Advanced web analytics tool for mouse tracking and real-time data processing

Čegan, Lukáš; Filip, Petr

doi:10.1109/informatics.2017.8327288

Cited by 12 publications

(8 citation statements)

References 7 publications

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“…The article by Cegan and Filip describes how user behavior monitoring allows detecting bottlenecks in the web environment, as well as in technical equipment and infrastructure components. The authors propose a new method for collecting mouse-clicking data based on real-time data transformation to convert discrete position data to system functions to optimize compression and analysis [14]. The usage of functional tests to assess the infrastructure and site reliability as a way to validate site operations and optimization techniques is described in Elmsheuser et al's study [15].…”

Section: Literature Reviewmentioning

confidence: 99%

User-Engagement Score and SLIs/SLOs/SLAs Measurements Correlation of E-Business Projects Through Big Data Analysis

et al. 2020

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The Covid-19 crisis lockdown caused rapid transformation to remote working/learning modes and the need for e-commerce-, web-education-related projects development, and maintenance. However, an increase in internet traffic has a direct impact on infrastructure and software performance. We study the problem of accurate and quick web-project infrastructure issues/bottleneck/overload identification. The research aims to achieve and ensure the reliability and availability of a commerce/educational web project by providing system observability and Site Reliability Engineering (SRE) methods. In this research, we propose methods for technical condition assessment by applying the correlation of user-engagement score and Service Level Indicators (SLIs)/Service Level Objectives (SLOs)/Service Level Agreements (SLAs) measurements to identify user satisfaction types along with the infrastructure state. Our solution helps to improve content quality and, mainly, detect abnormal system behavior and poor infrastructure conditions. A straightforward interpretation of potential performance bottlenecks and vulnerabilities is achieved with the developed contingency table and correlation matrix for that purpose. We identify big data and system logs and metrics as the central sources that have performance issues during web-project usage. Throughout the analysis of an educational platform dataset, we found the main features of web-project content that have high user-engagement and provide value to services’ customers. According to our study, the usage and correlation of SLOs/SLAs with other critical metrics, such as user satisfaction or engagement improves early indication of potential system issues and avoids having users face them. These findings correspond to the concepts of SRE that focus on maintaining high service availability.

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Section: Literature Reviewmentioning

confidence: 99%

User-Engagement Score and SLIs/SLOs/SLAs Measurements Correlation of E-Business Projects Through Big Data Analysis

et al. 2020

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“…Em [15]é proposta uma solução para identificação dé areas de interesse de usuários em páginas Web através do rastreamento do mouse, que realiza a conversão de posições de mouse em funções matemáticas predefinidas. Os resultados apresentam redução significativa de dados transmitidos do cliente para o servidor, o que levaà economia expressiva dos recursos do sistema no lado do servidor.…”

Section: Trabalhos Correlatosunclassified

Metodologia Baseada em Agrupamento de Rastreamento de Mouse e Olhar para Melhoria da Experiência do Usuário

Aviz¹,

Souza²,

Seruffo³

et al. 2020

Anais Do 14. Congresso Brasileiro De Inteligência Computacional

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Com a expansão de sistemas em plataforma web, tornou-se crítico o desenvolvimento de novas ferramentas e metodologias de avaliação da experiência do usuário (UX) para tornar tais sistemas mais intuitivos. Técnicas como rastreamento de mouse e rastreamento de olhos geram dados de interação que podem ser usados para melhorar o layout e a usabilidade destes sistemas. Além disso, recentemente aárea de desenvolvimento de sistemas passou a adotar Interfaces de Usuários Adaptativas (IUA), visando aprimorar em tempo real a UX. Nesse contexto, este artigo propõe uma metodologia para capturar e analisar os dados de comportamento de um grupo de usuários e sugerir a adaptação de um sistema web. Nesta análise foi utilizado o método de clusterização k-means. Os resultados sugerem que a metodologia pode auxiliar em mudanças automatizadas para melhoria da UX. Keywords-avaliação de experiência do usuário; rastreamento do mouse; rastreamento do olhar; interface de usuários adaptativas; agrupamento.

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“…As an additional approach for UX evaluation using the mouse-tracking technique, in [7] a solution for capturing the mouse movements of users on Web pages to identify areas of interest was proposed. The application was developed to process client server requests quickly and thus optimizes server resources.…”

Section: A User Experience Evaluationmentioning

confidence: 99%

User Experience Evaluation Using Mouse Tracking and Artificial Intelligence

et al. 2019

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Business platform models frequently require continuous adaptation and agility to allow new experiences to be created and delivered to customers. To understand user behavior in online systems, researchers have taken advantage of a combination of traditional and recently developed analysis techniques. Earlier studies have shown that user behavior monitoring data, as obtained by mouse tracking, can be utilized to improve user experience (UX). Many mouse-tracking solutions exist; however, the vast majority is proprietary, and open-source packages do not provide the resources and data needed to support UX research. Thus, this paper presents: 1) the development of an interaction monitoring application titled Artificial Intelligence and Mouse Tracking-based User eXperience Tool (AIMT-UXT); 2) the validation of the tool in a case study conducted on the Website of the Brazilian Federal Revenue Service (BFR); 3) the definition of a new relationship pattern of variables that determine user behavior; 4) the construction of a fuzzy inference system for measuring user performance using the defined variables and the data captured in the case study; and 5) the application of a clustering algorithm to complement the analysis. A comparison of the results of the applied quantitative methodologies indicates that the developed framework was able to infer UX scores similar to those reported by users in questionnaires.

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Advanced web analytics tool for mouse tracking and real-time data processing

Cited by 12 publications

References 7 publications

User-Engagement Score and SLIs/SLOs/SLAs Measurements Correlation of E-Business Projects Through Big Data Analysis

User-Engagement Score and SLIs/SLOs/SLAs Measurements Correlation of E-Business Projects Through Big Data Analysis

Metodologia Baseada em Agrupamento de Rastreamento de Mouse e Olhar para Melhoria da Experiência do Usuário

User Experience Evaluation Using Mouse Tracking and Artificial Intelligence

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