15 research outputs found
Visual Analysis of Industrial Multivariate Time-Series Data : Effective Solution to Maximise Insights from Blow Moulding Machine Sensory Data
No full textDevelopments in the field of data analytics provides a boost for small-sized factories. These factories are eager to take full advantage of the potential insights in the remotely collected data to minimise cost and maximise quality and profit. This project aims to process, cluster and visualise sensory data of a blow moulding machine in a plastic production factory. In collaboration with Lean Automation, we aim to develop a data visualisation solution to enable decision-makers in a plastic factory to improve their production process. We will investigate three different aspects of the solution: methods for processing multivariate time-series data, clustering approaches for the sensory-data cultivated, and visualisation techniques that maximises production process insights. We use a formative evaluation method to develop a solution that meets partners' requirements and best practices within the field. Through building the MTSI dashboard tool, we hope to answer questions on optimal techniques to represent, cluster and visualise multivariate time series data.
Visual Analysis of Industrial Multivariate Time-Series Data : Effective Solution to Maximise Insights from Blow Moulding Machine Sensory Data
No full textDevelopments in the field of data analytics provides a boost for small-sized factories. These factories are eager to take full advantage of the potential insights in the remotely collected data to minimise cost and maximise quality and profit. This project aims to process, cluster and visualise sensory data of a blow moulding machine in a plastic production factory. In collaboration with Lean Automation, we aim to develop a data visualisation solution to enable decision-makers in a plastic factory to improve their production process. We will investigate three different aspects of the solution: methods for processing multivariate time-series data, clustering approaches for the sensory-data cultivated, and visualisation techniques that maximises production process insights. We use a formative evaluation method to develop a solution that meets partners' requirements and best practices within the field. Through building the MTSI dashboard tool, we hope to answer questions on optimal techniques to represent, cluster and visualise multivariate time series data.
Visual analysis of blow molding machine multivariate time series data
Get PDFABSTRACT: The recent development in the data analytics field provides a boost in production for modern industries. Small-sized factories intend to take full advantage of the data collected by sensors used in their machinery. The ultimate goal is to minimize cost and maximize quality, resulting in an increase in profit. In collaboration with domain experts, we implemented a data visualization tool to enable decision-makers in a plastic factory to improve their production process. The tool is an interactive dashboard with multiple coordinated views supporting the exploration from both local and global perspectives. In summary, we investigate three different aspects: methods for preprocessing multivariate time series data, clustering approaches for the already refined data, and visualization techniques that aid domain experts in gaining insights into the different stages of the production process. Here we present our ongoing results grounded in a human-centered development process. We adopt a formative evaluation approach to continuously upgrade our dashboard design that eventually meets partners’ requirements and follows the best practices within the field. We also conducted a case study with a domain expert to validate the potential application of the tool in the real-life context. Finally, we assessed the usability and usefulness of the tool with a two-layer summative evaluation that showed encouraging results. GRAPHICAL ABSTRACT: [Image: see text
Agritology: A Decision Support System for Local Farmers in Malta and Palestine
No full textWe aim to provide a platform to act as a single-source of information to support decision making in the growth and cultivation of crops in local farms. The primary objective of our work is to promote sustainable development, by encouraging local farmers in Malta and Palestine to adopt seasonal and organic farming, in line with the second Sustainable Development Goal (SDG2) of the United Nations. Through SDG2, the United Nations aims to "end hunger, achieve food security and improved nutrition and promote sustainable agriculture". The secondary objective of this study is to promote the local language and terminology. Accordingly, the information on the dashboard will be displayed in English, Maltese and Arabic
Angeleitete visuelle Analytik für die Entscheidungsfindung bei Unsicherheit
Get PDFVisual Analytics (VA) has emerged from the need to optimize decision making by involving human reasoning in sense making. The development of VA has been facilitated by significant technological advances in modern computer graphics and data processing capabilities. Involving humans in the loop aims to address high-risk scenarios where artificial intelligence (AI) automated approaches are insufficient. One active area of research with VA is the development of methods that enable the user to make efficient and effective decisions under high uncertainty. Yet, the field of VA research has not fully understood how user attitude, namely trust and confidence, interplay in VA decision making under uncertainty. Properties of the user attitude play a crucial role in optimizing VA decision making, but they are challenging to externalize and evaluate. For instance, user confidence in their decision emerges as an important indicator of effectiveness when the correctness of the decision cannot be measured. In this dissertation, we explore the use of guidance techniques to address uncertainties in VA decision making, focusing on scenarios where the correctness of decisions cannot be definitively established. Throughout this work, we learned that a multidimensional guidance mechanism can address uncertainties more effectively when uncertainties are challenging to quantify and visualize, especially in the case of subjective uncertainty. However, evaluating the effectiveness of guidance approaches requires a more comprehensive analysis of the interplay between trust and confidence within the sense-making process. Using provenance networks and SNA metrics can provide a more reliable and comprehensive assessment of user confidence, indicating that such approaches can be employed to support co-adaptive guidance
Visual Analysis of Industrial Multivariate Time Series
No full textThe recent development in the data analytics field provides a boost in production for modern industries. Small-sized factories intend to take full advantage of the data collected by sensors used in their machinery. The ultimate goal is to minimize cost and maximize quality, resulting in an increase in profit. In collaboration with domain experts, we implemented a data visualization tool to enable decision-makers in a plastic factory to improve their production process. We investigate three different aspects: methods for preprocessing multivariate time series data, clustering approaches for the already refined data, and visualization techniques that aid domain experts in gaining insights into the different stages of the production process. Here we present our ongoing results grounded in a human-centered development process. We adopt a formative evaluation approach to continuously upgrade our dashboard design that eventually meets partners' requirements and follows the best practices within the field.</p
