Entity reconciliation in big data sources: A systematic mapping study. (1st September 2017)

• Record Type: Journal Article
• Authors: Enríquez, J.G.; Domínguez-Mayo, F.J.; Escalona, M.J.; Ross, M.; Staples, G.
• Is Part Of: Expert systems with applications. Volume 80(2017)
• Page Start: 14
• Page End: 27
• Publication Date: 2017-09-01
• Subjects: Systematic mapping study -- Entity reconciliation -- Heterogeneous databases -- Big data; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.03.010
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Systematic Mapping Study about entity reconciliation in Big Data. Eleven databases were consulted. Rigorous process where 2255 papers were analyzed leaving 61 primary studies. Analytic data statistics after the classification of the primary studies. Discussion that presents some interesting intelligent system-based papers for solving entity reconciliation and conclusions obtained from the data retrieved. Abstract: The entity reconciliation (ER) problem aroused much interest as a research topic in today's Big Data era, full of big and open heterogeneous data sources. This problem poses when relevant information on a topic needs to be obtained using methods based on: (i) identifying records that represent the same real world entity, and (ii) identifying those records that are similar but do not correspond to the same real-world entity. ER is an operational intelligence process, whereby organizations can unify different and heterogeneous data sources in order to relate possible matches of non-obvious entities. Besides, the complexity that the heterogeneity of data sources involves, the large number of records and differences among languages, for instance, must be added. This paper describes a Systematic Mapping Study (SMS) of journal articles, conferences and workshops published from 2010 to 2017 to solve the problem described before, first trying to understand the state-of-the-art, and then identifying any gaps in current research. Eleven digital libraries were analyzed following a systematic, semiautomatic and rigorous process that has resulted in 61 primary studies. They represent a great variety of intelligent proposals that aim to solve ER. The conclusion obtained is that most of the research is based on the operational phase as opposed to the design phase, and most studies have been tested on real-world data sources, where a lot of them are heterogeneous, but just a few apply to industry. There is a clear trend in research techniques based on clustering/blocking and graphs, although the level of automation of the proposals is hardly ever mentioned in the research work.