Events and Activities: Is there an Ontology behind BPMN?

In the context of business process modelling, the Business Process Model and Notation (BPMN) is a de-facto standard with more than 70 commercial tools that currently support its use. Amongst its main modelling constructs, BPMN includes activities and events. However, the focus of the standard is on providing an intuitive graphical language, rather than formal semantics specifications. This results in semantic ambiguities regarding the interpretation of its modelling constructs. We investigate whether the main building blocks of BPMN commit to an ontological theory of the domain entities at hand, eventually clarifying this commitment by the approach of ontological analysis

Service Querying to Support Process Variant Development

International audienceDeveloping process variants enables enterprises to effectively adapt their business models to different markets. Existing approaches focus on business process models to support the variant development. The assignment of services in a business process, which ensures the process variability, has not been widely examined. In this paper, we present an innovative approach that focuses on component services instead of process models. We target to recommend services to a selected position in a business process. We define the service composition context as the relationships between a service and its neighbors. We compute the similarity between services based on the matching of their composition contexts. Then, we propose a query language that considers the composition context matching for service querying. We developed an application to demonstrate our approach and performed different experiments on a public dataset of real process models. Experimental results show that our approach is feasible and efficient

Software para desenho de processos de negócios semanticamente descritos : uma aplicação em uma redação jornalística

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2017.A modelagem de processos de negócios têm sido um foco recorrente nas organizações para aprimorar o fluxo de trabalho. Entretanto, atualmente as ferramentas de código aberto de modelagem de processos de negócios ainda não oferecem suporte à modelagem de processos usando a sua semântica, apesar de já existirem diversos trabalhos que formalizam a ontologia de processos e a sua anotação semântica. Com ferramentas semânticas é possível ampliar o poder da linguagem de processos de negócios e oferecer entendimento semântico sobre o significado dos elementos que compõem um processo. Dessa forma a semântica passa a ser entendida por máquina, o que amplia o auxílio na modelagem de novos processos. Neste contexto, este trabalho implementa uma ferramenta que auxilia a modelagem de novos processos em uma organização de comunicação, mais especifcamente em uma redação jornalística flexível, onde os processos podem ser criados ou reconfigurados onthe-fly. Para este fim, criou-se uma ontologia de domínio de uma redação jornalística, que pode ser classificada como leve, usando a metodologia 101 e a ferramenta prótégé. Também se utilizou o padrão BPMN para implementar um sistema de informação que sugerisse automaticamente o papel mais indicado para executar uma determinada tarefa num processo de produção de notícia de uma redação jornalística. Para alcançar este objetivo foi desenvolvido uma ferramenta para auxiliar na anotação semântica do domínio de aplicação para o qual os processos são voltados e enriquecer o poder da modelagem de processos no contexto semântico. De fato, seguido de regras de implementação, a ferramenta propostas evita alocação de tarefas à papéis indevidos.Business process modeling has been a recurring focus in organizations to improve it’s workflow. However, today’s business process modeling open-source tools still do not support process modeling using their semantics, although there are already several papers that formalize the process ontology and its semantic annotation. With semantic tools it is possible to extend the power of the business process language and offer semantic understanding about the meaning of the elements that make up a process. In this way the semantics becomes understood by machine, which increases the aid in the modeling of new processes. In this context, this work implements a tool that assists a modeling of new processes in a communication organization, more specifically in a flexible newsroom, in which process could be created or modified on the fly. To this end, it was created a domain ontology for newsroom classified as light, using the methodology 101 and protégé modeling tool. The BPMN standard was also used to implement the information system that would automatically suggest the most appropriate role to perform a given task in a news production process of a newsroom. In order To achieve this goal, a tool was developed to aid in the semantic annotation of the application domain to which the processes are addressed and to enrich the power of process modeling in the semantic context. In fact, followed by implementation rules, the proposed tool avoids assigning tasks to improper roles

Semantic Model Alignment for Business Process Integration

Business process models describe an enterprise’s way of conducting business and in this form the basis for shaping the organization and engineering the appropriate supporting or even enabling IT. Thereby, a major task in working with models is their analysis and comparison for the purpose of aligning them. As models can differ semantically not only concerning the modeling languages used, but even more so in the way in which the natural language for labeling the model elements has been applied, the correct identification of the intended meaning of a legacy model is a non-trivial task that thus far has only been solved by humans. In particular at the time of reorganizations, the set-up of B2B-collaborations or mergers and acquisitions the semantic analysis of models of different origin that need to be consolidated is a manual effort that is not only tedious and error-prone but also time consuming and costly and often even repetitive. For facilitating automation of this task by means of IT, in this thesis the new method of Semantic Model Alignment is presented. Its application enables to extract and formalize the semantics of models for relating them based on the modeling language used and determining similarities based on the natural language used in model element labels. The resulting alignment supports model-based semantic business process integration. The research conducted is based on a design-science oriented approach and the method developed has been created together with all its enabling artifacts. These results have been published as the research progressed and are presented here in this thesis based on a selection of peer reviewed publications comprehensively describing the various aspects

Data-driven conceptual modeling: how some knowledge drivers for the enterprise might be mined from enterprise data

As organizations perform their business, they analyze, design and manage a variety of processes represented in models with different scopes and scale of complexity. Specifying these processes requires a certain level of modeling competence. However, this condition does not seem to be balanced with adequate capability of the person(s) who are responsible for the task of defining and modeling an organization or enterprise operation. On the other hand, an enterprise typically collects various records of all events occur during the operation of their processes. Records, such as the start and end of the tasks in a process instance, state transitions of objects impacted by the process execution, the message exchange during the process execution, etc., are maintained in enterprise repositories as various logs, such as event logs, process logs, effect logs, message logs, etc. Furthermore, the growth rate in the volume of these data generated by enterprise process execution has increased manyfold in just a few years. On top of these, models often considered as the dashboard view of an enterprise. Models represents an abstraction of the underlying reality of an enterprise. Models also served as the knowledge driver through which an enterprise can be managed. Data-driven extraction offers the capability to mine these knowledge drivers from enterprise data and leverage the mined models to establish the set of enterprise data that conforms with the desired behaviour. This thesis aimed to generate models or knowledge drivers from enterprise data to enable some type of dashboard view of enterprise to provide support for analysts. The rationale for this has been started as the requirement to improve an existing process or to create a new process. It was also mentioned models can also serve as a collection of effectors through which an organization or an enterprise can be managed. The enterprise data refer to above has been identified as process logs, effect logs, message logs, and invocation logs. The approach in this thesis is to mine these logs to generate process, requirement, and enterprise architecture models, and how goals get fulfilled based on collected operational data. The above a research question has been formulated as whether it is possible to derive the knowledge drivers from the enterprise data, which represent the running operation of the enterprise, or in other words, is it possible to use the available data in the enterprise repository to generate the knowledge drivers? . In Chapter 2, review of literature that can provide the necessary background knowledge to explore the above research question has been presented. Chapter 3 presents how process semantics can be mined. Chapter 4 suggest a way to extract a requirements model. The Chapter 5 presents a way to discover the underlying enterprise architecture and Chapter 6 presents a way to mine how goals get orchestrated. Overall finding have been discussed in Chapter 7 to derive some conclusions

Ontology-based representation and generation of workflows for micro-task human-machine computation

Doctoral Program in Computer ScienceA crescente popularidade das plataformas de crowdsourcing de micro-tarefas levou ao aparecimento de novas abordagens baseadas em fluxos e workflows de micro-tarefas. Juntamente com estas novas abordagens, surgem novos desafios. A falta de estruturação dos dados das micro-tarefas torna difícil, por parte de quem solicita as tarefas, a inclusão de participantes máquina no processo de execução dos workflows. Outro desafio deve-se á falta de componentes que permitam o controlo do fluxo em workflows de micro-tarefas, embora estes componentes sejam comuns em abordagens de workflow tradicionais e em processos de negócio. Nesta tese, é proposto um método para a representação, construção, instanciação e execução de workflows de tarefas em ambientes de computação pessoa-máquina, baseado em ontologias. A representação é capaz de capturar a estrutura e a semântica das operações e dos seus dados, ao mesmo tempo que se mantém próxima do nível conceptual humano. Os workflows são construidos em duas dimensões: a dimensão de domínio estático e a dimensão (da tarefa) dinâmica. Isto permite que os dados de entrada e de saída dos workflows possam ser descritos exclusivamente de acordo com uma ontologia de domínio, de forma completamente independente da representação do workflow. Para que possa ser efetuada a instanciação e a execução da representação do workflow, foi implementado um motor de workflows baseado no método proposto. Para facilitar o papel do solicitador (ou requester) na criação de novas representações de workflows (ou workflow-definitions), um processo de construção semi-automático baseado em ontologias de domínio é também proposto. O processo foi implementado numa ferramenta de construção que permite a construção assistida, iterativa e visual de representações de workflows. O método de representação e o processo de construção propostos são avaliados através de múltiplos cenários de aplicação em diferentes domínios.The growing popularity of micro-task crowdsourcing platforms has led to new approaches based on workflows of micro-tasks. Along with these new approaches, new challenges have emerged. The unstructured nature of micro-tasks in terms of domain representation makes it difficult for task requesters to include machine workers in the workflow execution process. Also, the representation of these human-machine computation workflows lack the flow control components often found in traditional workflow and business process approaches. In this thesis, a method for the representation, construction, instantiation and execution of human-machine computation task workflows through ontologies is proposed. The representation captures the structure and semantics of the tasks and their domain, while remaining close to the human conceptual level. Workflows are built according to two dimensions: the static domain dimension and the dynamic (task) dimension. This allows the input and the output of workflows to be described according to a domain ontology, completely independent from the workflow representation. The instantiation and execution of the represented workflow can be performed through the implemented workflow engine. To aid the requester in the creation of new workflow representations (or workflowdefinitions), a semi-automatic construction process based on domain ontologies is also proposed. The process has been implemented into a construction framework that allows the aided, iterative and visual construction of workflow-definitions. The proposed method and construction process is evaluated through several application scenarios in different domains.Portuguese Foundation for Science and Technology within the doctoral grant SFRH/BD/ 70302/2010

Reasoning on Semantically Annotated Processes

Enriching business process models with semantic tags taken from an ontology has become a crucial necessity in service provisioning, integration and composition. In this paper we propose to represent semantically labelled business processes as part of a knowledge base that formalises: business process structure, business domains, and a set of criteria describing correct semantic labelling. Our approach allows (1) to impose domain dependent constraints during the phase of process design, and (2) to automatically verify, via logical reasoning, if business processes fulfill a set of given constraints, and to formulate queries that involve both knowledge about the domain and the process structure. Feasibility and usefulness of our approach will be shown by means of two use cases. The first one on domain specific constraints, and the second one on mining and evolution of crosscutting concerns

Reasoning on Semantically Annotated Processes

Enriching business process models with semantic tags taken from an ontology has become a crucial necessity in service provisioning, integration and composition. In this paper we propose to represent semantically labelled business processes as part of a knowledge base that formalises: business process structure, business domains, and a set of criteria describing correct semantic labelling. Our approach allows (1) to impose domain dependent constraints during the phase of process design, and (2) to automatically verify, via logical reasoning, if business processes fulfill a set of given constraints, and to formulate queries that involve both knowledge about the domain and the process structure. Feasibility and usefulness of our approach will be shown by means of two use cases. The first one on domain specific constraints, and the second one on mining and evolution of crosscutting concerns