International Journal of Advanced Information and Communication Technology


A Approach for Class Based Matching over Heterogeneous Data Sets

S. Revathi, N. Poongothai, Sasurie Academy of Engineering, Coimbatore, Tamilnadu, India.

DOI : 01.0401/ijaict.2015.07.05

International Journal of Advanced Information and Communication Technology

Received On :January 12, 2019

Revised On :February 16, 2019

Accepted On :March 10, 2019

Published On :April 05, 2019

Volume 06, Issue 04

Pages : 1063-1066

Abstract


In heterogeneous datasets while used matching instances state-of-the-art instance matching approaches do not perform well. From the core operation on direct matching these drawbacks should be derived. The direct matching involves a direct comparison between instances from the source dataset and instances in the target dataset. If the overlap between the datasets is small direct matching is not suitable. The big aim of this survey is resolving this problem by proposing a new paradigm called class-based matching. The class of interest is defined as a class of instances from the source dataset. The class-based matching is defined as a set of candidate matches retrieved from the target. The candidate refining process could be done by filtering out those that do not belong to the class of interest. For this type of refinement, only data in the target dataset is used which states that no direct comparison between source and target is involved. Based on the public benchmarks in the difficult matching tasks this approach greatly improves the quality of state-of-the-art systems.

Keywords


Data Integration, Class-Based Matching, Direct Matching, Instance Matching, Semantic Web.

Cite this article


S. Revathi, N. Poongothai, “Reduction of Common-Mode Voltage in Open End Winding Induction Motor Drive Using Carrier Phase-Shift Strategy, ” INTERNATIONAL JOURNAL OF ADVANCED INFORMATION AND COMMUNICATION TECHNOLOGY, pp.1063-1066, April. 2019.

Copyright


© 2019 S. Revathi, N. Poongothai. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.