A core ontology for business process analysis

Business Process Management (BPM) aims at supporting the whole life-cycle necessary to deploy and maintain business processes in organisations. An important step of the BPM life-cycle is the analysis of the processes deployed in companies. However, the degree of automation currently achieved cannot support the level of adaptation required by businesses. Initial steps have been performed towards including some sort of automated reasoning within Business Process Analysis (BPA) but this is typically limited to using taxonomies. We present a core ontology aimed at enhancing the state of the art in BPA. The ontology builds upon a Time Ontology and is structured around the process, resource, and object perspectives as typically adopted when analysing business processes. The ontology has been extended and validated by means of an Events Ontology and an Events Analysis Ontology aimed at capturing the audit trails generated by Process-Aware Information Systems and deriving additional knowledge

The BPM ontology

This chapter introduces the BPM ontology that can be applied within the area of process modelling, process engineering and process architecture. At the highest level by providing the fundamental process concepts that are used to document corporate knowledge. At the lowest level by structuring the process knowledge itself in defining its relations

The value of ontology, The BPM ontology

It is generally accepted that the creation of added value requires collaboration inside and between organizations. Collaboration requires sharing knowledge (e.g., a shared understanding of business processes) between trading partners and between colleagues. It is on the (unique) knowledge that is shared between and created by colleagues that organizations build their competitive advantage. To take full advantage of this knowledge, it should be disseminated as widely as possible within an organization. Nonaka distinguished tacit knowledge, which is personal, context specific, and not so easy to communicate (e.g., intuitions, unarticulated mental models, embodied technological skills), from explicit knowledge, which is meaningful information articulated in clear language, including numbers and diagrams. Tacit knowledge can be disseminated through socialization (e.g., face-to-face communication, sharing experiences), which implies a reduced dissemination speed, or can be externalized , which is the conversion of tacit into explicit knowledge. Although explicit knowledge can take many forms (e.g., business (process) models, manuals), this chapter focuses on ontologies, which are versatile knowledge artifacts created through externalization, with the power to fuel Nonaka’s knowledge spiral. Nonaka’s knowledge spiral visualizes how a body of unique corporate knowledge, and hence a competitive advantage, is developed through a collaborative and iterative knowledge creation process that involves iterative cycles of externalization, combination, and internalization. When corporate knowledge is documented with ontology, a knowledge spiral leads to ontology evolution

Semantic business process management: a vision towards using semantic web services for business process management

Business process management (BPM) is the approach to manage the execution of IT-supported business operations from a business expert's view rather than from a technical perspective. However, the degree of mechanization in BPM is still very limited, creating inertia in the necessary evolution and dynamics of business processes, and BPM does not provide a truly unified view on the process space of an organization. We trace back the problem of mechanization of BPM to an ontological one, i.e. the lack of machine-accessible semantics, and argue that the modeling constructs of semantic Web services frameworks, especially WSMO, are a natural fit to creating such a representation. As a consequence, we propose to combine SWS and BPM and create one consolidated technology, which we call semantic business process management (SBPM

Towards an ontology for process monitoring and mining

Business Process Analysis (BPA) aims at monitoring, diagnosing, simulating and mining enacted processes in order to support the analysis and enhancement of process models. An effective BPA solution must provide the means for analysing existing e-businesses at three levels of abstraction: the Business Level, the Process Level and the IT Level. BPA requires semantic information that spans these layers of abstraction and which should be easily retrieved from audit trails. To cater for this, we describe the Process Mining Ontology and the Events Ontology which aim to support the analysis of enacted processes at different levels of abstraction spanning from fine grain technical details to coarse grain aspects at the Business Level

The business process modelling ontology

In this paper we describe the Business Process Modelling Ontology (BPMO), which is part of an approach to modelling business processes at the semantic level, integrating knowledge about the organisational context, workflow activities and Semantic Web Services. We harness knowledge representation and reasoning techniques so that business process workflows can: be exposed and shared through semantic descriptions; refer to semantically annotated data and services; incorporate heterogeneous data though semantic mappings; and be queried using a reasoner or inference engine. In this paper we describe our approach and evaluate BPMO through a use case

Ontology-based metrics computation for business process analysis

Business Process Management (BPM) aims to support the whole life-cycle necessary to deploy and maintain business processes in organisations. Crucial within the BPM lifecycle is the analysis of deployed processes. Analysing business processes requires computing metrics that can help determining the health of business activities and thus the whole enterprise. However, the degree of automation currently achieved cannot support the level of reactivity and adaptation demanded by businesses. In this paper we argue and show how the use of Semantic Web technologies can increase to an important extent the level of automation for analysing business processes. We present a domain-independent ontological framework for Business Process Analysis (BPA) with support for automatically computing metrics. In particular, we define a set of ontologies for specifying metrics. We describe a domain-independent metrics computation engine that can interpret and compute them. Finally we illustrate and evaluate our approach with a set of general purpose metrics

Translating semantic web service based business process models

We describe a model-driven translation approach between Semantic Web Service based business process models in the context of the SUPER project. In SUPER we provide a set of business process ontologies for enabling access to the business process space inside the organisation at the semantic level. One major task in this context is to handle the translations between the provided ontologies in order to navigate from different views at the business level to the IT view at the execution level. In this paper we present the results of our translation approach, which transforms instances of BPMO to instances of sBPEL

Integration of multi-scale biosimulation models via light-weight semantics

Currently, biosimulation researchers use a variety of computational environments and languages to model biological processes. Ideally, researchers should be able to semi- automatically merge models to more effectively build larger, multi-scale models. How- ever, current modeling methods do not capture the underlying semantics of these models sufficiently to support this type of model construction. In this paper, we both propose a general approach to solve this problem, and we provide a specific example that demon- strates the benefits of our methodology. In particular, we describe three biosimulation models: (1) a cardio-vascular fluid dynamics model, (2) a model of heart rate regulation via baroreceptor control, and (3) a sub-cellular-level model of the arteriolar smooth mus- cle. Within a light-weight ontological framework, we leverage reference ontologies to match concepts across models. The light-weight ontology then helps us combine our three models into a merged model that can answer questions beyond the scope of any single model