Redundancy Avoidance in Entity Resolution Based On Social Networks Paradigm

Entity resolution (ER) aims at identifying and merging records in one or more datasets that refer to the same real-world entity. The ER problem is becoming more challenging in the context of Big Data. We study the ER problem by transforming it into a Social Network where data records can be treated as real-world entities capturing the existing relationships (e.g. friendship, householder). A framework to handle the transformation of the data model is presented and evaluated on several datasets. The framework is tested using four state-of-the-art ER, including (1) k-mean, (2) Levenshtein, (3) Jaro Winkler, and (4) Soundex on SNA in terms of time and accuracy performance metrics.