The Internet-of-Things Meets Business Process Management: Mutual Benefits and Challenges

The Internet of Things (IoT) refers to a network of connected devices collecting and exchanging data over the Internet. These things can be artificial or natural, and interact as autonomous agents forming a complex system. In turn, Business Process Management (BPM) was established to analyze, discover, design, implement, execute, monitor and evolve collaborative business processes within and across organizations. While the IoT and BPM have been regarded as separate topics in research and practice, we strongly believe that the management of IoT applications will strongly benefit from BPM concepts, methods and technologies on the one hand; on the other one, the IoT poses challenges that will require enhancements and extensions of the current state-of-the-art in the BPM field. In this paper, we question to what extent these two paradigms can be combined and we discuss the emerging challenges

Multi-perspective process mining

Process mining methods analyze an organization’s processes by using process execution data. During the handling of a process instance data about the execution of activities is recorded. Process mining uses such data to gain insights about the real execution of processes. In this thesis, we address research challenges in which a multi-perspective view on processes is needed and that look beyond the control-flow perspective, which defines the sequence of activities of a process. We consider problems in which multiple interacting process perspectives — in particular control-flow, data, resources, time, and functions — are considered together. The contributed methods span several types of process mining: two are concerned with conformance checking, two are process discovery techniques, and one is a decision mining method. All methods have been implemented, evaluated, and applied in the context of four case studies.</p

Food habits and patterns of the Multiracial Population of Cape Town

Dietitians and nutritionists today realise the importance of knowing about peoples' food habits and patterns to be able to understand their dietary and medical problems more readily. It is my intention to study the current and traditional food habits and patterns of the various population groups by interviewing as many members of each group as possible on: - daily eating patterns - cooking methods - traditional foods - taboos, feasts and fasts In the introduction the discussion will focus on the origin of food habits and patterns and the influencing factors in a wider context

Analysis of Tyrosine Kinase Inhibitor-Mediated Decline in Contractile Force in Rat Engineered Heart Tissue

Introduction Left ventricular dysfunction is a frequent and potentially severe side effect of many tyrosine kinase inhibitors (TKI). The mode of toxicity is not identified, but may include impairment of mitochondrial or sarcomeric function, autophagy or angiogenesis, either as an on-target or off-target mechanism. Methods and Results We studied concentration-response curves and time courses for nine TKIs in three-dimensional, force generating engineered heart tissue (EHT) from neonatal rat heart cells. We detected a concentration- and time-dependent decline in contractile force for gefitinib, lapatinib, sunitinib, imatinib, sorafenib, vandetanib and lestaurtinib and no decline in contractile force for erlotinib and dasatinib after 96 hours of incubation. The decline in contractile force was associated with an impairment of autophagy (LC3 Western blot) and appearance of autophagolysosomes (transmission electron microscopy). Conclusion This study demonstrates the feasibility to study TKI-mediated force effects in EHTs and identifies an association between a decline in contractility and inhibition of autophagic flux

Analyzing the trajectories of patients with sepsis using process mining

Process mining techniques analyze processes based on event data. We analyzed the trajectories of patients in a Dutch hospital from their registration in the emergency room until their discharge. We considered a sample of 1050 patients with symptoms of a sepsis condition, which is a life-threatening condition. We extracted an event log that includes events on activities in the emergency room, admission to hospital wards, and discharge. The event log was enriched with data from laboratory tests and triage checklists.We try to automatically discover a process model of the patient trajectories, we check conformance to medical guidelines for sepsis patients, and visualize the flow of patients on a de-jure process model. The lessons-learned from this analysis are: (1) process mining can be used to clarify the patient flow in a hospital; (2) process mining can be used to check the daily clinical practice against medical guidelines; (3) process discovery methods may return unsuitable models that are difficult to understand for stakeholders; and (4) process mining is an iterative process, e.g., data quality issues are often discovered and need to be addressed

Unsupervised event abstraction using pattern abstraction and local process models

Process mining analyzes business processes based on events stored in event logs. However, some recorded events may correspond to activities on a very low level of abstraction. When events are recorded on a too low level of abstraction, process discovery methods tend to generate overgeneralizing process models. Grouping low-level events to higher level activities, i.e., event abstraction, can be used to discover better process models. Existing event abstraction methods are mainly based on common sub-sequences and clustering techniques. In this paper, we propose to first discover local process models and, then, use those models to lift the event log to a higher level of abstraction. Our conjecture is that process models discovered on the obtained high-level event log return process models of higher quality: their fitness and precision scores are more balanced. We show this with preliminary results on several real-life event logs

Isogenic Pairs of hiPSC-CMs with Hypertrophic Cardiomyopathy/LVNC-Associated ACTC1 E99K Mutation Unveil Differential Functional Deficits

Hypertrophic cardiomyopathy (HCM) is a primary disorder of contractility in heart muscle. To gain mechanistic insight and guide pharmacological rescue, this study models HCM using isogenic pairs of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) carrying the E99K-ACTC1 cardiac actin mutation. In both 3D engineered heart tissues and 2D monolayers, arrhythmogenesis was evident in all E99K-ACTC1 hiPSC-CMs. Aberrant phenotypes were most common in hiPSC-CMs produced from the heterozygote father. Unexpectedly, pathological phenotypes were less evident in E99K-expressing hiPSC-CMs from the two sons. Mechanistic insight from Ca2+ handling expression studies prompted pharmacological rescue experiments, wherein dual dantroline/ranolazine treatment was most effective. Our data are consistent with E99K mutant protein being a central cause of HCM but the three-way interaction between the primary genetic lesion, background (epi)genetics, and donor patient age may influence the pathogenic phenotype. This illustrates the value of isogenic hiPSC-CMs in genotype-phenotype correlations

Unsupervised event abstraction using pattern abstraction and local process models

Process mining analyzes business processes based on events stored in event logs. However, some recorded events may correspond to activities on a very low level of abstraction. When events are recorded on a too low level of abstraction, process discovery methods tend to generate overgeneralizing process models. Grouping low-level events to higher level activities, i.e., event abstraction, can be used to discover better process models. Existing event abstraction methods are mainly based on common sub-sequences and clustering techniques. In this paper, we propose to first discover local process models and, then, use those models to lift the event log to a higher level of abstraction. Our conjecture is that process models discovered on the obtained high-level event log return process models of higher quality: their fitness and precision scores are more balanced. We show this with preliminary results on several real-life event logs

Revealing Work Practices in Hospitals Using Process Mining

In order to improve health care processes (both in terms of quality and efficiency), we do need insight into how these processes are actually executed in reality. Interviewing health personnel and observing them in their work, are proven field-work techniques for gaining this insight. In this paper, we will introduce a complementary technique. This technique, called process mining, is based on the automatic analysis of digital events, registered in different information systems that support clinical work. Based on an event log, process mining can help in constructing a model of the process (discovery) or with checking to which extend an actual process confirms to a prescriptive model of it (conformance). This paper will briefly discuss two examples, which illustrate the use of process mining.submittedVersion© 2018. This is the authors' manuscript to the chapter. The final publication is available at IOS Press through http://dx.doi.org/10.3233/978-1-61499-852-5-28