Virtually all techniques, developed in the area of process mining, assume the input event data to be discrete, and, at a relatively high level (i.e., close to the business-level). However, in many cases, the event data generated during the execution of a process is at a much lower level of abstraction, e.g., sensor data. Hence, in this paper, we present a novel technique that allows us to translate sensor data into higher-level, discrete event data, thus enabling existing process mining techniques to work on data tracked at a sensory level. Our technique discretises the observed sensor data into activities by applying unsupervised learning in the form of clustering. Furthermore, we refine the observed sequences by deducing imperative sub-models for the observed discretised data, i.e., allowing us to identify concurrency and interleaving within the data. We evaluated the approach by comparing the obtained model quality for several clustering techniques on a publicly available data-set in a smart home scenario. Our results show that applying our framework combined with a clustering technique yields results on data that otherwise would not be suitable for process discovery.

Event logs recorded during the execution of business processes constitute a valuable source of information. Applying process mining techniques to them, event logs may reveal the actual process execution and enable reasoning on quantitative or qualitative process properties. However, event logs often contain sensitive information that could be related to individual process stakeholders through background information and cross-correlation. We therefore argue that, when publishing event logs, the risk of such re-identification attacks must be considered. In this paper, we show how to quantify the re-identification risk with measures for the individual uniqueness in event logs. We also report on a large-scale study that explored the individual uniqueness in a collection of publicly available event logs. Our results suggest that potentially up to all of the cases in an event log may be re-identified, which highlights the importance of privacy-preserving techniques in process mining.

From the study of numerical and structural chromosomal abnormalities, there is convincing evidence and accumulating information of a direct karyotype to phenotype correlation. Knowledge of phenotypic consequences of a specific chromosomal imbalance is important for genetic counseling and prenatal diagnosis. However, for unbalanced non-Robertsonian translocations a precise karyotype to phenotype correlation is difficult to predict for several reasons: (I) unbalanced non-Robertsonian translocations are rare, (II) the published case reports are often not age-matched, (III) varying breakpoints result in different lengths of the monosomic and trisomic segments and therefore the phenotype will depend on additional genes present or the loss of coding regions, and (IV) the combination of the same trisomy with different monosomies, or vice versa, can result in diverging phenotypes. Therefore, the study of the karyotype to phenotype correlation in affected relatives of the same age and the identical unbalanced translocation provides a good model to investigate phenotypic consequences of a specific genetic imbalance. We report of two second trimester fetuses with the identical major partial trisomy 9 (9pter-9q22.2) and minor partial trisomy 7 (q35-qter) resulting from a familial translocation (7;9)(q35;q22.2)mat. One fetus presented with a Dandy-Walker malformation, polymicrogyria, and mild dysmorphic features, whereas the other fetus showed unilateral cleft lip and palate without cerebral anomalies. Potential mechanisms for this different phenotypic expression of the same unbalanced translocation resulting in partial trisomy 9 and 7 in the two cousins and possible consequences for genetic counseling and prenatal diagnosis are discussed.