Process mining using BPMN : relating event logs and process models

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. 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. 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 involved BPMN models in comparison with the initial models are identified. Keywords: Process mining; Process discovery; Conformance checking; BPMN (Business Process Model and Notation); Petri nets; Bisimulatio

Adaptive workflow nets for grid computing

Existing grid applications commonly use workflows for the orchestration of grid services. Existing workflow models however suffer from the lack of adaptivity. In this paper we define Adaptive Grid Workflow nets (AGWF nets) appropriate for modeling grid workflows and allowing changes in the process structure as a response to triggering events/exceptions. Moreover, a recursion is allowed, which makes the model especially appropriate for a number of grid applications. We show that soundness can be verified for AGWF nets

Adaptive Workflow Nets for Grid Computing

Abstract. Existing grid applications commonly use workflows for the orchestration of grid services. Existing workflow models however suf-fer from the lack of adaptivity. In this paper we define Adaptive Grid Workflow nets (AGWF nets) appropriate for modeling grid workflows and allowing changes in the process structure as a response to trigger-ing events/exceptions. Moreover, a recursion is allowed, which makes the model especially appropriate for a number of grid applications. We show that soundness can be verified for AGWF nets

Process mining using BPMN: relating event logs and process models

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. Keywords: Process mining Process discovery Conformance checking BPMN (Business Process Model and Notation) Petri nets Bisimulatio

Process mining using BPMN: relating event logs and process models

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. Keywords: Process mining Process discovery Conformance checking BPMN (Business Process Model and Notation) Petri nets Bisimulatio

Process model discovery : a method based on transition system decomposition

Process mining aims to discover and analyze processes by extracting information from event logs. Process mining discovery algorithms deal with large data sets to learn automatically process models. As more event data become available there is the desire to learn larger and more complex process models. To tackle problems related to the readability of the resulting model and to ensure tractability, various decomposition methods have been proposed. This paper presents a novel decomposition approach for discovering more readable models from event logs on the basis of a priori knowledge about the event log structure: regular and special cases of the process execution are treated separately. The transition system, corresponding to a given event log, is decomposed into a regular part and a specific part. Then one of the known discovery algorithms is applied to both parts, and finally these models are combined into a single process model. It is proven, that the structural and behavioral properties of submodels are inherited by the unified process model. The proposed discovery algorithm is illustrated using a running example

Nested nets for adaptive systems

We consider nested nets, i.e. Petri nets in which tokens can be Petri nets themselves. We study value semantics of nested nets rather than reference semantics, and apply nested nets to model adaptive workflow, i.e. flexible workflow that can be modified during the execution. A typical domain with a great need for this kind of workflow is health care from which domain we choose the running example. To achieve the desired flexibility we allow transitions that create new nets out of the existing ones. Therefore, nets with completely new structure can be created at the run time. We show that by careful selection of basic operations on the nets we can obtain a powerful modeling formalism that enforces correctness of models. Moreover, the formalism can be implemented based on existing workflow engines

Checking properties of adaptive workflow nets

n this paper we consider adaptive workflow nets, a class of nested nets that allows more comfort and expressive power for modeling adaptability and exception handling in workflow nets. We define two important behavioural properties of adaptive workflow nets: soundness and circumspectness. Soundness means that a proper final marking (state) can be reached from any marking which is reachable from the initial marking, and no garbage will be left. Circumspectness means that the upper layer is always ready to handle any exception that can happen in a lower layer. We define a finite state abstraction for adaptive workflow nets and show that soundness and circumspectness can be verified on this abstraction

Nested nets for adaptive systems

We consider nested nets, i.e. Petri nets in which tokens can be Petri nets themselves. We study value semantics of nested nets rather than reference semantics, and apply nested nets to model adaptive workflow, i.e. flexible workflow that can be modified during the execution. A typical domain with a great need for this kind of workflow is health care from which domain we choose the running example. To achieve the desired flexibility we allow transitions that create new nets out of the existing ones. Therefore, nets with completely new structure can be created at the run time. We show that by careful selection of basic operations on the nets we can obtain a powerful modeling formalism that enforces correctness of models. Moreover, the formalism can be implemented based on existing workflow engines

Nested nets for adaptive systems

We consider nested nets, i.e. Petri nets in which tokens can be Petri nets themselves. We study the value semantics of nested nets rather than the reference semantics, and apply nested nets to model adaptive workflow, i.e. flexible workflow that can be modified during the execution. A typical domain with a great need for this kind of workflow is health care, from which domain we choose the running example. To achieve the desired flexibility we allow transitions that create new nets out of the existing ones. Therefore, nets with completely new structure can be created at the run time. We show that by careful selection of basic operations on the nets we can obtain a powerful modeling formalism that enforces correctness of models. Moreover, the formalism can be implemented based on existing workflow engines