An Interactive Visualization Web Application for Industrial-Focused Statistical Process Control Analysis

Statistical process control (SPC) implementation plays a major role in quality assurance during the manufacturing process. Nevertheless, the adoption rate of SPC commercial software solutions is unsatisfactory in most Malaysian manufacturing companies due to high software subscription costs and difficulties in applying the software without proper know-how, guidance, and training. This study proposes the development of a purpose-built interactive data visualization web application for rapid SPC analysis in the manufacturing industry using open-sourced software packages. An agile software development model is applied as the software development methodology. In the requirement phase, an interview session was conducted to identify project requirements among stakeholders, i.e. industrial practitioners that are involved with SPC analysis. Based on the feedback and expectations from stakeholders, a design of a web application for SPC analysis that incorporates interactive parameter settings and automated reporting was proposed. The web application was developed using the R programming language and the Shiny package library, and deployed at ShinyApps.io, a web service provider. For evaluation, a usability testing procedure was designed and conducted with five industrial SPC practitioners to determine the usefulness of the web application. The outcome of the usability testing indicated positive results and feedback from evaluators. In conclusion, the developed web-app can assist users, particularly from the manufacturing industry sectors, to perform fast SPC data analytics, visualization, and reporting with ease

Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

Assessing the Quality of Mobile Graphical User Interfaces Using Multi-Objective Optimization

Aesthetic defects are a violation of quality attributes that are symptoms of bad interface design programming decisions. They lead to deteriorating the perceived usability of mobile user interfaces and negatively impact the Users eXperience (UX) with the mobile app. Most existing studies relied on a subjective evaluation of aesthetic defects depending on end-users feedback, which makes the manual evaluation of mobile user interfaces human-centric, time-consuming, and error-prone. Therefore, recent studies have dedicated their effort to focus on the definition of mathematical formulas that each targets a specific structural quality of the interface. As the UX is tightly dependent on the user profile, the combi-nation and calibration of quality attributes, formulas, and users characteristics, when defining a defect, is not straightforward. In this context, we propose a fully automated framework which combines literature quality attributes with the users profile to identify aesthetic defects of MUI. More precisely, we consider the mobile user interface evaluation as a multi-objective optimization problem where the goal is to maximize the number of detected violations while minimizing the detection complexity of detection rules and enhancing the interfaces overall quality in means