Data in Business Process Models. A Preliminary Empirical Study

Traditional activity-centric process modeling languages treat data as simple black boxes acting as input or output for activities. Many alternate and emerging process modeling paradigms, such as case handling and artifact-centric process modeling, give data a more central role. This is achieved by introducing lifecycles and states for data objects, which is beneficial when modeling data-or knowledge-intensive processes. We assume that traditional activity-centric process modeling languages lack the capabilities to adequately capture the complexity of such processes. To verify this assumption we conducted an online interview among BPM experts. The results not only allow us to identify various profiles of persons modeling business processes, but also the problems that exist in contemporary modeling languages w.r.t. The modeling of business data. Overall, this preliminary empirical study confirms the necessity of data-awareness in process modeling notations in general

Enhancing declarative process models with DMN decision logic

Modeling dynamic, human-centric, non-standardized and knowledge-intensive business processes with imperative process modeling approaches is very challenging. Declarative process modeling approaches are more appropriate for these processes, as they offer the run-time flexibility typically required in these cases. However, by means of a realistic healthcare process that falls in the aforementioned category, we demonstrate in this paper that current declarative approaches do not incorporate all the details needed. More specifically, they lack a way to model decision logic, which is important when attempting to fully capture these processes. We propose a new declarative language, Declare-R-DMN, which combines the declarative process modeling language Declare-R with the newly adopted OMG standard Decision Model and Notation. Aside from supporting the functionality of both languages, Declare-R-DMN also creates bridges between them. We will show that using this language results in process models that encapsulate much more knowledge, while still offering the same flexibility

Context-aware modeling for knowledge-intensive medicinal product development processes

Context-Aware Modeling for Knowledge-intensive Medicinal Product Development Processe

Towards a decision-aware declarative process modeling language for knowledge-intensive processes

Modeling loosely framed and knowledge-intensive business processes with the currently available process modeling languages is very challenging. Some lack the flexibility to model this type of processes, while others are missing one or more-perspectives needed to add the necessary level of detail to the models. In this paper we have composed a list of requirements that a modeling language should fulfil in order to adequately support the modeling of this type of processes. Based on these requirements, a metamodel for a new modeling language was developed that satisfies them all. The new language, called DeciClare, incorporates parts of several existing modeling languages, integrating them with new solutions to requirements that had not yet been met, Deciclare is a declarative modeling language at its core, and therefore, can inherently deal with the flexibility required to model loosely framed processes. The complementary resource and data perspectives add the capability to reason about, respectively, resources and data values. The latter makes it possible to encapsulate the knowledge that governs the process flow by offering support for decision modeling. The abstract syntax of DeciClare has been implemented in the form of an Ecore model. Based on this implementation, the language-domain appropriateness of the language was validated by domain experts using the arm fracture case as application scenario. (C) 2017 Elsevier Ltd. All rights reserved

Modeling the role variability in the MAP process model

International audienceBusiness process modeling is a valuable technique helping organizations to specify their processes, to analyze their structure and to improve their performance. Conventional process modeling techniques are proven to be inefficient while dealing with non-repetitive, knowledge-intensive processes such as Case Management processes. In this work we use the MAP notation to model a Mortgage Approval Process as defined in Banking. To increase the navigability and practical value of map models, we extend the MAP notation with the concepts of Roles, Relations between roles, and Role Configuration Rules

Object-aware Business Processes: Fundamental Requirements and their Support in Existing Approaches

Despite the increasing maturity of process management technology not all business processes are adequately supported by it. In particular, support for unstructured and knowledge-intensive processes is missing, especially since they cannot be straight-jacketed into predefined activities. A common characteristic of these processes is the role of business objects and data as drivers for process modeling and enactment. This paper elicits fundamental requirements for effectively supporting such object-aware processes; i.e., their modeling, execution and monitoring. Based on these requirements, we evaluate imperative, declarative, and data-driven process support approaches and investigate how well they support object-aware processes. We consider a tight integration of process and data as major step towards further maturation of process management technology

DESIGNING OBJECT-ORIENTED REPRESENTATIONS FOR REASONING FROM FIRST-PRINCIPLES

Modeling expert knowledge using "situation-action" rules is not always feasible in knowledge intensive domains involving volatile knowledge (e.g., trading). The explosive search space involved in such domains and its dynamic nature make it extremely difficult to setup a rule base and keep it accurate. An alternative approach suggests that in some domains many of the rules expert use can be derived by reasoning from "first-principles". That approach entails modeling experts' deep knowledge, and emulating reasoning processes with deep knowledge that allow experts to derive many of the rules they use and justify them. This paper discusses the design and implementation of an object-oriented representation for the deep knowledge traders utilize in a business domain called hedging, which is knowledge intensive and involves volatile knowledge. It illustrates how deep knowledge modeled using that representation is used to support reasoning from first-principles. The paper also analyzes features of that representation that we have found to be extremely beneficial in the development of a knowledge-based system called INTELLIGENT-HEDGER. Based on our experience we feel that, with minor modifications, this representation can be used in other managerial domains involving financial reasoning.Information Systems Working Papers Serie

A Survey on Handling Data in Business Process Models (Discussion Paper)

Traditional activity-centric process modeling languages treat data as simple black boxes acting as input or output for activities. Many alternate and emerging process modeling paradigms, such as case handling and artifact-centric process modeling, give data a more central role. This is achieved by introducing lifecycles and states for data objects, which is beneficial when modeling data- or knowledge-intensive processes. We assume that traditional activity-centric process modeling languages lack the capabilities to adequately capture the complexity of such processes. To verify this assumption, we conducted a survey among Business Process Management experts. The survey results allow us to identify the problems of contemporary modeling languages in regard to the modeling of business data. To this end, survey respondents rated the data modeling capabilities of a variety of business process modeling tools and notations. Overall, the paper confirms the need of data-awareness in process modeling notations in general

Object-aware Business Processes: Properties, Requirements, Existing Approaches

Despite the increasing maturity of process management technology not all business processes are adequately supported by it. In particular, support for unstructured and knowledge-intensive processes is missing, especially since they cannot be straight-jacketed into predefined activities. A common characteristic of these processes is the role of busi-ness objects and data as drivers for process modeling and enactment. This paper elicits fundamental requirements for effectively supporting such object-aware processes; i.e., their modeling, execution and monitoring. Based on these requirements, we evaluate imperative, declarative, and data-driven process support approaches and investigate how well they support object-aware processes. We consider a tight integration of process and data as major step towards further maturation of process management technology

Techniques for organizational memory information systems

The KnowMore project aims at providing active support to humans working on knowledge-intensive tasks. To this end the knowledge available in the modeled business processes or their incarnations in specific workflows shall be used to improve information handling. We present a representation formalism for knowledge-intensive tasks and the specification of its object-oriented realization. An operational semantics is sketched by specifying the basic functionality of the Knowledge Agent which works on the knowledge intensive task representation. The Knowledge Agent uses a meta-level description of all information sources available in the Organizational Memory. We discuss the main dimensions that such a description scheme must be designed along, namely information content, structure, and context. On top of relational database management systems, we basically realize deductive object- oriented modeling with a comfortable annotation facility. The concrete knowledge descriptions are obtained by configuring the generic formalism with ontologies which describe the required modeling dimensions. To support the access to documents, data, and formal knowledge in an Organizational Memory an integrated domain ontology and thesaurus is proposed which can be constructed semi-automatically by combining document-analysis and knowledge engineering methods. Thereby the costs for up-front knowledge engineering and the need to consult domain experts can be considerably reduced. We present an automatic thesaurus generation tool and show how it can be applied to build and enhance an integrated ontology /thesaurus. A first evaluation shows that the proposed method does indeed facilitate knowledge acquisition and maintenance of an organizational memory