Detecting Text Similarity on a Scalable No-SQL Database Platform

Abstract

The paper looks at the platform scalability problem for near-to-similar document detection tasks. The application areas for the proposed approach include plagiarism detection and text filtering in data leak prevention systems. The paper reviews limitations of the current solutions based on the relational DBMS and suggests data structure suitable for implementation in no-SQL databases on the highly scalable clustered platforms. The proposed data structure is based on "key-value" model and it does not depend on the shingling method used to encode the text. The proposed model was implemented on the clustered MongoDB platform and tested with the large dataset on the platform that was scaled up horizontally during the experiment. The experiments indicated the applicability of the proposed approach to near-to-similar document detection.

Strengthening the research agenda of educational integrity in Canada: a review of the research literature and call to action

“…One final note about knowledge mobilization, is that we observed that the most prolific among the authors whose work we analyzed was Butakov, who authored or co-authored six research outputs, including one conference proceeding (Butakov 2014), one book chapter (Butakov, Shcherbinin, Diagilev and Tskhay 2013) and four peer-reviewed articles (Butakov and Barber 2012;Butakov, Dyagilev andTskha 2012, Butakov, Murzintsev andTskhai 2016;Butakov and Shcherbinin 2009), including both analytic and descriptive contributions. In our analysis, we found the work conducted by Butakov and his colleagues to be among the most technical, with a focus on computer science and software development.…”

“…Sub-theme 2b: Prevention Related to professional development, a similar body of work addressed the issue of prevention, and was dominated by the work of one researcher (Butakov) in collaboration with others interested in the development of software to detect plagiarism (Butakov and Barber 2012;Butakov, Murzintsev and Tskhai, 2016;Butakov and Shcherbinin 2009;Butakov, Shcherbinin, Diagilev and Tskhay 2013). Interestingly, these studies seemed to stand apart from all the others in that their focus was highly technical and focused intently on computer science.…”