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Datasets for Parallel Meta-blocking

Name: Datasets for Parallel Meta-blocking
Creator: George Papadakis
Keywords: Blocks, Entity Resolution, Parallel Meta-blocking

Research datakeyboard_double_arrow_right Dataset 03 Apr 2022Publisher:Zenodo

Authors: George Papadakis;

doi: 10.5281/zenodo.6408959 , 10.5281/zenodo.6408960

Datasets for Parallel Meta-blocking

- Summary
- Subjects
- Related research
  (2)
- Metrics

Abstract

This is a collection of the real-world datasets that were used in the publications: Vasilis Efthymiou, George Papadakis, George Papastefanatos, Kostas Stefanidis, Themis Palpanas: Parallel meta-blocking for scaling entity resolution over big heterogeneous data. Inf. Syst. 65: 137-157 (2017) 2015 Vasilis Efthymiou, George Papadakis, George Papastefanatos, Kostas Stefanidis, Themis Palpanas: Parallel meta-blocking: Realizing scalable entity resolution over large, heterogeneous data. IEEE BigData 2015: 411-420

Related Organizations

National and Kapodistrian University of Athens
Greece

Keywords

Blocks, Entity Resolution, Parallel Meta-blocking

2 Research products, page 1 of 1

Boosting the Efficiency of Large-Scale Entity Resolution with Enhanced Meta-Blocking
2016IsAmongTopNSimilarDocuments
Boosting the Efficiency of Large-Scale Entity Resolution with Enhanced Meta-Blocking
2016IsAmongTopNSimilarDocuments

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	0
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average