Identification of approximately duplicate material records in ERP systems
| Autor: | Feng Wu, Wei Zong, D. Sculli, Lap Keung Chu |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Information Systems and Management
Information retrieval business.industry Computer science String (computer science) Master data 02 engineering and technology computer.software_genre Field (computer science) Computer Science Applications Identifier Identification (information) Semantic similarity 020204 information systems Data quality 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining business Enterprise resource planning computer |
| Zdroj: | Enterprise Information Systems. 11:434-451 |
| ISSN: | 1751-7583 1751-7575 |
| DOI: | 10.1080/17517575.2015.1065513 |
| Popis: | The quality of master data is crucial for the accurate functioning of the various modules of an enterprise resource planning ERP system. This study addresses specific data problems arising from the generation of approximately duplicate material records in ERP databases. Such problems are mainly due to the firm’s lack of unique and global identifiers for the material records, and to the arbitrary assignment of alternative names for the same material by various users. Traditional duplicate detection methods are ineffective in identifying such approximately duplicate material records because these methods typically rely on string comparisons of each field. To address this problem, a machine learning-based framework is developed to recognise semantic similarity between strings and to further identify and reunify approximately duplicate material records – a process referred to as de-duplication in this article. First, the keywords of the material records are extracted to form vectors of discriminating words. Second, a machine learning method using a probabilistic neural network is applied to determine the semantic similarity between these material records. The approach was evaluated using data from a real case study. The test results indicate that the proposed method outperforms traditional algorithms in identifying approximately duplicate material records. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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