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This paper proposes a method based on a new word embedding approach for matching business process model. The proposed method aligns two process models in four steps. First activity labels are extracted and pre-processed to remove meaningless words, then each word composing an activity label and using a semantic similarity metric based on WordNet is represented with an n-dimensional vector in the space of the vocabulary of the two labels to be compared. Based on these representations, a vector representation of each activity label is computed by averaging the vectors representing words found in the activity label. Finally, the two activity labels are reported as similar if their similarity score computed using the cosine metric is greater than some predefined threshold. An experiment was conducted on well-known dataset to assess the performance of the proposed method. The results showed that the proposed method shared the first place with RMM/NHCM and OPBOT tools and can be effective in matching process models.

This paper proposes coordinated corrective control of input/state asynchronous sequential machines (ASMs). The states of the considered ASM are physically or logically separated to which complete o...

The problem of compensating a given plant by means of a static compensator in such a way that, for any input, the output of the compensated system matches that of a given positive model, when both are initialized at 0, and its state evolves in the positive cone for positive initial conditions and inputs is considered. Under a mild structural assumption for the output-difference system between the plant and the model, a complete characterization of solvability of the problem in terms of necessary and sufficient conditions is obtained by means of structural geometric methods. Solvability conditions are practically checkable by algorithmic procedures and by solving a set of linear inequalities. The problem of asymptotic matching for any initial condition is then considered and solvability is characterized by necessary and sufficient conditions. A necessary condition that is practically checkable is given. Solvability by a dynamic compensator is also studied and a sufficient condition to characterize it is given.

Process model matchers automate the detection of activities that represent similar functionality in different models. Thus, they provide support for various tasks related to the management of business processes including model collection management and process design. Yet, prior research primarily demonstrated the matchers' effectiveness, i.e., the accuracy and the completeness of the results. In this context, all data is used for the matcher development and the validity of the design decisions is not studied. A result of these shortcomings is that existing matchers yield a varying and typically low effectiveness when applied to different datasets. With that in mind, the thesis studies the effectiveness of matchers by separating development from evaluation data and by empirically analyzing the validity and the limitations of design decisions. In more detail, the thesis develops matching techniques that rely on different sources of information. First, the activity labels are considered as natural-language descriptions and the Bag-of-Words Technique is introduced. In comparison to the state of the art it achieves a high effectiveness. However, its effectiveness depends on the degree to which the underlying knowledge sources reflect the domain characteristics of the models. Moreover, it needs to be configured for each model collection which can require a huge manual effort. Second, the Order Preserving Bag-of-Words Technique analyzes control flow dependencies between activities in order to automatically configure the Bag-of-Words Technique and to maximize its effectiveness. Thus, it relieves experts from manually configuring the matchers. Third, expert feedback is used to adapt the matchers to the domain characteristics of process model collections and to further improve the effectiveness. Here, the Adaptive Bag-of-Words Technique is introduced. It analyzes expert feedback in order to continuously adjust the matching process and yields a strongly improved effectiveness. Consequently, it outperforms state-of-the-art matchers as well as the other matchers from this thesis.

ASHVIN WP5 “Digital twin-based structural monitoring and asset management” focus on establishing a real-time connection between physical infrastructures and virtual yet realistic numerical models of such assets for the purpose of supporting maintenance and asset management tasks. Deliverable 5.2 entitled ”Digital-Twin enabled multi-physics simulation and model matching” was submitted in April 2023 under this work package. This report is related to task 5.2 “Multi-physics FEM model calibration” lead by UPC. Abstract This deliverable presents a report on the developed actions and results concerning Digital- Twin-enabled multi-physics simulations and model matching. Enabling meaningful simulations within new human-infrastructure interfaces such as Digital twins is paramount. Accessing the power of simulation opens manifold new ways for observation, understanding, analysis and prediction of numerous scenarios to which the asset may be faced. As a result, managers can access countless ways of acquiring synthetic data for eventually taking better, more informed decisions. The tool MatchFEM is conceived as a fundamental part of this endeavour. From a broad perspective, the tool is aimed at contextualizing information between multi-physics simulations and vaster information constructs such as digital twins. 3D geometries, measurements, simulations, and asset management coexist in such information constructs. This report provides guidance for generating comprehensive, adequate initial conditions of the assets to be used during their life span using a DT basis. From a more specific focus, this deliverable presents a set of exemplary recommendations for developing DT-enabled load tests of assets in the form of a white paper.&nbsp

Business process models are the conceptual models to depict the workflow of an organization. Process model matching (PMM) refers to the automatic identification of corresponding activities between a pair of process models that show similar or the same behavior. During the last few years, PMM has received much of the researchers’ attention due to its wide range of applications, such as clone detection and harmonization of process models. Consequently, a plethora of PMM techniques has been developed. In order to evaluate the effectiveness of these techniques, experts have developed three benchmark datasets, formally called PMMC’15 datasets. Furthermore, the process models in the datasets have been converted into OAEI’17 ontologies. These resources are a valuable asset for the PMM community to evaluate process model matching techniques. However, these resources (PMMC’15 and OAEI’17) are limited to fewer models and a handful collection of corresponding activities among these models that may not be sufficient to rigorously evaluate the PMM techniques. To fill this gap, this paper provides a large, diverse, and a carefully handcrafted collection of process models, along with their benchmark correspondences. The process model collection and benchmark correspondences between these models are freely available for the community [1] . Our newly developed dataset, together with the existing resources, can be used for a thorough evaluation of PMM techniques, especially in the context of the vocabulary mismatch problem. At last, we have evaluated the characteristics of our dataset by a series of experiments while involving widely used similarity measures in PMM research. The results reveal that our dataset is larger, diverse, and challenging as compared to existing datasets in the PMM domain.