On the use of hierarchical subtrace mining for efficient local process model mining

Mining local patterns of process behavior is a vital tool for the analysis of event data that originates from flexible processes, for which it is generally not possible to describe the behavior of the process in a single process model without overgeneralizing the behavior allowed by the process. Several techniques for mining such local patterns have been developed throughout the years, including Local Process Model (LPM) mining and the hierarchical mining of frequent subtraces (i.e., subprocesses). These two techniques can be considered to be orthogonal, i.e., they provide different types of insights on the behavior observed in an event log. As a consequence, it is often useful to apply both techniques to the data. However, both techniques can be computationally intensive, hindering data analysis. In this work, we explore how the output of a subtrace mining approach can be used to mine LPMs more efficiently. We show on a collection of real-life event logs that exploiting the ordering constraints extracted from subtraces lowers the computation time needed for LPM mining compared to state-of-the-art techniques, while at the same time mining higher quality LPMs. Additionally, by mining LPMs from subtraces, we can obtain a more structured and meaningful representation of subprocesses allowing for classic process-flow constructs such as parallel ordering, choices, and loops, besides the precedence relations shown by subtraces.</p

The use of {99m}Tc-Al[2]O[3] for detection of sentinel lymph nodes in cervical cancer patients

The purpose of the study was to evaluate the feasibility of using {99m}Tc-Al[2]O[3]- based radiopharmaceutical, a novel molecular imaging agent for sentinel lymph node detection in patients with invasive cervical cancer. The study included 23 cervical cancer patients (TlaNxMx- T[2]bNxMx) treated at the Tomsk Cancer Research Institute. At 18 hours before surgery, 80 MBq of the {99m}Tc-Al[2]O[3] were injected peritumorally, followed by single-photon emission computed tomography (SPECT) of the pelvis and intraoperative SLN identification. Twenty-seven SLNs were detected by SPECT, and 34 SLNs were identified by intraoperative gamma probe. The total number of identified SLNs per patient ranged from 1 to 3(the mean number of SLNs was 1.4 per patient). The most common site for SLN detection was the external iliac region (57.2%), followed by the internal iliac, obturator, presacral and retrosacral regions (they amounted to 14%, respectively),and the parametrial region (1%). Sensitivity in detecting SLNs was 100% for intraoperative SLN identification and 79% for SPECT image

Service Interaction Flow Analysis Technique for Service Personalization

Abstract Service interaction flows are difficult to capture, analyze, outline, and represent for research and design purposes. We examine how variation of personalized service flows in technology-mediated service interaction can be modeled and analyzed to provide information on how service personalization could support interaction. We have analyzed service interaction cases in a context of technology-mediated car rental service. With the analysis technique we propose, inspired by Interaction Analysis method, we were able to capture and model the situational service interaction. Our contribution regarding technology-mediated service interaction design is twofold: First, with the increased understanding on the role of personalization in managing variation in technology-mediated service interaction, our study contributes to designing service management information systems and human-computer interfaces that support personalized service interaction flows. Second, we provide a new analysis technique for situated interaction analysis, particularly when the aim is to understand personalization in service interaction flows

The Impact of Error-Management Climate, Error Type and Error Originator on Auditors’ Reporting Errors Discovered on Audit Work Papers

We examine factors affecting the auditor’s willingness to report their own or their peers’ self-discovered errors in working papers subsequent to detailed working paper review. Prior research has shown that errors in working papers are detected in the review process; however, such detection rates only rarely exceed 50% of the seeded errors. Hence, measures that encourage auditors to be alert to their own (or their peers’) potential errors any time they revisit the audit working papers may be valuable in detecting such residual errors and potentially correcting them before damage occurs to the audit firm or its client. We hypothesize that three factors affect the auditor’s willingness to report post detailed review discovered errors: the local office error-management climate (open versus blame), the type of error (mechanical versus conceptual) and who committed the error (the individual who committed the error (self) or a peer). Local office error-management climate is said to be open and supportive where errors and mistakes are accepted as part of everyday life as long as they are learned from and not repeated. In alternative, a blame error-management climate focuses on a “get it right the first time” culture where mistakes are not tolerated and blame gets attached to those admitting to or found committing such errors. We find that error-management climate has a significant overall effect on auditor willingness to report errors, as does who committed the error originally. We find both predicted and unpredicted significant interactions among the three factors that qualify these observed significant main effects. We discuss implications for audit practice and further research

Evaluating fibre orientation dispersion in white matter: comparison of diffusion MRI, histology and polarized light imaging

Diffusion MRI is an exquisitely sensitive probe of tissue microstructure, and is currently the only non-invasive measure of the brain’s fibre architecture. As this technique becomes more sophisticated and microstructurally informative, there is increasing value in comparing diffusion MRI with microscopic imaging in the same tissue samples. This study compared estimates of fibre orientation dispersion in white matter derived from diffusion MRI to reference measures of dispersion obtained from polarized light imaging and histology. Three post-mortem brain specimens were scanned with diffusion MRI and analyzed with a two-compartment dispersion model. The specimens were then sectioned for microscopy, including polarized light imaging estimates of fibre orientation and histological quantitative estimates of myelin and astrocytes. Dispersion estimates were correlated on region – and voxel-wise levels in the corpus callosum, the centrum semiovale and the corticospinal tract. The region-wise analysis yielded correlation coefficients of r=0.79 for the diffusion MRI and histology comparison, while r=0.60 was reported for the comparison with polarized light imaging. In the corpus callosum, we observed a pattern of higher dispersion at the midline compared to its lateral aspects. This pattern was present in all modalities and the dispersion profiles from microscopy and diffusion MRI were highly correlated. The astrocytes appeared to have minor contribution to dispersion observed with diffusion MRI. These results demonstrate that fibre orientation dispersion estimates from diffusion MRI represents the tissue architecture well. Dispersion models might be improved by more faithfully incorporating an informed mapping based on microscopy data

Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process

Toward a theory of repeat purchase drivers for consumer services

The marketing discipline’s knowledge about the drivers of service customers’ repeat purchase behavior is highly fragmented. This research attempts to overcome that fragmented state of knowledge by making major advances toward a theory of repeat purchase drivers for consumer services. Drawing on means–end theory, the authors develop a hierarchical classification scheme that organizes repeat purchase drivers into an integrative and comprehensive framework. They then identify drivers on the basis of 188 face-to-face laddering interviews in two countries (USA and Germany) and assess the drivers’ importance and interrelations through a national probability sample survey of 618 service customers. In addition to presenting an exhaustive and coherent set of hierarchical repeat-purchase drivers, the authors provide theoretical explanations for how and why drivers relate to one another and to repeat purchase behavior. This research also tests the boundary conditions of the proposed framework by accounting for different service types. In addition to its theoretical contribution, the framework provides companies with specific information about how to manage long-term customer relationships successfully

Consumer Complaints and Company Market Value

Consumer complaints affect company market value and common sense suggests that a negative impact is expected. However, do complaints always negatively impact company market value? We hypothesize in this study that complaints may have a non-linear effect on market value. Positive (e.g. avoiding high costs to solve complaints) and negative (e.g. speedy and intense diffusion) tradeoffs may occur given the level of complaints. To test our non-linear hypothesis, a panel data was collected from cell phone service providers from 2005 to 2013. The results supported our tradeoff rationale. Low levels of complaints allow for companies to increase market value, while high levels of complaints cause increasing harm to market value. The sample, model and period considered in this study, indicates a level of 0.49 complaints per thousand consumers as the threshold for a shift in tradeoffs. The effects on market value become increasingly negative when trying to make reductions to move below this level, due to negative tradeoffs