Process model comparison based on cophenetic distance

The automated comparison of process models has received increasing attention in the last decade, due to the growing existence of process models and repositories, and the consequent need to assess similarities between the underlying processes. Current techniques for process model comparison are either structural (based on graph edit distances), or behavioural (through activity profiles or the analysis of the execution semantics). Accordingly, there is a gap between the quality of the information provided by these two families, i.e., structural techniques may be fast but inaccurate, whilst behavioural are accurate but complex. In this paper we present a novel technique, that is based on a well-known technique to compare labeled trees through the notion of Cophenetic distance. The technique lays between the two families of methods for comparing a process model: it has an structural nature, but can provide accurate information on the differences/similarities of two process models. The experimental evaluation on various benchmarks sets are reported, that position the proposed technique as a valuable tool for process model comparison.Peer ReviewedPostprint (author's final draft

Pengelompokan Model Proses Berdasarkan Matrik Similaritas Dengan Pendekatan Semantik

Saat ini, pemanfaatan sebuah sistem informasi berorientasi proses pada sebuah organisasi sangat marak dilakukan. Sistem informasi berorientasi proses bertujuan untuk meningkatkan kinerja sebuah organisasi. Dalam sebuah organisasi berskala besar, model proses yang digunakan untuk mendukung bisnis tidak berjumlah sedikit, melainkan dapat mencapai angka ratusan bahkan hingga ribuan. Repositori model proses adalah sebuah media untuk menyimpan model proses dalam sebuah organisasi. Terdapat permasalahan dalam pengelolaan repositori model proses, antara lain proses perhitungan kesamaan model proses yang masih menggunakan pendekatan kesamaan sintaktik. Pendekatan tersebut membuat proses pengelompokan model proses menjadi kurang optimal. Untuk menjawab permasalahan tersebut, pada penelitian ini dilakukan mekanisme pengelompokan model proses berdasarkan kedekatan derajat kesamaan yang dimiliki tiap model proses. Penghitungan derajat kesamaan dilakukan berdasarkan beberapa matrik kesamaan, antara lain kesamaan titik (node), kesamaan struktur, dan kesamaan perilaku (behavior). Serta perhitungan derajat kesamaan dilakukan dengan menggunakan metode kesamaan arti (semantik). Penggunaan metode kesamaan arti dapat meningkatkan nilai compactness pada kelompok yang dihasilkan dari proses clustering

Adopted topic modeling for business process and software component conformity checking

Business processes and software components, especially class diagrams, have a firm connection. Considering software components support the business process in providing an excellent product and service. Besides, business process changes affect on software component design. One of them usually appears on the label or name of the software component or business process. Sometimes, a related business process and software component appears in the different label but the same meaning rather than using the same label. This situation is problematic when there are many changes to be made, in which the software component's modifying process becomes quite long. Therefore, the software maintainers should obtain an efficient procedure to shorten the modifying process. One solution is by using conformity checking, which helps the software maintainers know which software component is related to a specific business process. This paper compared two leading topic modeling techniques, namely probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA), to determine which one has a better performancefor process traceability

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

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

Applicability of Business Process Model Analysis Approaches – A Case Study in Financial Services Consulting

The analysis of business process models gains more and more attention in IS research. Several analysis approaches have been developed. All of them provide different features, such as syntax checking or pattern recognition. This paper investigates the applicability and relevance of business process model analysis approaches using a case study from financial services consulting. Two research contributions are provided. First, an overview about common model analysis features and its relevance for consulting processes are provided. Second, the applicability of the automatic business process model analysis approaches is investigated. Results show that the majority of features can raise efficiency of analyses in business process reengineering projects

SUPPORTING ENTERPRISE TRANSFORMATION USING A UNIVERSAL MODEL ANALYSIS APPROACH

Enterprise Architecture Management has been proposed to help organizations in their efforts to flexibly adapt to rapidly changing market environments. Enterprise architectures are described by means of conceptual models depicting, e.g., an enterprise?s business processes, its organisational structure, or the data the enterprise needs to manage. Such models are stored in large repositories. Using these repositories to support enterprise transformation processes often requires detecting structural patterns containing particular labels within the model graphs. As an example, consider the case of mergers and acquisitions. Respective patterns could represent specific model fragments that occur frequently within the process models of the merging companies. This paper introduces an approach to analyse conceptual models at a structural and semantic level. In terms of structure, the approach is able to detect patterns within the model graphs. In terms of semantics, the approach is able to detect previously standardized model labels. Its core contribution to enterprise architecture management and transformation is two-fold. First, it is able to analyse conceptual models created in arbitrary modelling languages. Second, it supports a wide variety of pattern-based analysis tasks related to managing change in organisations. The approach is applied in a merger and acquisition scenario to demonstrate its applicability

Information Is Selection-A Review of Basics Shows Substantial Potential for Improvement of Digital Information Representation

Any piece of information is a selection from a set of possibilities. In this paper, this set is called a "domain". Digital information consists of number sequences, which are selections from a domain. At present, these number sequences are defined contextually in a very variable way, which impairs their comparability. Therefore, global uniformly defined "domain vectors" (DVs), with a structure containing a "Uniform Locator" ("UL"), referred to as "UL plus number sequence", are proposed. The "UL" is an efficient global pointer to the uniform online definition of the subsequent number sequence. DVs are globally defined, identified, comparable, and searchable by criteria which users can define online. In medicine, for example, patients, doctors, and medical specialists can define DVs online and can, therefore, form global criteria which are important for certain diagnoses. This allows for the immediate generation of precise diagnostic specific statistics of "similar medical cases", in order to discern the best therapy. The introduction of a compact DV data structure may substantially improve the digital representation of medical information