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has been cited by the following article:

• TITLE: Mining Metrics for Enhancing E-Commerce Systems User Experience

  AUTHORS: Antonia Stefani

  KEYWORDS: E-Commerce, Web Metrics, Quality Attributes, Data Mining, Association Rules, Evaluation Framework, Taxonomy, Ontology, ISO25010

  JOURNAL NAME: Intelligent Information Management, Vol.14 No.1, January 27, 2022

  ABSTRACT: The diversity of e-commerce Business to Consumer systems and the significant increase in their use during the COVID-19 pandemic as a one of the primary channels of retail commerce, has made all the most important the need to measuring their quality using practical methods. This paper presents a quality evaluation framework for web metrics that are B2C specific. The framework uses three dimensions based on end-user interaction categories, metrics internal specs and quality sub-characteristics as defined by ISO25010. Beginning from the existing large corpus of general-purpose web metrics, e-commerce specific metrics are chosen and categorized. Analysis results are subjected to a data mining analysis to provide association rules between the various dimensions of the framework. Finally, an ontology that corresponds to the framework is developed to answer to complicated questions related to metrics use and to facilitate the production of new, user defined meta-metrics.