González Enríquez, JoséLee, VivianGoto, MasatomoDomínguez Mayo, Francisco JoséEscalona Cuaresma, María José2019-08-192019-08-192015González Enríquez, J., Lee, V., Goto, M., Domínguez Mayo, F.J. y Escalona Cuaresma, M.J. (2015). Entity Identification Problem in Big and Open Data. En ICEIS 2015: 17th International Conference on Enterprise Information Systems (404-408), Barcelona, España: ScitePress Digital Library.978-989-758-096-3https://hdl.handle.net/11441/88412Big and Open Data provide great opportunities to businesses to enhance their competitive advantages if utilized properly. However, during past few years’ research in Big and Open Data process, we have encountered big challenge in entity identification reconciliation, when trying to establish accurate relationships between entities from different data sources. In this paper, we present our innovative Intelligent Reconciliation Platform and Virtual Graphs solution that addresses this issue. With this solution, we are able to efficiently extract Big and Open Data from heterogeneous source, and integrate them into a common analysable format. Further enhanced with the Virtual Graphs technology, entity identification reconciliation is processed dynamically to produce more accurate result at system runtime. Moreover, we believe that our technology can be applied to a wide diversity of entity identification problems in several domains, e.g., e- Health, cultural heritage, and company identities in financial world.application/pdfengAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Software EngineeringBig dataOpen dataEntity IdentificationIntelligent ReconciliationVirtual Graphsinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccesshttps://doi.org/10.5220/0005470704040408