Online Conformance Checking to Support Human Behavior Study (Extended Abstract) Gemma Di Federico Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby, Denmark gdfe@dtu.dk Abstract—Human behavior could be represented in form of a schema. In order to succeed in such goal, in the following process model. Internet of Things systems allow the collection of section I elaborate the research questions. the behavior in the reality, and the data are then processed by Process Mining algorithms. To achieve this integration, several II. R ESEARCH QUESTIONS challenges must be faced. The objective of this PhD project is The overall process could be divided into three main topics to adapt online conformance checking to be able to deal with human-related processes. which are the data collection, the process modeling and the mining. Firstly, there is a simulated environment, equipped I. P ROBLEM C ONTEXT AND M OTIVATION with IoT sensors, that captures the behavior of a patient in The project aims to contribute into the integration between form of sensors measurements. Secondly, data collected must Internet of Things (IoT) and Business Process Management, be organized in an event log that is suitable for discovery combining the potentiality of sensor data toward the imple- algorithms which are used to derive a reference model. This mentation of dynamic and enriched models with the objective model represents the typical behavior of the patient. The to support conformance checking techniques. In particular, last is the conformance, in which the behavior in the reality the project focuses on the modeling of human behavior [8] is compared with the reference models. The derivation and by means of process mining techniques, obtaining data from replication of processes is what process mining is intended to IoT devices. There is a desire to dwell on aspects relating to do. Complications emerge as the nature of human processes the use of conformance checking techniques in the context of differs from the structured one that are typically dealt with. behavioral study. Observing human behavior for a long time, RQ1 - What is the most appropriate way to abstract can lead to the collection of various information that can be activities from a stream of sensor measurements? used in a plenty of application fields [10]. The use case of Are semi-supervised approaches the proper manner my PhD project is the dementia disease, under the healthcare to organize sensor data into an event log? field. Patients effected by dementia usually fit into a very tight Data produced by IoT systems are unstructured and of a routine. The repetition of routines helps people to control their different level of granularity compared to the event log ex- world in a predictable manner, adding a sense of order to the pected by Process Mining algorithms. There is a need to days. Repetition of activities is a comfort zone. Establishing bring sensors measurements and model to the same level a routine contributes to a sense of security and peace, which of abstraction. Actually, there is not a standard technique helps to reduce agitation and troublesome behavior [6]. In the to organize sensors data into a structured event log. In the middle to later stages of most types of dementia, a person may literature there are several approaches of event abstraction[15], start to behave differently. Given that, the breaking of a routine both supervised or unsupervised. The unsupervised techniques could be symptom of a worsening of the disease [12]. The idea group fine-granular events on the basis of strong re-occurrence is to observe the behavior of people living with dementia to of patterns [9]. In the supervised event abstraction techniques, identify significant variations in the behavior. To do so, an authors propose a set of dimensions based on their understand- habits model is derived for each patient and it is continuously ing of the field and classifying them accordingly [7]. After a compared with the actual behavior to find discrepancies. IoT first investigation of the available techniques, we would like systems are used to capture the behavior in the reality, while to focus in the between, i.e., on semi-supervised approaches. online conformance checking techniques to compare the actual A fully supervised abstraction could be influenced by human behavior with the modeled (and expected) one. errors, since it is an expert who must identify every single The work in this PhD project also aims to be generalized, activity starting from a series of proposed configurations. On so that it can be reused in other contexts. My plan is to the other hand, an unsupervised technique does not allow extend Process Mining techniques to be applicable in IoT, to verify the way in which the labeling was carried out, or with the aim of contribute into verifying the compliance of how the sequence of actions for each activity was identified. the actual IoT system execution with the expected process Therefore, a semi-supervised approach could provide both Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). the right level of automation and allow us to oversee and there is no certainty that a Petri net has enough expressiveness contribute with knowledge to the activity recognition. Once to represent these processes. For this reason, the conformance the data has been structured in an event log, we need to focus algorithm must be able to deal with the process modeling on the process modeling language that is able to represent language selected, perhaps, also in this case, the algorithm human-related processes. should be adapted to respond to the process requirements. To RQ2 - Are current modeling languages able to conclude, given the large amount of data produced by IoT represent human behavior and their context? It may systems, it is desirable that the conformance is carried out be valid to consider stochastic models? online [2, 16]. Human related process differs from business one. Human R EFERENCES behavior evolves over the time [5] and it is influenced by [1] N. Banovic et al. “Modeling and understanding human the participant and by the context. The process is completely routine behavior”. In: Proc. of CHI. 2016, pp. 248–260. guided by the participants, who decide how and when to [2] A. 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