How to make process model matching work better? An analysis of current similarity measures

Process model matching techniques aim at automatically identifying activity correspondences between two process models that represent the same or similar behavior. By doing so, they provide essential input for many advanced process model analysis techniques such as process model search. Despite their importance, the performance of process model matching techniques is not yet convincing and several attempts to improve the performance have not been successful. This raises the question of whether it is really not possible to further improve the performance of process model matching techniques. In this paper, we aim to answer this question by conducting two consecutive analyses. First, we review existing process model matching techniques and give an overview of the specific technologies they use to identify similar activities. Second, we analyze the correspondences of the Process Model Matching Contest 2015 and reflect on the suitability of the identified technologies to identify the missing correspondences. As a result of these analyses, we present a list of three specific recommendations to improve the performance of process model matching techniques in the future.</p

Towards Process-Aware Cross-Organizational Human Resource Management

Finding human resources with the required set of skills, experience, and availability to execute an activity at a specific moment, is a socio-technical challenge for enterprises that use business-process aware systems. On an intra-organizational level, there exists an increasing body of knowledge for automated human-resource management. However, the recent pervasiveness of service-oriented cloud computing combined with mobile devices and big data, has resulted in the emergence of crossorganizational ecosystems in which workforce is distributed. Consequently, human-resource management has to consider more requirements compared to a purely intra-organizational setting. This position paper addresses the gap and describes a set of challenges in the management of human resources in service outsourcing scenarios based on process views and automatic process-view matching. The contribution is a specification of research directions that must be pursued so that resource management successfully adopts the special requirements for scaling to a cross-organizational level. Keywords: human resource management, process matching, process view, resource allocation, resource assignment, service outsourcin

Effective Utilization of Supervised Learning Techniques for Process Model Matching

The recent attempts to use supervised learning techniques for process model matching have yielded below par performance. To address this issue, we have transformed the well-known benchmark correspondences to a readily usable format for supervised learning. Furthermore, we have performed several experiments using eight supervised learning techniques to establish that imbalance in the datasets is the key reason for the abysmal performance. Finally, we have used four data balancing techniques to generate balanced training dataset and verify our solution by repeating the experiments for the four datasets, including the three benchmark datasets. The results show that the proposed approach increases the matching performance significantly

25 Desafíos de la Modelación de Procesos Semánticos

Process modeling has become an essential part of many organizations for documenting, analyzing and redesigning their business operations and to support them with suitable information systems. In order to serve this purpose, it is important for process models to be well grounded in for- mal and precise semantics. While behavioural semantics of process models are well understood, there is a considerable gap of research into the semantic aspects of their text labels and natural lan- guage descriptions. The aim of this paper is to make this research gap more transparent. To this end, we clarify the role of textual content in process models and the challenges that are associated with the interpretation, analysis, and improvement of their natural language parts. More specifically, we discuss particular use cases of semantic process modeling to identify 25 challenges. For each cha- llenge, we identify prior research and discuss directions for addressing themEl modelado de procesos se ha convertido en una parte esencial de muchas organizaciones para documentar, analizar, y rediseñar sus operaciones de negocios y apoyarlos con información apropiada. Para cumplir este fin, es importante para estos que estén completos dentro de una semántica formal y precisa. Mientras la semántica del comportamiento del modelado de procesos se entiende bien, hay una considerable laguna en la investigación entre los aspectos semánticos de sus rótulos textuales, y las descripciones en lenguaje natural. El objetivo de este artículo es hacer esta laguna en la investigación más transparente. Con este fin, clarificamos el papel del contenido textual en los modelos de proceso, y los retos relacionados con la interpretación, el análisis, y desarrollo de sus partes en lenguaje natural. De forma más específica, debatimos los casos particulares del uso del modelado de procesos semánticos para identificar 25 retos. Para cada reto, identificamos antes de la investigación y debatimos las direcciones para dirigirnos a ellos

Probabilistic evaluation of process model matching techniques

Process model matching refers to the automatic identification of corresponding activities between two process models. It represents the basis for many advanced process model analysis techniques such as the identification of similar process parts or process model search. A central problem is how to evaluate the performance of process model matching techniques. Often, not even humans can agree on a set of correct correspondences. Current evaluation methods, however, require a binary gold standard, which clearly defines which correspondences are correct. The disadvantage of this evaluation method is that it does not take the true complexity of the matching problem into account and does not fairly assess the capabilities of a matching technique. In this paper, we propose a novel evaluation method for process model matching techniques. In particular, we build on the assessment of multiple annotators to define probabilistic notions of precision and recall. We use the dataset and the results of the Process Model Matching Contest 2015 to assess and compare our evaluation method. We found that our probabilistic evaluation method assigns different ranks to the matching techniques from the contest and allows to gain more detailed insights into their performance

Supporting Event Log Extraction based on Matching

Process mining allows organizations to obtain relevant insights into the execution of their processes. However, the starting point of any process mining analysis is an event log, which is typically not readily available in practice. The extraction of event logs from the relevant databases is a manual and highly time-consuming task, and often a hurdle for the application of process mining altogether. Available support for event log extraction comes with different assumptions and requirements and only provides limited automated support. In this paper, we therefore take a novel angle at supporting event log extraction. The core idea of our paper is to use an existing process model as a starting point and automatically identify to which database tables the activities of the considered process model relate to. Based on the resulting mapping, an event log can then be extracted in an automated fashion. We use this paper to define a first approach that is able to identify such a mapping between a process model and a database. We evaluate our approach using three real-world databases and five process models from the purchase-to-pay domain. The results of our evaluation show that our approach has the potential to successfully support event log extraction based on matching

Investigating text power in predicting semantic similarity

This article presents an empirical evaluation to investigate the distributional semantic power of abstract, body and full-text, as different text levels, in predicting the semantic similarity using a collection of open access articles from PubMed. The semantic similarity is measured based on two criteria namely, linear MeSH terms intersection and hierarchical MeSH terms distance. As such, a random sample of 200 queries and 20000 documents are selected from a test collection built on CITREC open source code. Sim Pack Java Library is used to calculate the textual and semantic similarities. The nDCG value corresponding to two of the semantic similarity criteria is calculated at three precision points. Finally, the nDCG values are compared by using the Friedman test to determine the power of each text level in predicting the semantic similarity. The results showed the effectiveness of the text in representing the semantic similarity in such a way that texts with maximum textual similarity are also shown to be 77% and 67% semantically similar in terms of linear and hierarchical criteria, respectively. Furthermore, the text length is found to be more effective in representing the hierarchical semantic compared to the linear one. Based on the findings, it is concluded that when the subjects are homogenous in the tree of knowledge, abstracts provide effective semantic capabilities, while in heterogeneous milieus, full-texts processing or knowledge bases is needed to acquire IR effectiveness