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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2018 Vol.8(4): 319-323 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2018.8.4.705

On the Use of Hash Maps for Data Reconciliation Optimization over a Data Integration System

Abdelghani Bakhtouchi and M’hamed Mataoui
Abstract—The invention of the Internet and the emergence of the World Wide Web revolutionized people’s access to digital data stored on electronic devices. But unlike traditional data management applications, the new services require the ability to share data among multiple applications and organizations, and to integrate data in a flexible and efficient fashion. Data integration systems enable building systems geared for flexible sharing and integration of data across multiple autonomous data providers. In this paper we propose the use of hash map structure to optimize the data reconciliation over our data integration system. We have noticed that the use of the array data structure to store the intermediate results of the reconciliation takes a considerable time. This led us to change it by the hash map that assures a reconciliation runtime much less than when using arrays. We validate our choice again theoretically and experimentally.

Index Terms—Data integration, hash map, optimization, reconciliation.

Abdelghani Bakhtouchi is with Ecole nationale Supérieure d’Informatique (ESI) and Ecole Militaire Polytechnique (EMP), Algiers, Algeria (e-mail: a_bakhtouchi@esi.dz).
M’hamed Mataoui is with Ecole Militaire Polytechnique, Algiers, Algeria (e-mail: mataoui.mhamed@gmail.com).

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Cite: Abdelghani Bakhtouchi and M’hamed Mataoui, "On the Use of Hash Maps for Data Reconciliation Optimization over a Data Integration System," International Journal of Machine Learning and Computing vol. 8, no. 4, pp. 319-323, 2018.

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