University of Oulu

P. Kostakos, M. Moilanen, A. Niemelä and M. Oussalah, "Catchem: A Browser Plugin for the Panama Papers Using Approximate String Matching," 2017 European Intelligence and Security Informatics Conference (EISIC), Athens, 2017, pp. 139-142. doi: 10.1109/EISIC.2017.28

Catchem : a browser plugin for the Panama papers using approximate string matching

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Author: Kostakos, Panos1; Moilanen, Miika1; Niemelä, Arttu1;
Organizations: 1Center for Ubiquitous Computing, University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.4 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe201901222693
Language: English
Published: Institute of Electrical and Electronics Engineers, 2017
Publish Date: 2019-01-22
Description:

Abstract

The Panama Papers is a collection of 11.5 million leaked records that contain information for more than 214,488 offshore entities. This collection is growing rapidly as more leaked records become available online. In this paper, we present a work in progress on a web browser plugin that detects company names from the Panama Papers and alerts the user by means of unobtrusive visual cues. We matched a random sample of company names from the Public Works and Government Services Canada registry against the Panama Papers using three different string matching techniques. Monge-Elkan is found to provide the best matching results but at increased computational cost. Levenshtein-based approach is found to provide the best tradeoff between matching and computational cost, while Jacquard index like approach is found to be less sensitive to slight textual change.

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ISBN: 978-1-5386-2385-5
ISBN Print: 978-1-5386-2386-2
Pages: 139 - 142
DOI: 10.1109/EISIC.2017.28
OADOI: https://oadoi.org/10.1109/EISIC.2017.28
Host publication: 2017 European Intelligence and Security Informatics Conference (EISIC)
Conference: European Intelligence and Security Informatics Conference
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Subjects:
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