Entity Resolution for Big Data using Combination of Supervised Meta-Blocking and pay-as-you-go Configuration
Viral A. Parekh1, K. H. Wandra2

1Viral A. Parekh, C. U. Shah University, Gujarat, India.

2Dr. K. H. Wandra, Gujarat Maritime Board, Gujarat Technological University, India.

Manuscript received on 05 April 2019 | Revised Manuscript received on 12 April 2019 | Manuscript Published on 26 July 2019 | PP: 166-167 | Volume-8 Issue-6S4 April 2019 | Retrieval Number: F10310486S419/19©BEIESP | DOI: 10.35940/ijitee.F1031.0486S419

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Entity resolution refers to the method of identifying the same real world object from multiple data sets. In Data cleaning and data integration application, entity resolution is an important process. When data is large the task of entity resolution becomes complex and time consuming. End-to-end entity resolution proposal involves stages like blocking (efficiently identifies duplicates), detailed comparison (refines blocking output) and clustering (identifies the set of records which may refer to the same entity). In this paper, an approach for feedback based optimization of complete entity resolution is proposed in which supervised meta-blocking is used for blocking stage. This paper proposes a technique for entity resolution which does optimization of each phase of entity resolution with benefits of supervised Meta-blocking to improve performance of entity resolution for big data.

Keywords: This Paper Proposes a Technique for Entity Resolution Which Does Optimization.
Scope of the Article: System Integration