Business Process Model and Notation

About: Business Process Model and Notation is a research topic. Over the lifetime, 9038 publications have been published within this topic receiving 190712 citations. The topic is also known as: Business Process Modeling Notation & BPMN.

Investigating business processes

Abstract: Systems and methods of investigating business processes are described. These systems and methods support real-time monitoring, analysis, management, and optimization of business processes. A graphical user interface enables users to apply data warehousing and data mining techniques to business process execution data and to visualize process execution data along multiple configurable dimensions and at different configurable levels of granularity.

VIVACE: A Framework for the Systematic Evaluation of Variability Support in Process-Aware Information Systems

Abstract: Context: The increasing adoption of process-aware information systems (PAISs) such as workow management systems, enterprise resource planning systems, or case management systems, together with the high variability in business processes (e.g., sales processes may vary depending on the respective products and countries), has resulted in large industrial process model repositories. To cope with this business process variability, the proper management of process variants along the entire process lifecycle becomes crucial. Objective: The goal of this paper is to develop a fundamental understanding of business process variability. In particular, the paper will provide a framework for assessing and comparing process variability approaches and the support they provide for the dierent phases of the business process life

A conceptually rich model of business process compliance

Abstract: In this paper we extend the preliminary work developed elsewhere and investigate how to characterise many aspects of the compliance problem in business process modeling. We first define a formal and conceptually rich language able to represent, and reason about, chains of reparational obligations of various types. Second, we devise a mechanism for normalising a system of legal norms. Third, we specify a suitable language for business process modeling able to automate and optimise business procedures and to embed normative constraints. Fourth, we develop an algorithm for compliance checking and discuss some computational issues regarding the possibility of checking compliance runtime or of enforcing it at design time.

Semantic Querying of Business Process Models

Abstract: Determining similarity between business process models has recently gained interest in the business process management community. So far similarity was addressed separately either at semantic or structural aspect of process models. Also, most of the contributions that measure similarity of process models assume an ideal case when process models are enriched with semantics---a description of meaning of process model elements. However, in real life this results in a heavy human effort consuming pre-processing phase which is often not feasible. In this paper we propose an automated approach for querying a business process model repository for structurally and semantically relevant models. Similar to the search on the Internet, a user formulates a BPMN-Q query and as a result receives a list of process models ordered by relevance to the query. We provide a business process model search engine implementation for evaluation of the proposed approach.

Split Miner: Discovering Accurate and Simple Business Process Models from Event Logs

Abstract: The problem of automated discovery of process models from event logs has been intensively researched in the past two decades. Despite a rich field of proposals, state-of-the-art automated process discovery methods suffer from two recurrent deficiencies when applied to real-life logs: (i) they produce large and spaghetti-like models; and (ii) they produce models that either poorly fit the event log (low fitness) or highly generalize it (low precision). Striking a tradeoff between these quality dimensions in a robust and scalable manner has proved elusive. This paper presents an automated process discovery method that produces simple process models with low branching complexity and consistently high and balanced fitness, precision and generalization, while achieving execution times 2-6 times faster than state-of-the-art methods on a set of 12 real-life logs. Further, our approach guarantees deadlock-freedom for cyclic process models and soundness for acyclic. Our proposal combines a novel approach to filter the directly-follows graph induced by an event log, with an approach to identify combinations of split gateways that accurately capture the concurrency, conflict and causal relations between neighbors in the directly-follows graph.