On Business Process Variants Generation (original) (raw)

Business process variant analysis: Survey and classification

Knowledge-Based Systems, 2021

It is common for business processes to exhibit a high degree of internal heterogeneity, in the sense that the executions of the process differ widely from each other due to contextual factors, human factors, or deliberate business decisions. For example, a quote-to-cash process in a multinational company is typically executed differently across different countries or even across different regions in the same country. Similarly, an insurance claims handling process might be executed differently across different claims handling centres or across multiple teams within the same claims handling centre. A subset of executions of a business process that can be distinguished from others based on a given predicate (e.g. the executions of a process in a given country) is called a process variant. Understanding differences between process variants helps analysts and managers to make informed decisions as to how to standardize or otherwise improve a business process, for example by helping them find out what makes it that a given variant exhibits a higher performance than another one. Process variant analysis is a family of techniques to analyze event logs produced during the execution of a process, in order to identify and explain the differences between two or more process variants. A wide range of methods for process variant analysis have been proposed in the past decade. However, due to the interdisciplinary nature of this field, the proposed methods and the types of differences they can identify vary widely, and there is a lack of a unifying view of the field. To close this gap, this article presents a systematic literature review of methods for process variant analysis. The identified studies are classified according to their inputs, outputs, analysis purpose, underpinning algorithms, and extra-functional characteristics. The paper closes with a broad classification of approaches into three categories based on the paradigm they employ to compare multiple process variants. CCS Concepts: • Applied computing → Process mining.

On managing business processes variants

Data & Knowledge Engineering, 2009

Variance in business process execution can be the result of several situations, such as disconnection between documented models and business operations, workarounds in spite of process execution engines, dynamic change and exception handling, flexible and ad-hoc requirements, and collaborative and/or knowledge intensive work. It is imperative that effective support for managing process variances be extended to organizations mature in their BPM (Business Process Management) uptake so that they can ensure organization wide consistency, promote reuse and capitalize on their BPM investments. This paper presents an approach for managing business processes that is conducive to dynamic change and the need for flexibility in execution. The approach is based on the notion of process constraints. It further provides a technique for effective utilization of the adaptations manifested in process variants. In particular, we will present a facility for discovery of preferred variants through effective search and retrieval based on the notion of process similarity, where multiple aspects of the process variants are compared according to specific query requirements. The advantage of this approach is the ability to provide a quantitative measure for the similarity between process variants, which further facilitates various BPM activities such as process reuse, analysis and discovery.

Multi-perspective Comparison of Business Process Variants Based on Event Logs

Conceptual Modeling

A process variant represents a collection of cases with certain shared characteristics, e.g. cases that exhibit certain levels of performance. The comparison of business process variants based on event logs is a recurrent operation in the field of process mining. Existing approaches focus on comparing variants based on directly-follows relations such as "a task directly follows another one" or a "resource directly hands-off to another resource". This paper presents a more general approach to log-based process variant comparison based on so-called perspective graphs. A perspective graph is a graph-based abstraction of an event log where a node represents any entity referred to in the log (e.g. task, resource, location) and an arc represents a relation between these entities within or across cases (e.g. directly-follows, co-occurs, hands-off to, works-together with). Statistically significant differences between two perspective graphs are captured in a so-called differential perspective graph, which allows us to compare two logs from any perspective. The paper illustrates the approach and compares it to an existing baseline using real-life event logs.

Modelling Business Process Variants using Graph Transformation Rules

Proceedings of the 4th International Conference on Model-Driven Engineering and Software Development, 2016

Business process variability is an active research area in the field of business process management and deals with variations and commonalities among processes of a given process family. Many theoretical approaches have been suggested in the last years; however, practical implementations are rare and limited in their functionality. In this paper, we propose a new approach for business process variability based on well-known graph transformation techniques and with focus on practical aspects like definition of variation points, linking and propagation of changes, as well as visual highlighting of differences in process variants. The suggested concepts are discussed within a case study comprising two graph transformation systems for generating process variants; one supports variability by restriction, the other supports variability by restriction and by extension. Both graph transformation systems are proven to be globally deterministic, but differ regarding their complexity. The overall approach is being implemented in the BPM suite of our partner company. 2 STATE-OF-THE-ART This section provides an overview of the state-of-theart concerning graph transformation, variability modelling, and the current support of business process variability in BPM suites.

Similarity matching of business process variants

2008

Evidence from business work practice indicates that variance from prescribed business process models is not only inevitable and frequent, but is in fact a valuable source of organizational intellectual capital that needs to be captured and capitalized, since variance is typically representative of preferred and successful work practice. In this paper, we present a framework for harnessing the value of business process variants. An essential aspect of this framework is the ability to search and retrieve variants. This functionality requires variants to be matched against a given criteria. The focus of this paper is on the structural criteria which is rather challenging as query process structures may have different levels of similarity with variant process structures. The paper provides methods for undertaking the similarity matching and subsequently providing ranked results in a systematic way, as well as a reference architecture within which the methods may be deployed.

Mining Multi-variant Process Models from Low-Level Logs

Lecture Notes in Business Information Processing, 2015

Process discovery techniques are a precious tool for analyzing the real behavior of a business process. However, their direct application to lowly structured logs may yield unreadable and inaccurate models. Current solutions rely on event abstraction or trace clustering, and assume that log events refer to well-defined (possibly low-level) process tasks. This reduces their suitability for logs of real BPM systems (e.g. issue management) where each event just stores several data fields, none of which fully captures the semantics of performed activities. We here propose an automated method for discovering an expressive kind of process model, consisting of three parts: (i) a logical event clustering model, for abstracting low-level events into classes; (ii) a logical trace clustering model, for discriminating among process variants; and (iii) a set of workflow schemas, each describing one variant in terms of the discovered event clusters. Experiments on a real-life data confirmed the capability of the approach to discover readable high-quality process models.

Controlled automated discovery of collections of business process models

Information Systems, 2014

Automated process discovery techniques aim at extracting process models from information system logs. Existing techniques in this space are effective when applied to relatively small or regular logs, but generate spaghetti-like and sometimes inaccurate models when confronted to logs with high variability. In previous work, trace clustering has been applied in an attempt to reduce the size and complexity of automatically discovered process models. The idea is to split the log into clusters and to discover one model per cluster. This leads to a collection of process models-each one representing a variant of the business process-as opposed to an all-encompassing model. Still, models produced in this way may exhibit unacceptably high complexity and low fitness. In this setting, this paper presents a two-way divide-and-conquer process discovery technique, wherein the discovered process models are split on the one hand by variants and on the other hand hierarchically using subprocess extraction. Splitting is performed in a controlled manner in order to achieve user-defined complexity or fitness thresholds. Experiments on real-life logs show that the technique produces collections of models substantially smaller than those extracted by applying existing trace clustering techniques, while allowing the user to control the fitness of the resulting models.

Configurable Process Mining: Semantic Variability in Event Logs

Proceedings of the 23rd International Conference on Enterprise Information Systems, 2021

Configurable process model represents a reference model regrouping multiple business process variants. The configurable process models offer various benefits like reusability and more flexibility when compared to business process models. The challenges encountered while managing this type of models are related to the creation and the configuration. Recently, process mining offers techniques to discover, check conformance of models, and enhance configurable process models using a collection of event logs, that captures traces during the execution of process variants. However, existing works in configurable process discovery lack the incorporation of semantics in the resulting model. Historically, semantic process mining has been applied to event logs to improve process discovery with respect to semantic. Furthermore, from the best of our knowledge, configurable process mining approaches do not fully support semantics. In this paper, we propose a novel method to enrich the collection of event logs with configurable process ontology concepts by introducing semantic annotations that capture variability of elements present in the logs. This is a first step towards discovering a semantically enriched configurable process.

Managing process variants as an information resource

Business Process Management, 2006

Many business solutions provide best practice process templates, both generic as well as for specific industry sectors. However, it is often the variance from template solutions that provide organizations with intellectual capital and competitive differentiation. In this paper, we present a modeling framework that is conducive to constrained variance, by supporting user driven process adaptations. The focus of the paper is on providing a means of utilizing the adaptations effectively for process improvement through effective management of the process variants repository (PVR). In particular, we will provide deliberations towards a facility to provide query functionality for PVR that is specifically targeted for effective search and retrieval of process variants.

Context-Based Variant Generation of Business Process Models

Lecture Notes in Business Information Processing, 2014

Nowadays, variability management of process models is a major challenge for Process-Aware Information Systems. Process model variants can be attributed to any of the following reasons: new technologies, governmental rules, organizational context or adoption of new standards. Current approaches to manage variants of process models address issues such as reducing the huge effort of modeling from scratch, preventing redundancy, and controlling inconsistency in process models. Although the effort to manage process model variants has been exerted, there are still limitations. Furthermore, existing approaches do not focus on variants that come from organizational or informational perspectives of process models. This paper introduces an approach to generate context-sensitive process model variants that come from adaptations in the organizational perspective. The approach is inspired by real life scenarios and has its conceptualization based on general concepts such as abstraction, and polymorphism.