Controlled automated discovery of collections of business process models

Automated process discovery techniques aim at extracting process models from information system logs. Existing techniques in this space are effective when applied to relatively small or regular logs, but generate spaghetti-like and sometimes inaccurate models when confronted to logs with high variability. In previous work, trace clustering has been applied in an attempt to reduce the size and complexity of automatically discovered process models. The idea is to split the log into clusters and to discover one model per cluster. This leads to a collection of process models – each one representing a variant of the business process – as opposed to an all-encompassing model. Still, models produced in this way may exhibit unacceptably high complexity and low fitness. In this setting, this paper presents a two-way divide-and-conquer process discovery technique, wherein the discovered process models are split on the one hand by variants and on the other hand hierarchically using subprocess extraction. Splitting is performed in a controlled manner in order to achieve user-defined complexity or fitness thresholds. Experiments on real-life logs show that the technique produces collections of models substantially smaller than those extracted by applying existing trace clustering techniques, while allowing the user to control the fitness of the resulting models.

Beyond tasks and gateways: discovering BPMN models with subprocesses, boundary events and activity markers

Existing techniques for automated discovery of process models from event logs largely focus on extracting flat process models. In other words, they fail to exploit the notion of subprocess, as well as structured error handling and repetition constructs provided by contemporary process modeling notations, such as the Business Process Model and Notation (BPMN). This paper presents a technique for automated discovery of BPMN models containing subprocesses, interrupting and non-interrupting boundary events, and loop and multi-instance markers. The technique analyzes dependencies between data attributes associated with events, in order to identify subprocesses and to extract their associated logs. Parent process and subprocess models are then discovered separately using existing techniques for flat process model discovery. Finally, the resulting models and logs are heuristically analyzed in order to identify boundary events and markers. A validation with one synthetic and two real-life logs shows that process models derived using the proposed technique are more accurate and less complex than those derived with flat process model discovery techniques.

Optimising the cell source and biomaterials for the generation of vascularized adipose tissue in vivo

We initially described a rat chamber model with an inserted arteriovenous pedicle which spontaneously generates 3-dimensional vascularized connective tissue (Tanaka Y et al., Br J Plast Surg 2000; 53: 51-7). More recently we have developed a murine chamber model containing reconstituted basement membrane (Matrigel®) and FGF-2 that generates vascularized adipose tissue in vivo (Cronin K et al., Plast Reconstr Surg 2004; in press). We have extended this work to assess the cellular and matrix requirements for the Matrigel®- induced neo-adipogenesis. We found that chambers sealed to host fat were unable to grow new adipose tissue. In these chambers the Matrigel® became vascularized with maximal outgrowth of vessels extending to the periphery at 6 weeks. A small amount of adipose tissue was found adjacent to the vessels, most likely arising from periadventitial adipose tissue. In contrast, chambers open to interaction with endogenous adipose tissue showed abundant new fat, and partial exposure to adjacent adipose tissue clearly showed neo-adipogenesis only in this area. Addition of small amounts of free fat to the closed chamber containing Matrigel® was able to induce neo-adipogenesis. Addition of small pieces of human fat also caused neo-adipogenesis in immunocompromised (SCID) mice. Also, we found Matrigel® to induce adipogenesis of Lac-Z-tagged (Rosa-26) murine bone marrow-derived mesenchymal stem cells, and cells similar to these have been isolated from human adipose tissue. Given that Matrigel® is a mouse product and cannot be used in humans, we have started investigating alternative matrix scaffolds for adipogenesis such as the PDA-approved PLGA, collagen and purified components derived from Matrigel®, such as laminin-1. The optimal conditions for adipogenesis with these matrices are still being elucidated. In conclusion, we have demonstrated that a precursor cell source inside the chamber is essential for the generation of vascularized adipose tissue in vivo. This technique offers unique potential for the reconstruction of soft tissue defects and may enable the generation of site-specific tissue using the correct microenvironment.

Mining business process deviance : a quest for accuracy

This paper evaluates the suitability of sequence classification techniques for analyzing deviant business process executions based on event logs. Deviant process executions are those that deviate in a negative or positive way with respect to normative or desirable outcomes, such as non-compliant executions or executions that undershoot or exceed performance targets. We evaluate a range of feature types and classification methods in terms of their ability to accurately discriminate between normal and deviant executions both when deviances are infrequent (unbalanced) and when deviances are as frequent as normal executions (balanced). We also analyze the ability of the discovered rules to explain potential causes and contributing factors of observed deviances. The evaluation results show that feature types extracted using pattern mining techniques only slightly outperform those based on individual activity frequency. The results also suggest that more complex feature types ought to be explored to achieve higher levels of accuracy.

Detecting approximate clones in business process model repositories

Empirical evidence shows that repositories of business process models used in industrial practice contain significant amounts of duplication. This duplication arises for example when the repository covers multiple variants of the same processes or due to copy-pasting. Previous work has addressed the problem of efficiently retrieving exact clones that can be refactored into shared subprocess models. This article studies the broader problem of approximate clone detection in process models. The article proposes techniques for detecting clusters of approximate clones based on two well-known clustering algorithms: DBSCAN and Hi- erarchical Agglomerative Clustering (HAC). The article also defines a measure of standardizability of an approximate clone cluster, meaning the potential benefit of replacing the approximate clones with a single standardized subprocess. Experiments show that both techniques, in conjunction with the proposed standardizability measure, accurately retrieve clusters of approximate clones that originate from copy-pasting followed by independent modifications to the copied fragments. Additional experiments show that both techniques produce clusters that match those produced by human subjects and that are perceived to be standardizable.

Maternal psychological distress mediates the relationship between asthma and physician visits in a population-based sample of adolescents

Objective This study examined whether maternal psychological distress mediates the relationship between presence of adolescent asthma and number of physician visits, and whether the association between maternal psychological distress and physician visits is moderated by adolescent general health. Methods Data were obtained from the Mater University Study of Pregnancy and included 4025 adolescents. Path analysis was used to examine mediating and moderating effects. Results Maternal psychological distress was found to partially mediate the relationship between adolescent asthma and number of physician visits, accounting for 25% of the effect of adolescent asthma on physician visits (p = .046). There was no evidence to suggest that adolescent general health moderated the association between maternal psychological distress and physician visits (p = .093). Conclusions The findings suggest that maternal psychological distress is associated with increased physician visits, regardless of adolescents' general health. Lowering maternal psychological distress may serve to reduce health care utilization and costs among adolescents with asthma.

Assessment of the molecular structure of an intermediate member of the triplite-zwieselite mineral series: A raman and infrared study

The mineral series triplite-zwieselite with theoretical formula (Mn2+)2(PO4)(F)-(Fe2+)2(PO4)(F) from the El Criolo granitic pegmatite, located in the Eastern Pampean Ranges of Córdoba Province, was studied using electron microprobe, thermogravimetry, and Raman and infrared spectroscopy. The analysis of the mineral provided a formula of (Fe1.00, Mn0.85, Ca0.08, Mg0.06)∑2.00(PO4)1.00(F0.80, OH0.20)∑1.00. An intense Raman band at 981 cm−1 with a shoulder at 977 cm−1 is assigned to the ν1 symmetric stretching mode. The observation of two bands for the phosphate symmetric stretching mode offers support for the concept that the phosphate units in the structure of triplite-zwieselite are not equivalent. Low-intensity Raman bands at 1012, 1036, 1071, 1087, and 1127 cm−1 are assigned to the ν3 antisymmetric stretching modes. A set of Raman bands at 572, 604, 639, and 684 cm−1 are attributed to the ν4 out-of-plane bending modes. A single intense Raman band is found at 3508 cm−1 and is assigned to the stretching vibration of hydroxyl units. Infrared bands are observed at 3018, 3125, and 3358 cm−1 and are attributed to water stretching vibrations. Supplemental materials are available for this article. Go to the publisher's online edition of Spectroscopy Letters to view the supplemental file.

A recommendation system for predicting risks across multiple business process instances

This paper proposes a recommendation system that supports process participants in taking risk-informed decisions, with the goal of reducing risks that may arise during process execution. Risk reduction involves decreasing the likelihood and severity of a process fault from occurring. Given a business process exposed to risks, e.g. a financial process exposed to a risk of reputation loss, we enact this process and whenever a process participant needs to provide input to the process, e.g. by selecting the next task to execute or by filling out a form, we suggest to the participant the action to perform which minimizes the predicted process risk. Risks are predicted by traversing decision trees generated from the logs of past process executions, which consider process data, involved resources, task durations and other information elements like task frequencies. When applied in the context of multiple process instances running concurrently, a second technique is employed that uses integer linear programming to compute the optimal assignment of resources to tasks to be performed, in order to deal with the interplay between risks relative to different instances. The recommendation system has been implemented as a set of components on top of the YAWL BPM system and its effectiveness has been evaluated using a real-life scenario, in collaboration with risk analysts of a large insurance company. The results, based on a simulation of the real-life scenario and its comparison with the event data provided by the company, show that the process instances executed concurrently complete with significantly fewer faults and with lower fault severities, when the recommendations provided by our recommendation system are taken into account.

Dancing as Knowledge Production/Dancer as Researcher in Practice-led Research

This chapter discusses research undertaken for a PhD in dance, highlighting the oscillating and ambivalent nature of practice-led research methods. Holding an unusual position within the field of practice-led research in dance, the researcher adopted the role of dancer within four creative processes led by four choreographers. Although instigating and leading this research, the author produced new knowledge of dancing practices whilst being directed creatively by the choreographers in the research environment. In line with many definitions of practice-led research, the methods used were emergent from the research arena and responsive to the requirements of the project. Deeply imbricated in the research environment, the researcher used her embodied self as a research tool, as the one who participated, discovered and recorded. This outsider/insider role of researcher/participant produced a series of meta-positions, leading to a mixed-modal outcome that encompassed live performance, a movement treatise and a written thesis.

Noise Filtering of Process Execution Logs based on Outliers Detection

This paper presents a technique for the automated removal of noise from process execution logs. Noise is the result of data quality issues such as logging errors and manifests itself in the form of infrequent process behavior. The proposed technique generates an abstract representation of an event log as an automaton capturing the direct follows relations between event labels. This automaton is then pruned from arcs with low relative frequency and used to remove from the log those events not fitting the automaton, which are identified as outliers. The technique has been extensively evaluated on top of various auto- mated process discovery algorithms using both artificial logs with different levels of noise, as well as a variety of real-life logs. The results show that the technique significantly improves the quality of the discovered process model along fitness, appropriateness and simplicity, without negative effects on generalization. Further, the technique scales well to large and complex logs.

Fast and accurate business process drift detection

Business processes are prone to continuous and unexpected changes. Process workers may start executing a process differently in order to adjust to changes in workload, season, guidelines or regulations for example. Early detection of business process changes based on their event logs – also known as business process drift detection – enables analysts to identify and act upon changes that may otherwise affect process performance. Previous methods for business process drift detection are based on an exploration of a potentially large feature space and in some cases they require users to manually identify the specific features that characterize the drift. Depending on the explored feature set, these methods may miss certain types of changes. This paper proposes a fully automated and statistically grounded method for detecting process drift. The core idea is to perform statistical tests over the distributions of runs observed in two consecutive time windows. By adaptively sizing the window, the method strikes a trade-off between classification accuracy and drift detection delay. A validation on synthetic and real-life logs shows that the method accurately detects typical change patterns and scales up to the extent it is applicable for online drift detection.

Log delta analysis: Interpretable differencing of Business process event logs

This paper addresses the problem of identifying and explaining behavioral differences between two business process event logs. The paper presents a method that, given two event logs, returns a set of statements in natural language capturing behavior that is present or frequent in one log, while absent or infrequent in the other. This log delta analysis method allows users to diagnose differences between normal and deviant executions of a process or between two versions or variants of a process. The method relies on a novel approach to losslessly encode an event log as an event structure, combined with a frequency-enhanced technique for differencing pairs of event structures. A validation of the proposed method shows that it accurately diagnoses typical change patterns and can explain differences between normal and deviant cases in a real-life log, more compactly and precisely than previously proposed methods.

Characterization of the sulphate mineral coquimbite, a secondary iron sulphate from Javier Ortega mine, Lucanas Province, Peru – Using infrared, Raman spectroscopy and thermogravimetry

The mineral coquimbite has been analysed using a range of techniques including SEM with EDX, thermal analytical techniques and Raman and infrared spectroscopy. The mineral originated from the Javier Ortega mine, Lucanas Province, Peru. The chemical formula was determined as ðFe3þ 1:37; Al0:63ÞP2:00ðSO4Þ3 9H2O. Thermal analysis showed a total mass loss of 73.4% on heating to 1000 C. A mass loss of 30.43% at 641.4 C is attributed to the loss of SO3. Observed Raman and infrared bands were assigned to the stretching and bending vibrations of sulphate tetrahedra, aluminium oxide/hydroxide octahedra, water molecules and hydroxyl ions. The Raman spectrum shows well resolved bands at 2994, 3176, 3327, 3422 and 3580 cm 1 attributed to water stretching vibrations. Vibrational spectroscopy combined with thermal analysis provides insight into the structure of coquimbite.