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The constructed GSM schemas can be refined with additional “non-default” behavior such as exceptions. In particular, we show how the constructed object life cycles can be translated to GSM schemas . The resulting object-centric design can be used as starting point for a data-centric process implementation. The approach uses synthesis rules that relate process model constructs to object life cycle constructs. This paper outlines a semi-automated approach for creating a data-centric design from a business process model that specifies the main scenarios in which business objects interact. Such translations are currently lacking in the literature, as we elaborate in Sect.
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To support this way of working, a process model specifying the default scenarios needs to be translated into a data-centric model. This allows actors to perform the process in the prescribed way for default scenarios, but respond in a flexible way to exceptional circumstances not covered by the default scenarios, which is one of the strengths of data-centric process management . In addition, extra behavior is specified for anticipated exceptional circumstances, for instance by specifying business rules that are not in the main scenarios. The backbone of the data-centric BPM system is the behavior specified by the default scenarios. In a later stage, a data-centric approach is used to actually realize a data-centric BPM system. Initially, classic process modeling techniques are used to specify the main “default” scenarios of a process, allowing users to understand and define exactly what the intended behavior is. Given these strengths of both approaches, we envision the following high-level design method for designing data-centric BPM systems. Conversely, data-centric approaches enable more flexible ways of performing business processes than activity-centric approaches, which are typically rigid . An activity-centric model shows clearly the behavior of the process, while in a data-centric model the actual behavior is difficult to predict, either since the global process is distributed over different data elements or since the behavior is specified in a declarative way, for instance in the Guard-Stage-Milestone (GSM) approach .
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In this paper, we aim to combine the strengths of both approaches. Data-centric approaches use for instance state machines or business rules as modeling techniques, while activity-centric approaches use process flow models such as UML activity diagrams or BPMN , where each modeling technique is supported by dedicated engines. These two paradigms are often positioned as alternatives, each having their own modeling techniques and implementation technologies. Moreover, data-centric modeling approaches support the specification and execution of semi-structured, knowledge-intensive business processes , which are more difficult to support using classic process modeling. Data-centric modeling approaches aim to have a more integrated perspective on business processes by treating data elements as first-class citizens next to process elements . In recent years, data-centric modeling paradigms have increasingly grown popular in research and industry as alternative to the classic, activity-centric paradigm.
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The classic way to model business processes is to specify atomic activities and their ordering in a flowchart-like, imperative process model. The synthesis approach has been implemented and tested using a graph transformation tool. Next, we define a mapping from an object-centric design to a declarative Guard-Stage-Milestone schema, which can be refined into a complete specification of a data-centric BPM system. The object-centric design specifies in an imperative way the life cycles of the objects and the object interactions. To support this vision, we define a semi-automated approach to synthesize an object-centric design from a business process model that specifies the flow of multiple stateful objects between activities. We therefore envision a data-centric process modeling approach in which the default behavior of the process is first specified in a classical, imperative process notation, which is then transformed to a declarative, data-centric process model that can be further refined into a complete model. However, it can be difficult to predict the actual behavior of a data-centric model, since the global process is typically distributed over several data elements and possibly specified in a declarative way. Data-centric business process models couple data and control flow to specify flexible business processes.
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