A Study on Human Transiting Based on Big Data and Web Semantics: Distinguishment and Detection

Qiang Zhou

Source Title: International Journal on Semantic Web and Information Systems (IJSWIS)18(1)

ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781799893967|DOI: 10.4018/IJSWIS.310055

MLA

Zhou, Qiang. "A Study on Human Transiting Based on Big Data and Web Semantics: Distinguishment and Detection." IJSWIS vol.18, no.1 2022: pp.1-18. http://doi.org/10.4018/IJSWIS.310055

APA

Zhou, Q. (2022). A Study on Human Transiting Based on Big Data and Web Semantics: Distinguishment and Detection. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-18. http://doi.org/10.4018/IJSWIS.310055

Chicago

Zhou, Qiang. "A Study on Human Transiting Based on Big Data and Web Semantics: Distinguishment and Detection," International Journal on Semantic Web and Information Systems (IJSWIS) 18, no.1: 1-18. http://doi.org/10.4018/IJSWIS.310055

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Abstract

In the progress of globalization, the transnational human traffic is spreading globally. It damages national economy and social order as well as infringes on the basic human rights of the victims, which has aroused general concern all over the world, becoming global issues. One of the important features in human being traffic is the factor of globalization. A destination-source model works as a deterrent which is applied in the identification of smuggling and trafficking of illegal immigrants. The related results show that the employer penalty and market wage will influence the amount of smuggling and trafficking immigrants. Tax offered by legal unskilled workers at destination countries provides financial support for the inland monitoring of illegal immigrants. The improved SVM (supported vector machine) is proposed to study online textual data used for advertisement classification, with the purpose of discerning underlying human trafficking patterns on the network and recognizing suspicious advertisements, a concern of law-enforcement agencies.