Process Mining of Programmable Logic Controllers: Input/Output Event Logs

This paper presents an approach to model an unknown Ladder Logic based Programmable Logic Controller (PLC) program consisting of Boolean logic and counters using Process Mining techniques. First, we tap the inputs and outputs of a PLC to create a data flow log. Second, we propose a method to translate the obtained data flow log to an event log suitable for Process Mining. In a third step, we propose a hybrid Petri net (PN) and neural network approach to approximate the logic of the actual underlying PLC program. We demonstrate the applicability of our proposed approach on a case study with three simulated scenarios

Semantic process mining tools: core building blocks

Process mining aims at discovering new knowledge based on information hidden in event logs. Two important enablers for such analysis are powerful process mining techniques and the omnipresence of event logs in today's information systems. Most information systems supporting (structured) business processes (e.g. ERP, CRM, and workflow systems) record events in some form (e.g. transaction logs, audit trails, and database tables). Process mining techniques use event logs for all kinds of analysis, e.g., auditing, performance analysis, process discovery, etc. Although current process mining techniques/tools are quite mature, the analysis they support is somewhat limited because it is purely based on labels in logs. This means that these techniques cannot benefit from the actual semantics behind these labels which could cater for more accurate and robust analysis techniques. Existing analysis techniques are purely syntax oriented, i.e., much time is spent on filtering, translating, interpreting, and modifying event logs given a particular question. This paper presents the core building blocks necessary to enable semantic process mining techniques/tools. Although the approach is highly generic, we focus on a particular process mining technique and show how this technique can be extended and implemented in the ProM framework tool

Coarse-graining the Dynamics of a Driven Interface in the Presence of Mobile Impurities: Effective Description via Diffusion Maps

Developing effective descriptions of the microscopic dynamics of many physical phenomena can both dramatically enhance their computational exploration and lead to a more fundamental understanding of the underlying physics. Previously, an effective description of a driven interface in the presence of mobile impurities, based on an Ising variant model and a single empirical coarse variable, was partially successful; yet it underlined the necessity of selecting additional coarse variables in certain parameter regimes. In this paper we use a data mining approach to help identify the coarse variables required. We discuss the implementation of this diffusion map approach, the selection of a similarity measure between system snapshots required in the approach, and the correspondence between empirically selected and automatically detected coarse variables. We conclude by illustrating the use of the diffusion map variables in assisting the atomistic simulations, and we discuss the translation of information between fine and coarse descriptions using lifting and restriction operators.Comment: 28 pages, 10 figure

Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?

After being collected for patient care, Observational Health Data (OHD) can further benefit patient well-being by sustaining the development of health informatics and medical research. Vast potential is unexploited because of the fiercely private nature of patient-related data and regulations to protect it. Generative Adversarial Networks (GANs) have recently emerged as a groundbreaking way to learn generative models that produce realistic synthetic data. They have revolutionized practices in multiple domains such as self-driving cars, fraud detection, digital twin simulations in industrial sectors, and medical imaging. The digital twin concept could readily apply to modelling and quantifying disease progression. In addition, GANs posses many capabilities relevant to common problems in healthcare: lack of data, class imbalance, rare diseases, and preserving privacy. Unlocking open access to privacy-preserving OHD could be transformative for scientific research. In the midst of COVID-19, the healthcare system is facing unprecedented challenges, many of which of are data related for the reasons stated above. Considering these facts, publications concerning GAN applied to OHD seemed to be severely lacking. To uncover the reasons for this slow adoption, we broadly reviewed the published literature on the subject. Our findings show that the properties of OHD were initially challenging for the existing GAN algorithms (unlike medical imaging, for which state-of-the-art model were directly transferable) and the evaluation synthetic data lacked clear metrics. We find more publications on the subject than expected, starting slowly in 2017, and since then at an increasing rate. The difficulties of OHD remain, and we discuss issues relating to evaluation, consistency, benchmarking, data modelling, and reproducibility.Comment: 31 pages (10 in previous version), not including references and glossary, 51 in total. Inclusion of a large number of recent publications and expansion of the discussion accordingl