Corpus Statistics for Measuring Business Process Similarity

In a rapidly changing environment, organizations must adapt their business processes continuously. While numerous methods enable enterprises to conceptualize and analyze their organizational structure, the task of business process modeling remains complex and time-consuming. However, by reusing and adapting existing process models, enterprises can reduce the task’s complexity while improving the quality of results. To facilitate the identification of adaptable processes, several techniques of business process similarity (BPS) have been proposed in recent years. Although most approaches produce sound results in controlled evaluations, this paper argues that their applicability is limited when analyzing real-world processes, which do not fully comply with notational labeling specifications. Consequently, we aim to enhance existing BPS techniques by using corpus statistics to account for the explanatory power of words within labels of process models. Results from our evaluation suggest that corpus statistics can improve BPS computations and can positively influence the quality of practical implications

Identifying Business Process Activity Mappings by Optimizing Behavioral Similarity

This paper describes an approach designed to create a mapping between corresponding activities from two business processes that is geared towards handling noisy similarity values for the labels describing these activities. This is achieved by formulating an optimization problem – maximize the behavioral similarity of the processes as a whole – whose target value depends on the mapping. Thereby, the mapping is created not only with respect to label similarities but also with respect to the overall control flow structure, which avoids some mistakes resulting from erroneous label similarities. A preliminary evaluation demonstrates the improvement

AN EMPIRICAL ASSESSMENT OF THE USEFULNESS OF WEAKNESS PATTERNS IN BUSINESS PROCESS REDESIGN

Business Process Management (BPM) is a topic with growing relevance for businesses as well as public organisations. Until today, the analysis part of a BPM cycle is mostly done manually. Process modelling methods are not designed to allow for automated analysis. Our aim is to show that meaningful weakness patterns that support semi-automatic analysis of business process diagrams (BPD) can be defined when a semantically enhanced modelling method is used. We derive exemplary weakness patterns by analysing literature and interviews from a business process redesign project. These are applied to a set of process models, in which occurrences of these weaknesses are being searched automatically. A comparison of achieved and expected results indicates that our approach helps to identify weaknesses within the processes and therefore supports business process analysis endeavours

Indexing and efficient instance-based retrieval of process models using untanglings

Process-Aware Information Systems (PAISs) support executions of operational processes that involve people, resources, and software applications on the basis of process models. Process models describe vast, often infinite, amounts of process instances, i.e., workflows supported by the systems. With the increasing adoption of PAISs, large process model repositories emerged in companies and public organizations. These repositories constitute significant information resources. Accurate and efficient retrieval of process models and/or process instances from such repositories is interesting for multiple reasons, e.g., searching for similar models/instances, filtering, reuse, standardization, process compliance checking, verification of formal properties, etc. This paper proposes a technique for indexing process models that relies on their alternative representations, called untanglings. We show the use of untanglings for retrieval of process models based on process instances that they specify via a solution to the total executability problem. Experiments with industrial process models testify that the proposed retrieval approach is up to three orders of magnitude faster than the state of the art

Detecting Semantic Business Process Model Clones

Process modeling with languages like BPMN allows process designers to create the same business process model in various ways. Detecting model clones, i.e., pairs of business process models sharing a certain degree of similarity can be difficult. In this paper, we propose an approach to process model clone detection based upon dominator trees and targeted at detecting semantically though not necessarily syntactically similar process models of business processes

A probabilistic evaluation procedure for 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. Current evaluation methods require a binary gold standard that clearly defines which correspondences are correct. The problem is that often not even humans can agree on a set of correct correspondences. Hence, evaluating the performance of matching techniques based on a binary gold standard 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 procedure for process model matching techniques. In particular, we build on the assessments of multiple annotators to define the notion of a non-binary gold standard. In this way, we avoid the problem of agreeing on a single set of correct correspondences. Based on this non-binary gold standard, we introduce probabilistic versions of precision, recall, and F-measure as well as a distance-based performance measure. We use a dataset from the Process Model Matching Contest 2015 and a total of 16 matching systems to assess and compare the insights that can be obtained by using our evaluation procedure. We find that our probabilistic evaluation procedure allows us to gain more detailed insights into the performance of matching systems than a traditional evaluation based on a binary gold standard

Analisis aspek Behavioral pada Business Process Model and Notation menggunakan Causal Footprints

Dari sekian banyaknya model proses bisnis dapat menimbulkan masalah baru seperti model proses bisnis yang dibuat mengalami duplikasi antara satu model proses bisnis dengan yang lainnya sehingga menyebabkan repository menjadi penuh atau menjadi keberagaman pada model proses bisnis. Dalam mengatasi masalah tersebut salah satu cara adalah dengan menganalisis similarity (kemiripan) antara model proses bisnis. Analisis tingkat kesamaan proses bisnis sangat diperlukan dalam penyederhanaan dan penyatuan berbagai proses bisnis yang ada. Analisis dilakukan pada aspek behavioral, karena behavioral similarity memiliki kelebihan daripada label dan structural similarity dimana pada saat melakukan pengukuran similiarity, memperhatikan relasi tidak langsung sehingga perhitungan similarity yang didapat tidak mengalami penurunan. Metode yang digunakan adalah Causal Footprints, sebuah graph untuk mereprentasikan behavior antara dua node dari suatu model proses bisnis, dinamakan look-back links dan look-ahead links. Untuk mendukung proses similarity perlu ditunjang oleh bahasa pemodelan proses yang memiliki activity nodes dan control nodes seperti Business Process Model and Notation (BPMN) juga memiliki format struktur data XML. Pengujian dilakukan dengan menggunakan tiga model BPMN yang dibandingkan sebagai query dan variant. Berdasarkan hasil pengujian, similarity BPMN pertama terhadap BPMN kedua sebesar 63 % begitu juga sebaliknya, BPMN kedua terhadap BPMN ketiga sebesar 79 % begitu juga sebaliknya, dan BPMN pertama terhadap BPMN ketiga sebesar 70 % begitu juga sebaliknya. Faktor yang mempengaruhi nilai similarity adalah jumlah node, pertukaran BPMN sebagai query dengan variant, intersection dan link

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

The 4C spectrum of fundamental behavioral relations for concurrent systems

The design of concurrent software systems, in particular process-aware information systems, involves behavioral modeling at various stages. Recently, approaches to behavioral analysis of such systems have been based on declarative abstractions defined as sets of behavioral relations. However, these relations are typically defined in an ad-hoc manner. In this paper, we address the lack of a systematic exploration of the fundamental relations that can be used to capture the behavior of concurrent systems, i.e., co-occurrence, conflict, causality, and concurrency. Besides the definition of the spectrum of behavioral relations, which we refer to as the 4C spectrum, we also show that our relations give rise to implication lattices. We further provide operationalizations of the proposed relations, starting by proposing techniques for computing relations in unlabeled systems, which are then lifted to become applicable in the context of labeled systems, i.e., systems in which state transitions have semantic annotations. Finally, we report on experimental results on efficiency of the proposed computations