Using context overlays to analyse the role of a priori information with Process Mining

Abstract

Notwithstanding the significant advances in contextaware computing in pervasive computing and self-adaptive systems, there is still much more to be desired in providing better context services. The number of sensors deployed world-wide increases very rapidly. The Internet of Things, amongst others, generates vast amounts of data of many different data types. How data are used is essential to improve user experience and efficiencies of the systems in which they occur. We explain how familiar concepts of Process Mining strengthen generalised sensor context services. We present a laboratory case to explain the approach. By way of a real-world example, we confirm the viability of using Process Mining to strengthen context-aware computing.

Machine Learning for Digital Twins to Predict Responsiveness of Cyber-Physical Energy Systems

“…Process Mining techniques seem an appropriate passive learning technique [12], [13], but do not easily address the state-space problem that many algorithms face in the way deep learning implicitly does. Instead, process discovery from logs and conformance checks need specialized algorithms.…”

Mobile Cloud Business Process Management System for the Internet of Things

“…Commonly, a system can utilise the rule-based schemes mentioned previously in Section 3.3.3 or utilise the machine-learning schemes based on Bayesian network, Markov chain to achieve context-awareness. In fact, recent research trends in context-awareness have started applying process mining techniques [139,140,141]. Since process mining is a popular technique in BPM [11], those process mining-based context recognition schemes can be quite promising in supporting context-aware BPMS4IoT.…”