Yordan Kalmukov

Yordan Kalmukov

One of the most popular and basic principles in programming is the DRY principle (don't repeat yourself). According to it, code duplication should be avoided within a single application. Instead of duplicating it, the code can be exported to/as a subroutine, which can be called as many times as needed and where needed. The same principle is fully adopted and integrated in the object-oriented design as well. It makes the code better structured and more flexible, and significantly facilitates future updates and development of the application. However, there is one problem - cascaded methods calls. Each subroutine call has a price - the code execution time increases and the application performance decreases. The aim of this paper is to conduct a series of experimental analyses to determine how much the performance of a PHP application decreases when the code is exported to subroutines and their subsequent call.

Yordan Kalmukov

Generating word (tag) clouds is a powerful data visualization technique that allows people to get easily acquainted with the content of a large collection of textual documents and identify their subject domains for a matter of seconds, without reading them at all. This paper suggests applying word clouds visualization to conference management systems (specialized document management and decision support systems) in order to support and facilitate decision making in at least two important processes - forming the Programme Committee by inviting suitable reviewers and manual (re)assignment of reviewers to papers. Word clouds proved to be very useful tool for fast identification of papers' subject domain and reviewers' competences.

Yordan Kalmukov

This article focuses on the importance of the precise calculation of similarity factors between papers and reviewers for performing a fair and accurate automatic assignment of reviewers to papers. It suggests that papers and reviewers' competences should be described by taxonomy of keywords so that the implied hierarchical structure allows similarity measures to take into account not only the number of exactly matching keywords, but in case of non-matching ones to calculate how semantically close they are. The paper also suggests a similarity measure derived from the well-known and widely-used Dice's coefficient, but adapted in a way it could be also applied between sets whose elements are semantically related to each other (as concepts in taxonomy are). It allows a non-zero similarity factor to be accurately calculated between a paper and a reviewer even if they do not share any keyword in common.