An Efficient Business Process Compliance Checking Approach

Lecture Notes in Computer Science, 2008

With more automation in inter-organizational supply chains and proliferation of Web services technology, the need for organizations to link their business services and processes is becoming increasingly important. Ideally, such linking must be automated and also possible to do on-the-fly in an ad hoc manner. In this paper, we view business processes in terms of standard patterns, and describe a pattern compatibility matrix and rules that allow us to simplify the task of checking compatibility between two or more processes because these prerequisite rules can be applied to each pattern separately, thus reducing the search space. We give an algorithm for applying these rules to check process compatibility. If two processes are compatible, we determine whether an adapter is required, and if so, a minimal adapter is generated by another algorithm. Two variants of the algorithm (PBA and PBA-MIN) are implemented, and experimental results and comparisons with an existing algorithm are given.

International Journal of Intelligent Engineering and Systems, 2020

In business process, similarity is important for comparing between business process models. The existing similarity methods, such as Graph-based Matching Method (GMA), Weighted Graph Edit Distance (WGED), Weighted Node Adjacent Relation Similarity (WNARS), Tree Declarative Pattern Edit Distance (TPED) and Cosine-Tree Declarative Pattern (Cosine-TDP) can distinguish between AND, OR, and XOR relationships. However, they have drawbacks in detecting same relationships with different event logs. This paper proposes a new similarity method based on weighted graph models called weighted graph-based parallel process model matching (WGPPM) for computing the behaviour of parallel activity relationships. The proposed method utilizes the frequency of activity relationships as the weight in the graph model to measure the similarity between processes containing parallel relationships. WGPPM is compared with GMA, WGED, WNARS, TPED, and Cosine-TDP. The result shows that WGPPM is able to compute similarity between same parallel relationship with different event logs.

2019

Business process models are commonly used to document a company's operations. They describe internal processes in a chronological and logical order. Business process model matching refers to the automatic detection of semantically similar correspondences in process models. The output of those matching techniques is the basis for many applications. Currently, most research effort has been undertaken to improve the performance of such matching techniques. However, to support the improvement of process model matching techniques further, efficient and fair evaluation strategies are required. Moreover, information about the matching task, regarding the complexity of a data set have to be gathered. In the current literature, complexity is mostly associated with different level of granularity, thus 1:m or n:m correspondences. However, the evaluation should also account for different complexity aspects of the matching task, for example syntactical overlap of correspondences. Moreover, t...

The rapid increase in generation of business process models in the industry has raised the demand on the development of process model matching approaches. In this paper, we introduce a novel optimization-based business process model matching approach which can flexibly incorporate both the behavioral and label information of processes for the identification of correspondences between activities. Given two business process models, we achieve our goal by defining a n i nteger l inear p rogram w hich m aximizes t he l abel s imilarities among process activities and the behavioral similarity between the process models. Our approach enables the user to determine the importance of the local label-based similarities and the global behavioral similarity of the models by offering the utilization of a predefined weighting parameter , allowing for flexibility. M oreover, e xtensive e xperimental e valuation p erformed o n three real-world datasets points out the high accuracy of our proposal, outperforming the state of the art.

Techniques and Applications

Organizations create collections of hundreds or even thousands of business process models to describe their operations. This chapter explains how graphs can be used as underlying formalism to develop techniques for managing such collections. To this end it defines the business process graph formalism. On this formalism it defines techniques for determining similarity of business process graphs. Such techniques can be used to quickly search through a collection of business process graphs to find the graph that is most relevant to a given query. These techniques can be used by tool builders that develop tools for managing large collections of business process models. The aim of the chapter is to provide an overview of the research area of using graphs to do similarity search and matching of business processes.

2013 IEEE International Conference on Services Computing, 2013

The importance gained by the process models in modern information systems led to the increasing proliferation of process model repositories. Matching process models and assessing their similarity are critical functionalities required for the management of these collections. In this paper we present an efficient graph-based technique for matching and evaluating the similarity of semantically annotated process models. Approximate graph matching algorithms which are used in literature (e.g., error correcting sub-graph isomorphism detection), are exponential in size of the graphs. In order to reduce the execution time and improve the applicability of the algorithm for matching and retrieval of process models, we propose a graph summarization technique which reduces the size of the graphs to be compared. Moreover, while most of the related works detect only 1-1 activity mappings, our matching approach is able to detect complex mappings (m-n) between activities based on their input/output sets. Experiments showed that the summarization technique reduces considerably the execution time, maintaining at the same time a good quality of the matching.

This article presents a method for checking the conformance between an event log capturing the actual execution of a business process, and a model capturing its expected or normative execution. Given a business process model and an event log, the method returns a set of statements in natural language describing the behavior allowed by the process model but not observed in the log and vice versa. The method relies on a unified representation of process models and event logs based on a well-known model of concurrency, namely event structures. Specifically, the problem of conformance checking is approached by folding the input event log into an event structure, unfolding the process model into another event structure, and comparing the two event structures via an error-correcting synchronized product. Each behavioral difference detected in the synchronized product is then verbalized as a natural language statement. An empirical evaluation shows that the proposed method scales up to real-life datasets while producing more concise and higher-level difference descriptions than state-of-the-art conformance checking methods.

Springer eBooks, 2017

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2010

For enterprises it has become crucial to check compliance of their business processes with certain rules such as medical guidelines or financial regulations. When automating compliance checks on process models, existing approaches have mainly addressed process-specific compliance rules so far, i.e., rules that correspond to a particular process model. However, in practice, we will rather find process-independent compliance rules that are nevertheless to be checked over process models. Thus, in this paper, we present an approach that enables the instantiation and verification of process-independent compliance rules over process models using domain models. For this, we provide an intuitive visualization of compliance rules and compliance rule instances at user level and show how rules and instances can be formalized and verified at system level. The overall approach is validated by a pattern-based comparison to existing approaches and by means of a prototypical implementation.

Information Technology and Management, 2007

Business processes can be modeled using a variety of schemes such as Petri Nets, Metagraphs and UML Activity Diagrams. When information analysis is as important an objective as the proper sequencing of tasks, the metagraph formalism is the most appropriate. In practice, however, metagraphs have not achieved wide popularity. Here we propose a modification of the original formulation that eliminates some of the inconveniences that have hindered the use of this technique. We represent a business process as a Task-Precedence Metagraph (TPMG), which is a type of AND/OR graph. A TPMG is similar to a metagraph but is visually clearer and more appealing, and the algorithmic procedures are graphical rather than algebraic. We first describe the proposed representation scheme for TPMGs and present a simple graph-search algorithm for the analysis of information flow. This can be readily extended to perform task analysis, resource analysis, and operational (i.e., semantic) verification. We then present a generalized graphical algorithm for structural (i.e., syntactic) verification that runs correctly not only on TPMGs containing directed cycles, but even on those that have overlapping patterns.