Efficient identity matching using static pruning q-gram indexing approach

Abstract

Information overload is a growing problem for information management and analytics in many organizations. Identity matching techniques are used to manage and resolve millions of identity records in diverse domains such as health care information, telecom subscribers, insurance holders, law offenders, and the census. In this paper, we propose an identity matching technique that is efficient for large datasets without compromising matching effectiveness. Our experimental results provide strong evidence that our proposed identity matching technique outperforms the adaptive detection identity matching technique in terms of efficiency and effectiveness, reducing the number of required comparisons by almost 98% and the completion time by 97%, with promising scalability results. Furthermore, our proposed technique achieves better matching results than the most trusted pairwise identity matching approach.