Monitoring Large Scale Production Processes Using a Rule-Based Visualization Recommendation System

Data visualization plays an important role in the analysis of data and the identification of insights and characteristics within the dataset. Current visualization tools suffer from limitations with respect to the number of dimensions that can be displayed simultaneously. This often results in the inability to highlight hidden patterns or trends, affecting the analysts’ decision-making capabilities. In this scenario, it becomes imperative to propose systems that recommend useful insights to guide users to understand and explore data more efficiently. Addressing this problem, we propose a rule-based visualization recommendation(VizRec) system which caters for the research goal of providing an intelligent assistant that can guide users directly to relevant insights in the data. Our proposed VizRec system automates aspects of visualization design and recommends visualizations incorporating both the data characteristics and the diverse tasks representing users’ goals and intents using a knowledge-based rule engine. To ensure the correctness of knowledge-based rules and the dynamic nature of the rule engine, a formalization theory is proposed. We implemented our proposed model into a working tool for the exploration of complex production data generated from the manufacturing processes for the German excellence cluster “Internet of Production”. Our tool was able to generate recommendations capable of visualizing data insights regardless of the domain it is used in. The existing systems face several shortcomings ranging from the number of dimensions they can handle to the limited number of supported visualizations. The recommended visualizations derived from our proposed system were able to mitigate such challenges by adopting a generic rule-based approach incorporating visualizations handling multidimensional data. We evaluated our system in multiple fields, including the engineering domain, where production systems datasets are used to achieve user-intended tasks and gain valuable insights. © 2022, The Author(s).