Differences of Pareto principle performance in e-resource download distribution

Abstract

Purpose The purpose of this paper is to explore whether the databases from a certain library are Pareto-compliant or not? If so, to what extent is the Pareto principle performance evident among these databases? The other purpose is to determine the differences in Pareto principle performance according to time change and database type. Design/methodology/approach Data on full-text downloads from six e-resources – Elsevier ScienceDirect (SD), Wiley Blackwell, Springer Journal, EBSCO Business Source Premier (BSP), American Chemical Society and American Institute of Physics (AIP) – for the period 2007-2013 were analysed; 42 samples were collected from these databases. The proportion of frequently downloaded journals from databases was selected as an indicator to determine differences in Pareto principle performance according to time change. The difference between the proportion of frequently downloaded journals and the classic proportion of 20 per cent was used as indicator to determine difference in Pareto principle performance related to database type. Findings There are 33 samples (78.57 per cent) which exhibited the Pareto principle. Four databases – Elsevier SD, Wiley Blackwell, EBSCO BSP and AIP – constantly exhibited the Pareto principle. The differences were not significant according to time change. The two multi-discipline databases – Elsevier SD and Wiley Blackwell – fluctuated more moderately than the two single-discipline databases – EBSCO BSP and AIP. Multi-discipline and single-discipline databases showed some differences in Pareto principle performance; however, these differences were not remarkable. Originality/value The Pareto principle confirmed that there were frequent and infrequent downloads of e-journals from e-journal databases. It was of great importance to analyse these to improve digital resources acquisition and user service.

Optimization of the Convolutional Neural Networks for Automatic Detection of Skin Cancer

“…The remaining 20% was used as test sets. This division is known as the Pareto principle (80/20 rule) that states that, for many events, roughly 80% of the effects come from 20% of the causes [50]. For determining what images should be included in the training, validating, or testing part, they were selected randomly.…”