Process modeling

About: Process modeling is a research topic. Over the lifetime, 11639 publications have been published within this topic receiving 223996 citations. The topic is also known as: process simulation.

Exploiting Inductive Logic Programming Techniques for Declarative Process Mining

Abstract: In the last few years, there has been a growing interest in the adoption of declarative paradigms for modeling and verifying process models. These paradigms provide an abstract and human understandable way of specifying constraints that must hold among activities executions rather than focusing on a specific procedural solution. Mining such declarative descriptions is still an open challenge. In this paper, we present a logic-based approach for tackling this problem. It relies on Inductive Logic Programming techniques and, in particular, on a modified version of the Inductive Constraint Logic algorithm. We investigate how, by properly tuning the learning algorithm, the approach can be adopted to mine models expressed in the ConDec notation, a graphical language for the declarative specification of business processes. Then, we sketch how such a mining framework has been concretely implemented as a ProM plug-in called DecMiner. We finally discuss the effectiveness of the approach by means of an example which shows the ability of the language to model concurrent activities and of DecMiner to learn such a model.

Modeling and forecasting of manufacturing variations

Abstract: Process-induced variations are an important consideration in the design of integrated circuits. Until recently, it was sufficient to model die-to-die shifts in device performance, leading to the well known worst-case modeling and design methodology. However, current and near-future integrated circuits are large enough that device and interconnect parameter variations within the chip are as important as those same variations from chip to chip. This presents a new set of challenges for process modeling and characterization and for the associated design tools and methodologies. This paper examines the sources and trends of process variability, the new challenges associated with the increase in within-die variability analysis, and proposes a modeling and simulation methodology to deal with this variability.

Extending the epc and the bpmn with business process goals and performance measures

Abstract: The Event-Driven Process Chain (EPC) and the Business Process Modeling Notation (BPMN) are designed for modelling business processes, but do not yet include any means for modelling process goals and their measures, and they do not have a published metamodel. We derive a metamodel for both languages, and extend the EPC and the BPMN with process goals and performance measures to make them conceptually visible. The extensions are based on the metamodels tested with example business processes.

Process mining using BPMN: relating event logs and process models

Abstract: Process-aware information systems (PAIS) are systems relying on processes, which involve human and software resources to achieve concrete goals. There is a need to develop approaches for modeling, analysis, improvement and monitoring processes within PAIS. These approaches include process mining techniques used to discover process models from event logs, find log and model deviations, and analyze performance characteristics of processes. The representational bias (a way to model processes) plays an important role in process mining. The BPMN 2.0 (Business Process Model and Notation) standard is widely used and allows to build conventional and understandable process models. In addition to the flat control flow perspective, subprocesses, data flows, resources can be integrated within one BPMN diagram. This makes BPMN very attractive for both process miners and business users, since the control flow perspective can be integrated with data and resource perspectives discovered from event logs. In this paper, we describe and justify robust control flow conversion algorithms, which provide the basis for more advanced BPMN-based discovery and conformance checking algorithms. Thus, on the basis of these conversion algorithms low-level models (such as Petri nets, causal nets and process trees) discovered from event logs using existing approaches can be represented in terms of BPMN. Moreover, we establish behavioral relations between Petri nets and BPMN models and use them to adopt existing conformance checking and performance analysis techniques in order to visualize conformance and performance information within a BPMN diagram. We believe that the results presented in this paper can be used for a wide variety of BPMN mining and conformance checking algorithms. We also provide metrics for the processes discovered before and after the conversion to BPMN structures. Cases for which conversion algorithms produce more compact or more complicated BPMN models in comparison with the initial models are identified.