Comparison Of Process Models Research Articles

Supplemental Material for How Do Humans Give Confidence? A Comprehensive Comparison of Process Models of Perceptual Metacognition

Supplemental Material for How Do Humans Give Confidence? A Comprehensive Comparison of Process Models of Perceptual Metacognition

Automatic Process Comparison for Subpopulations: Application in Cancer Care.

Processes in organisations, such as hospitals, may deviate from the intended standard processes, due to unforeseeable events and the complexity of the organisation. For hospitals, the knowledge of actual patient streams for patient populations (e.g., severe or non-severe cases) is important for quality control and improvement. Process discovery from event data in electronic health records can shed light on the patient flows, but their comparison for different populations is cumbersome and time-consuming. In this paper, we present an approach for the automatic comparison of process models that were extracted from events in electronic health records. Concretely, we propose comparing processes for different patient populations by cross-log conformance checking, and standard graph similarity measures obtained from the directed graph underlying the process model. We perform a user study with 20 participants in order to obtain a ground truth for similarity of process models. We evaluate our approach on two data sets, the publicly available MIMIC database with the focus on different cancer patients in intensive care, and a database on breast cancer patients from a Dutch hospital. In our experiments, we found average fitness to be a good indicator for visual similarity in the ZGT use case, while the average precision and graph edit distance are strongly correlated with visual impression for cancer process models on MIMIC. These results are a call for further research and evaluation for determining which similarity or combination of similarities is needed in which type of process model comparison.

A knowledge-intensive approach to process similarity calculation

Process model comparison and similar processes retrieval are key issues to be addressed in many real world situations, and particularly relevant ones in some applications (e.g., in medicine), where similarity quantification can be exploited in a quality assessment perspective.Most of the process comparison techniques described in the literature suffer from two main limitations: (1) they adopt a purely syntactic (vs. semantic) approach in process activity comparison, and/or (2) they ignore complex control flow information (i.e., other than sequence). These limitations oversimplify the problem, and make the results of similarity-based process retrieval less reliable, especially when domain knowledge is available, and can be adopted to quantify activity or control flow construct differences.In this paper, we aim at overcoming both limitations, by introducing a framework which allows to extract the actual process model from the available process execution traces, through process mining techniques, and then to compare (mined) process models, by relying on a novel distance measure.The novel distance measure, which represents the main contribution of this paper, is able to address issues (1) and (2) above, since: (1) it provides a semantic, knowledge-intensive approach to process activity comparison, by making use of domain knowledge; (2) it explicitly takes into account complex control flow constructs (such as AND and XOR splits/joins), thus fully considering the different semantic meaning of control flow connections in a reliable way.The positive impact of the framework in practice has been tested in stroke management, where our approach has outperformed a state-of-the art literature metric on a real world event log, providing results that were closer to those of a human expert. Experiments in other domains are foreseen in the future.

Hidden processes in structural representations: A reply to Abbott, Austerweil, and Griffiths (2015).

In recent work exploring the semantic fluency task, we found evidence indicative of optimal foraging policies in memory search that mirror search in physical environments. We determined that a 2-stage cue-switching model applied to a memory representation from a semantic space model best explained the human data. Abbott, Austerweil, and Griffiths demonstrate how these patterns could also emerge from a random walk applied to a network representation of memory based on human free-association norms. However, a major representational issue limits any conclusions that can be drawn about the process model comparison: Our process model operated on a memory space constructed from a learning model, whereas their model used human behavioral data from a task that is quite similar to the behavior they attempt to explain. Predicting semantic fluency (e.g., how likely it is to say cat after dog in a sequence of animals) from free association (how likely it is to say cat when given dog as a cue) should be possible with a relatively simple retrieval mechanism. The 2 tasks both tap memory, but they also share a common process of retrieval. Assuming that semantic memory is a network from free-association behavior embeds variance due to the shared retrieval process directly into the representation. A simple process mechanism is then sufficient to simulate semantic fluency because much of the requisite process complexity may already be hidden in the representation. (PsycINFO Database Record

Improving structural medical process comparison by exploiting domain knowledge and mined information.

Process model comparison and similar process retrieval is a key issue to be addressed in many real-world situations, and a particularly relevant one in medical applications, where similarity quantification can be exploited to accomplish goals such as conformance checking, local process adaptation analysis, and hospital ranking. In this paper, we present a framework that allows the user to: (i) mine the actual process model from a database of process execution traces available at a given hospital; and (ii) compare (mined) process models. The tool is currently being applied in stroke management. Our framework relies on process mining to extract process-related information (i.e., process models) from data. As for process comparison, we have modified a state-of-the-art structural similarity metric by exploiting: (i) domain knowledge; (ii) process mining outputs and statistical temporal information. These changes were meant to make the metric more suited to the medical domain. Experimental results showed that our metric outperforms the original one, and generated output closer than that provided by a stroke management expert. In particular, our metric correctly rated 11 out of 15 mined hospital models with respect to a given query. On the other hand, the original metric correctly rated only 7 out of 15 models. The experiments also showed that the framework can support stroke management experts in answering key research questions: in particular, average patient improvement decreased as the distance (according to our metric) from the top level hospital process model increased. The paper shows that process mining and process comparison, through a similarity metric tailored to medical applications, can be applied successfully to clinical data to gain a better understanding of different medical processes adopted by different hospitals, and of their impact on clinical outcomes. In the future, we plan to make our metric even more general and efficient, by explicitly considering various methodological and technological extensions. We will also test the framework in different domains.

Process model comparison and transferability across bioreactor scales and modes of operation for a mammalian cell bioprocess

A Monod kinetic model, logistic equation model, and statistical regression model were developed for a Chinese hamster ovary cell bioprocess operated under three different modes of operation (batch, bolus fed-batch, and continuous fed-batch) and grown on two different bioreactor scales (3 L bench-top and 15 L pilot-scale). The Monod kinetic model was developed for all modes of operation under study and predicted cell density, glucose glutamine, lactate, and ammonia concentrations well for the bioprocess. However, it was computationally demanding due to the large number of parameters necessary to produce a good model fit. The transferability of the Monod kinetic model structure and parameter set across bioreactor scales and modes of operation was investigated and a parameter sensitivity analysis performed. The experimentally determined parameters had the greatest influence on model performance. They changed with scale and mode of operation, but were easily calculated. The remaining parameters, which were fitted using a differential evolutionary algorithm, were not as crucial. Logistic equation and statistical regression models were investigated as alternatives to the Monod kinetic model. They were less computationally intensive to develop due to the absence of a large parameter set. However, modeling of the nutrient and metabolite concentrations proved to be troublesome due to the logistic equation model structure and the inability of both models to incorporate a feed. The complexity, computational load, and effort required for model development has to be balanced with the necessary level of model sophistication when choosing which model type to develop for a particular application.

The effect of moving from a plan-driven to an incremental software development approach with agile practices

So far, only few in-depth studies focused on the direct comparison of process models in general, and between plan-driven and incremental/agile approaches in particular. That is, it is not made explicit what the effect is of moving from one model to another model. Furthermore, there is limited evidence on advantages and issues encountered in agile software development, this is specifically true in the context of large-scale development. The objective of the paper is to investigate how the perception of bottlenecks, unnecessary work, and rework (from hereon referred to as issues) changes when migrating from a plan-driven to an incremental software development approach with agile practices (flexible product backlog, face-to-face interaction, and frequent integration), and how commonly perceived these practices are across different systems and development roles. The context in which the objective should be achieved is large-scale development with a market-driven focus. The selection of the context was based on the observation in related work that mostly small software development projects were investigated and that the investigation was focused on one agile model (eXtreme programming). A case study was conducted at a development site of Ericsson AB, located in Sweden in the end of 2007. In total 33 interviews were conducted in order to investigate the perceived change when migrating from plan-driven to incremental and agile software development, the interviews being the primary source of evidence. For triangulation purposes measurements collected by Ericsson were considered, the measurements relating to unnecessary work (amount of discarded requirements) and rework (data on testing efficiency and maintenance effort). Triangulation in this context means that the measurements were used to confirm the perceived changes with an additional data source. In total 64 issues were identified, 24 being of general nature and the remaining 40 being local and therefore unique to individual’s opinions or a specific system. The most common ones were documented and analyzed in detail. The commonality refers to how many persons in different roles and across the systems studied have mentioned the issues for each of the process models. The majority of the most common issues relates to plan-driven development. We also identified common issues remaining for agile after the migration, which were related to testing lead-time, test coverage, software release, and coordination overhead. Improvements were identified as many issues commonly raised for the plan-driven approach were not raised anymore for the incremental and agile approach. It is concluded that the recent introduction (start in 2005 with the study being conducted in the end of 2007) of incremental and agile practices brings added values in comparison to the plan-driven approach, which is evident from the absence of critical issues that are encountered in plan-driven development.