-989-758-027-7
AU - Peng T.
AU - Mackay C.
PY - 2014
SP - 217
EP - 224
DO - 10.5220/0004892802170224