A knowledge-intensive approach to process similarity calculation

Process model comparison and similar processes retrieval are key issues to be addressed in many real world situations, and particularly relevant ones in some applications (e.g., in medicine), where similarity quantification can be exploited in a quality assessment perspective. Most of the process comparison techniques described in the literature suffer from two main limitations: (1) they adopt a purely syntactic (vs. semantic) approach in process activity comparison, and/or (2) they ignore complex control flow information (i.e., other than sequence). These limitations oversimplify the problem, and make the results of similarity-based process retrieval less reliable, especially when domain knowledge is available, and can be adopted to quantify activity or control flow construct differences. In this paper, we aim at overcoming both limitations, by introducing a framework which allows to extract the actual process model from the available process execution traces, through process mining techniques, and then to compare (mined) process models, by relying on a novel distance measure. The novel distance measure, which represents the main contribution of this paper, is able to address issues (1) and (2) above, since: (1) it provides a semantic, knowledge-intensive approach to process activity comparison, by making use of domain knowledge; (2) it explicitly takes into account complex control flow constructs (such as AND and XOR splits/joins), thus fully considering the different semantic meaning of control flow connections in a reliable way. The positive impact of the framework in practice has been tested in stroke management, where our approach has outperformed a state-of-the art literature metric on a real world event log, providing results that were closer to those of a human expert. Experiments in other domains are foreseen in the future

Measuring Semantic and Structural Information for Data Oriented Workflow Retrieval with Cost Constraints

The reuse of data oriented workflows (DOWs) can reduce the cost of workflow system development and control the risk of project failure and therefore is crucial for accelerating the automation of business processes. Reusing workflows can be achieved by measuring the similarity among candidate workflows and selecting the workflow satisfying requirements of users from them. However, due to DOWs being often developed based on an open, distributed, and heterogeneous environment, different users often can impose diverse cost constraints on data oriented workflows. This makes the reuse of DOWs challenging. There is no clear solution for retrieving DOWs with cost constraints. In this paper, we present a novel graph based model of DOWs with cost constraints, called constrained data oriented workflow (CDW), which can express cost constraints that users are often concerned about. An approach is proposed for retrieving CDWs, which seamlessly combines semantic and structural information of CDWs. A distance measure based on matrix theory is adopted to seamlessly combine semantic and structural similarities of CDWs for selecting and reusing them. Finally, the related experiments are made to show the effectiveness and efficiency of our approach

A graph distance based metric for data oriented workflow retrieval with variable time constraints

There are many applications in business process management that require measuring the similarity between business processes, such as workflow retrieval and process mining, etc. However, most existing approaches and models cannot represent variable constraints and achieve data oriented workflow retrieval of considering different QoS requirements, and also fail to allow users to express arbitrary constraints based on graph structures of workflows. These problems will impede the customization and reuse of workflows, especially for data oriented workflows. In this paper, we will be towards workflow retrieval with variable time constraints. We propose a graph distance based approach for measuring the similarity between data oriented workflows with variable time constraints. First, a formal structure called Time Dependency Graph (TDG) is proposed and further used as representation model of workflows. Similarity comparison between two workflows can be reduced to computing the similarity between their TDGs. Second, we detect whether two TDGs of workflows for similarity comparison are compatible. A distance based measure is proposed for computing their similarity by their normalization matrices established based on their TDGs. We theoretically proof that the proposed measure satisfies the all the properties of distance. In addition, some exemplar processes are studied to illustrate the effectiveness of our approach of similarity comparison for workflows. © 2013 Elsevier Ltd. All rights reserved.There are many applications in business process management that require measuring the similarity between business processes, such as workflow retrieval and process mining, etc. However, most existing approaches and models cannot represent variable constraints and achieve data oriented workflow retrieval of considering different QoS requirements, and also fail to allow users to express arbitrary constraints based on graph structures of workflows. These problems will impede the customization and reuse of workflows, especially for data oriented workflows. In this paper, we will be towards workflow retrieval with variable time constraints. We propose a graph distance based approach for measuring the similarity between data oriented workflows with variable time constraints. First, a formal structure called Time Dependency Graph (TDG) is proposed and further used as representation model of workflows. Similarity comparison between two workflows can be reduced to computing the similarity between their TDGs. Second, we detect whether two TDGs of workflows for similarity comparison are compatible. A distance based measure is proposed for computing their similarity by their normalization matrices established based on their TDGs. We theoretically proof that the proposed measure satisfies the all the properties of distance. In addition, some exemplar processes are studied to illustrate the effectiveness of our approach of similarity comparison for workflows. © 2013 Elsevier Ltd. All rights reserved

Aplicação do Process Mining na Auditoria de Processos Governamentais

A auditoria de processos de negócios é um tema de relevância crescente na literatura. No entanto, técnicas tradicionais e manuais demonstram-se insatisfatórias ou insuficientes, visto que as mesmas são custosas, podem ser tendenciosas e passíveis de erros, além de envolverem grande quantidade de recursos temporais, humanos e materiais. Nesse sentido, o presente estudo vem demonstrar como a técnica de process mining pode ser utilizada, de forma automática, na auditoria de processos governamentais, a partir de um sistema de informação e de uma ferramenta de mining denominada ProM. A partir de técnicas de verificação de conformidade, realizou-se a comparação entre os processos reais e seus respectivos modelos oficiais de uma organização governamental. Os resultados obtidos demonstram algumas divergências entre eles, e indicam que a técnica pode ser utilizada como um meio auxiliar na realização de auditoria de processos de negócios

Understanding Legacy Workflows through Runtime Trace Analysis

abstract: When scientific software is written to specify processes, it takes the form of a workflow, and is often written in an ad-hoc manner in a dynamic programming language. There is a proliferation of legacy workflows implemented by non-expert programmers due to the accessibility of dynamic languages. Unfortunately, ad-hoc workflows lack a structured description as provided by specialized management systems, making ad-hoc workflow maintenance and reuse difficult, and motivating the need for analysis methods. The analysis of ad-hoc workflows using compiler techniques does not address dynamic languages - a program has so few constrains that its behavior cannot be predicted. In contrast, workflow provenance tracking has had success using run-time techniques to record data. The aim of this work is to develop a new analysis method for extracting workflow structure at run-time, thus avoiding issues with dynamics. The method captures the dataflow of an ad-hoc workflow through its execution and abstracts it with a process for simplifying repetition. An instrumentation system first processes the workflow to produce an instrumented version, capable of logging events, which is then executed on an input to produce a trace. The trace undergoes dataflow construction to produce a provenance graph. The dataflow is examined for equivalent regions, which are collected into a single unit. The workflow is thus characterized in terms of its treatment of an input. Unlike other methods, a run-time approach characterizes the workflow's actual behavior; including elements which static analysis cannot predict (for example, code dynamically evaluated based on input parameters). This also enables the characterization of dataflow through external tools. The contributions of this work are: a run-time method for recording a provenance graph from an ad-hoc Python workflow, and a method to analyze the structure of a workflow from provenance. Methods are implemented in Python and are demonstrated on real world Python workflows. These contributions enable users to derive graph structure from workflows. Empowered by a graphical view, users can better understand a legacy workflow. This makes the wealth of legacy ad-hoc workflows accessible, enabling workflow reuse instead of investing time and resources into creating a workflow.Dissertation/ThesisMasters Thesis Computer Science 201