Refined Process Structure Tree Research Articles

Scalable and Optimal QoS-Aware Manufacturing Service Composition via Business Process Decomposition

With the adoption of service-oriented manufacturing modes, more and more manufacturing services are released over manufacturing service platforms. As it is known, the problem of the QoS (Quality of Service)-aware manufacturing service composition is NP-hard. Thus, the optimization remains a challenging research issue, especially in the situation of large-scale manufacturing service data which arouse a scalability problem as well. To improve both the optimization performance and scalability of the QoS-aware manufacturing service composition, this paper proposes a scalable and optimal QoS-aware manufacturing service composition approach via business process decomposition. Specifically, the service composition process is decomposed by using a refined process structure tree (RPST). Moreover, an optimized service composition is achieved layer by layer based on the refined process structure tree in a bottom-up manner. For the atomic tasks or the compound tasks in the same layer of RPST, the corresponding QoS-aware service selection is optimized by calculating Skyline services, which can be carried out in parallel if necessary. When the optimization arrives at the root node, the complete service composition plans are derived. In our approach, the optimal manufacturing service candidates are picked out stage by stage. In this way, both the optimality and scalability of the whole approach can be guaranteed. Extensive experiments are conducted to verify the optimality and scalability of our approach.

An alternative method for refined process structure trees (RPST)

PurposeThe refined process structure tree (RPST), the hierarchy of non-overlapping single-entry single-exit (SESE) regions of a process model, has been utilized for better comprehension and more efficient analysis of business process models. Existing RPST methods, based on the triconnected components of edges, fail to identify a certain type of SESE region. The purpose of this paper is to introduce an alternative method for generating a complete RPST utilizing rather simple techniques.Design/methodology/approachThe proposed method first focuses on the SESE regions of bonds and rigids, from the innermost ones to the outermost ones, utilizing dominance and post-dominance relations. Then, any SESE region of a series nested in a bond or a rigid is identified with a depth-first search variation. Two-phase algorithms and their completeness proofs, a software tool incorporating visualization of stepwise outcomes, and the experimental results of the proposed method are provided.FindingsThe proposed method utilizes simple techniques that allow their straightforward implementation. Visualization of stepwise outcomes helps process analysts to understand the proposed method and the SESE regions. Experiments with 604 SAP reference models demonstrated the limitation of the existing RPST methods. The proposed method, however, completely identified all types of SESE regions, defined with nodes, in less computation time than with the old methods.Originality/valueEach triconnected component of the undirected version of a process model is associated with a pair of boundary nodes without discriminating between the entry and the exit. Here, each non-atomic SESE region is associated with two distinct entry and exit nodes from the original model in the form of a directed graph. By specifying the properties of SESE regions in more comprehensible ways, this paper facilitates a deeper understanding of SESE regions rather than relying on the resulting RPST.

Decomposed and parallel process discovery: A framework and application

Abstract The rapid growth of event data motivates the developing of decomposed and parallel process discovery, which solves process discovery from a large event log by decomposing the log into multiple small event logs, discovering process models from these small logs in parallel, and merging discovered process models. As such, process discovery from a large event log can be solved in less time. Currently, the passage and maximal decomposition based techniques are successful in the activity partition by decomposing causal graph structure derived from a large event log. In this paper, we propose a five-step framework, based on which we can build various decomposed and parallel process discovery techniques by simply combining and adapting existing techniques. Then, we propose a technique, RPSTHD, based on the framework using the refined process structure tree (RPST), heuristic miner, process mining using integer linear programming (ILP), etc. An experimental evaluation shows that our technique significantly outperforms the state-of-the-art decomposed discovery techniques in both efficiency and effectiveness.

Detecting Difference between Process Models Based on the Refined Process Structure Tree

The development of mobile workflow management systems (mWfMS) leads to large number of business process models. In the meantime, the location restriction embedded in mWfMS may result in different process models for a single business process. In order to help users quickly locate the difference and rebuild the process model, detecting the difference between different process models is needed. Existing detection methods either provide a dissimilarity value to represent the difference or use predefined difference template to generate the result, which cannot reflect the entire composition of the difference. Hence, in this paper, we present a new approach to solve this problem. Firstly, we parse the process models to their corresponding refined process structure trees (PSTs), that is, decomposing a process model into a hierarchy of subprocess models. Then we design a method to convert the PST to its corresponding task based process structure tree (TPST). As a consequence, the problem of detecting difference between two process models is transformed to detect difference between their corresponding TPSTs. Finally, we obtain the difference between two TPSTs based on the divide and conquer strategy, where the difference is described by an edit script and we make the cost of the edit script close to minimum. The extensive experimental evaluation shows that our method can meet the real requirements in terms of precision and efficiency.

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.

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.

Fast detection of exact clones in business process model repositories

As organizations reach higher levels of business process management maturity, they often find themselves maintaining very large process model repositories, representing valuable knowledge about their operations. A common practice within these repositories is to create new process models, or extend existing ones, by copying and merging fragments from other models. We contend that if these duplicate fragments, a.k.a. exact clones, can be identified and factored out as shared subprocesses, the repository's maintainability can be greatly improved. With this purpose in mind, we propose an indexing structure to support fast detection of clones in process model repositories. Moreover, we show how this index can be used to efficiently query a process model repository for fragments. This index, called RPSDAG, is based on a novel combination of a method for process model decomposition (namely the Refined Process Structure Tree), with established graph canonization and string matching techniques. We evaluated the RPSDAG with large process model repositories from industrial practice. The experiments show that a significant number of non-trivial clones can be efficiently found in such repositories, and that fragment queries can be handled efficiently.

The refined process structure tree

We consider a workflow graph as a model for the control flow of a business process and study the problem of workflow graph parsing, i.e., finding the structure of a workflow graph. More precisely, we want to find a decomposition of a workflow graph into a hierarchy of sub-workflows that are subgraphs with a single entry and a single exit of control. Such a decomposition is the crucial step, for example, to translate a process modeled in a graph-based language such as BPMN into a process modeled in a block-based language such as BPEL. For this and other applications, it is desirable that the decomposition be unique, modular and as fine as possible, where modular means that a local change of the workflow graph can only cause a local change of the decomposition. In this paper, we provide a decomposition that is unique, modular and finer than in previous work. We call it the refined process structure tree. It is based on and extends similar work for sequential programs by Tarjan and Valdes [ACM POPL ’80, 1980, pp. 95–105]. We give two independent characterizations of the refined process structure tree which we prove to be equivalent: (1) a simple descriptive characterization that justifies our particular choice of the decomposition and (2) a constructive characterization that allows us to compute the decomposition in linear time. The latter is based on the tree of triconnected components (elsewhere also known as the SPQR tree) of a biconnected graph.