Simplified computation and generalization of the refined process structure tree

A business process is often modeled using some kind of a directed flow graph, which we call a workflow graph. The Refined Process Structure Tree (RPST) is a technique for workflow graph parsing, i.e., for discovering the structure of a workflow graph, which has various applications. In this paper, we provide two improvements to the RPST. First, we propose an alternative way to compute the RPST that is simpler than the one developed originally. In particular, the computation reduces to constructing the tree of the triconnected components of a workflow graph in the special case when every node has at most one incoming or at most one outgoing edge. Such graphs occur frequently in applications. Secondly, we extend the applicability of the RPST. Originally, the RPST was applicable only to graphs with a single source and single sink such that the completed version of the graph is biconnected. We lift both restrictions. Therefore, the RPST is then applicable to arbitrary directed graphs such that every node is on a path from some source to some sink. This includes graphs with multiple sources and/or sinks and disconnected graphs

Towards a compendium of process technologies: The jBPT library for process model analysis

This paper presents the idea of a compendium of process technologies, i.e., a concise but comprehensive collection of techniques for process model analysis that support research on the design, execution, and evaluation of processes. The idea originated from observations on the evolution of process-related research disciplines. Based on these observations, we derive design goals for a compendium. Then, we present the jBPT library, which addresses these goals by means of an implementation of common analysis techniques in an open source codebase

Change Propagation in Collaborative Processes Scenarios

Process flexibility and change constitute major challenges for process-aware information systems. This does not only hold for centralized process scenarios, but also for collaborative ones involving multiple distributed and autonomous partners. If one partner adapts its private process, the applied change might affect the processes of the other partners as well. Hence the change must be propagated to concerned partners in a transitive way. A fundamental challenge is then to find ways of propagating the changes in a decentralized manner. Existing approaches dealing with changes of collaborative processes are limited with respect to the change operations considered and their dependency on certain process specification languages. By contrast, this paper presents a generic change propagation approach based on the Refined Process Structure Tree. Our approach is applicable independently of a particular process specification language. Further, it considers a comprehensive set of change patterns. Finally, it is shown that the provided change propagation algorithms preserve structural dependencies for any change pattern

Dealing with change in process choreographies: Design and implementation of propagation algorithms

Enabling process changes constitutes a major challenge for any process-aware information system. This not only holds for processes running within a single enterprise, but also for collaborative scenarios involving distributed and autonomous partners. In particular, if one partner adapts its private process, the change might affect the processes of the other partners as well. Accordingly, it might have to be propagated to concerned partners in a transitive way. A fundamental challenge in this context is to find ways of propagating the changes in a decentralized manner. Existing approaches are limited with respect to the change operations considered as well as their dependency on a particular process specification language. This paper presents a generic change propagation approach that is based on the Refined Process Structure Tree, i.e., the approach is independent of a specific process specification language. Further, it considers a comprehensive set of change patterns. For all these change patterns, it is shown that the provided change propagation algorithms preserve consistency and compatibility of the process choreography. Finally, a proof-of-concept prototype of a change propagation framework for process choreographies is presented. Overall, comprehensive change support in process choreographies will foster the implementation and operational support of agile collaborative process scenarios

The structured phase of concurrency

This extended abstract summarizes the state-of-the-art solution to the structuring problem for models that describe existing real world or envisioned processes. Special attention is devoted to models that allow for the true concurrency semantics. Given a model of a process, the structuring problem deals with answering the question of whether there exists another model that describes the process and is solely composed of structured patterns, such as sequence, selection, option for simultaneous execution, and iteration. Methods and techniques for structuring developed by academia as well as products and standards proposed by industry are discussed. Expectations and recommendations on the future advancements of the structuring problem are suggested

Business Process Modeling Abstraction Based on Semi-Supervised Clustering Analysis

The most prominent Business Process Model Abstraction (BPMA) use case is the construction of the process “quick view” for rapidly comprehending a complex process. Some researchers propose process abstraction methods to aggregate the activities on the basis of their semantic similarity. One important clustering technique used in these methods is traditional k-means cluster analysis which so far is an unsupervised process without any priori information, and most of the techniques aggregate the activities only according to business semantics without considering the requirement of an order-preserving model transformation. The paper proposes a BPMA method based on semi-supervised clustering which chooses the initial clusters based on the refined process structure tree and designs constraints by combining the control flow consistency of the process and the semantic similarity of the activities to guide the clustering process. To be more precise, the constraint function is discovered by mining from a process model collection enriched with subprocess relations. The proposed method is validated by applying it to a process model repository in use. In an experimental validation, the proposed method is compared to the traditional k-means clustering (parameterized with randomly chosen initial clusters and an only semantics-based distance measure), showing that the approach closely approximates the decisions of the involved modelers to cluster activities. As such, the paper contributes to the development of modeling support for effective process model abstraction, facilitating the use of business process models in practice

Isotactics as a foundation for alignment and abstraction of behavioral models

There are many use cases in business process management that require the comparison of behavioral models. For instance, verifying equivalence is the basis for assessing whether a technical workflow correctly implements a business process, or whether a process realization conforms to a reference process. This paper proposes an equivalence relation for models that describe behaviors based on the concurrency semantics of net theory and for which an alignment relation has been defined. This equivalence, called isotactics, preserves the level of concurrency of aligned operations. Furthermore, we elaborate on the conditions under which an alignment relation can be classified as an abstraction. Finally, we show that alignment relations induced by structural refinements of behavioral models are indeed behavioral abstractions

Decomposing conformance checking on Petri nets with data

Process mining techniques relate observed behavior to modeled behavior, e.g., the automatic discovery of a Petri net based on an event log. Process mining is not limited to process discovery and also includes conformance checking. Conformance checking techniques are used for evaluating the quality of discovered process models and to diagnose deviations from some normative model (e.g., to check compliance). Existing conformance checking approaches typically focus on the control flow, thus being unable to diagnose deviations concerning data. This paper proposes a technique to check the conformance of data-aware process models. We use so-called "data Petri nets" to model data variables, guards, and read/write actions. Additional perspectives such as resource allocation and time constraints can be encoded in terms of variables. Data-aware conformance checking problem may be very time consuming and sometimes even intractable when there are many transitions and data variables. Therefore, we propose a technique to decompose large data-aware conformance checking problems into smaller problems that can be solved more efficiently. We provide a general correctness result showing that decomposition does not influence the outcome of conformance checking. Moreover, two decomposition strategies are presented. The approach is supported through ProM plug-ins and experimental results show that significant performance improvements are indeed possible

A Practical Data-Flow Verification Scheme for Business Processes

Data in business processes is becoming more and more important. Current standards for process-modeling languages like BPMN 2.0 which include the data flow reflect this. Ensuring the correctness of the data flow in processes is challenging. Model checking, i. e., verifying properties of process models, is a well-known technique to this end. An important part of model checking is the construction of the state space of the model. State-space explosion however typically is in the way of an effective verification. We study how to overcome this problem in our context by means of reduction. More specifically, we propose a reduction on the level of the process model. To our knowledge, this is new for the data-flow analysis of processes. To accomplish this, we specify regions relevant for the verification of properties describing the data flow. Our evaluation shows that our approach works well on real process models