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A comparison of techniques for name matching (2012)
Journal Article
Peng, T., Li, L., & Kennedy, J. (2012). A comparison of techniques for name matching. GSTF journal on computing, 2,

Information explosion is a problem for everyone nowadays. It is a great challenge to all kinds of businesses to maintain high quality of data in their information applications, such as data integration, text and web mining, information retrieval, sea... Read More about A comparison of techniques for name matching.

Data quality and data cleaning in database applications (2012)
Thesis
Li, L. Data quality and data cleaning in database applications. (Thesis). Edinburgh Napier University. Retrieved from http://researchrepository.napier.ac.uk/id/eprint/5788

Today, data plays an important role in people’s daily activities. With the help of some database applications such as decision support systems and customer relationship management systems (CRM), useful information or knowledge could be derived from l... Read More about Data quality and data cleaning in database applications.

An evaluation of name matching techniques. (2011)
Conference Proceeding
Peng, T., Li, L., & Kennedy, J. (2011). An evaluation of name matching techniques. In Proceedings of 2nd Annual International Conference on Business Intelligence and Data Warehousing

Abstract—There is a growing awareness that the high quality of string matching is a key to a variety of applications, such as data integration, text and web mining, information retrieval, search engine. In such applications, matching names is one of... Read More about An evaluation of name matching techniques..

A rule based taxonomy of dirty data. (2011)
Journal Article
Li, L., Peng, T., & Kennedy, J. (2011). A rule based taxonomy of dirty data. GSTF journal on computing, 1(2), 140-148

There is a growing awareness that high quality of data is a key to today’s business success and that dirty data existing within data sources is one of the causes of poor data quality. To ensure high quality data, enterprises need to have a process, m... Read More about A rule based taxonomy of dirty data..

Improving data quality in data warehousing applications (2010)
Conference Proceeding
Li, L., Peng, T., & Kennedy, J. (2010). Improving data quality in data warehousing applications. In J. Filipe, & J. Cordeiro (Eds.), Proceedings of the 12th International Conference on Enterprise Information Systems (379-382). https://doi.org/10.5220/0002903903790382

There is a growing awareness that high quality of data is a key to today’s business success and dirty data that exits within data sources is one of the reasons that cause poor data quality. To ensure high quality, enterprises need to have a process,... Read More about Improving data quality in data warehousing applications.