• Proceedings of the CALSEC Conference
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• 2005.03a
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• pp.195-200
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• 2005

As the workflow systems are becoming complex and obscure, there are discrepancies between actual workflow process and designed process. Therefore, we have developed techniques for discovering workflow models. The starting point for such techniques is a so-called 'workflow log' containing information about the workflow process as it is actually being executed. This paper presents an algorithm of workflow process mining based on heuristic approach from the workflow log, which can be happen to business process system.

• Journal of Internet Computing and Services
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• v.20 no.3
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• pp.85-92
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• 2019

In this paper, we extract the quantitative relation data of activities from the workflow event log file recorded in the XES standard format and connect them to rediscover the workflow process model. Extract the workflow process patterns and proportions with the rediscovered model. There are four types of control-flow elements that should be used to extract workflow process patterns and portions with log files: linear (sequential) routing, disjunctive (selective) routing, conjunctive (parallel) routing, and iterative routing patterns. In this paper, we focus on four of the factors, disjunctive routing, and conjunctive path. A framework implemented by the authors' research group extracts and arranges the activity data from the log and converts the iteration of duplicate relationships into a quantitative value. Also, for accurate analysis, a parallel process is recorded in the log file based on execution time, and algorithms for finding and eliminating information distortion are designed and implemented. With these refined data, we rediscover the workflow process model following the relationship between the activities. This series of experiments are conducted using the Large Bank Transaction Process Model provided by 4TU and visualizes the experiment process and results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.8
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• pp.1325-1335
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• 2002

Workflow systems, automated business processing, have been generalized. In this paper, we propose a method to mine workflow specification on workflow logs. The method detects workflow specification considering cycle, AND and OR control flow between tasks. Also, we provide dynamic mining method to detect workflow specification in which log is generated.

• Journal of Internet Computing and Services
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• v.20 no.3
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• pp.77-84
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• 2019

Workflow management system is a system that manages the workflow model which defines the process of work in reality. We can define the workflow process by sequencing jobs which is performed by the performers. Using the workflow management system, we can also analyze the flow of the process and revise it more efficiently. Many researches are focused on how to make the workflow process model more efficiently and manage it more easily. Recently, many researches use the workflow log files which are the execution history of the workflow process model performed by the workflow management system. Ourresearch group has many interests in making useful knowledge from the workflow event logs. In this paper we use XES log files because there are many data using this format. This papersuggests what are the cardinalities of the temporal workcases and how to get them from the workflow event logs. Cardinalities of the temporal workcases are the occurrence pattern of critical elements in the workflow process. We discover instance cardinalities, activity cardinalities and organizational resource cardinalities from several XES-based workflow event logs and visualize them. The instance cardinality defines the occurrence of the workflow process instances, the activity cardinality defines the occurrence of the activities and the organizational cardinality defines the occurrence of the organizational resources. From them, we expect to get many useful knowledge such as a patterns of the control flow of the process, frequently executed events, frequently working performer and etc. In further, we even expect to predict the original process model by only using the workflow event logs.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.21-28
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• 2019

Process mining is state-of-the-art technology in the workflow field. Recently, process mining becomes more important because of the fact that it shows the status of the actual behavior of the workflow model. However, as the process mining get focused and developed, the material of the process mining - workflow event log - also grows fast. Thus, the process mining algorithms cannot operate with some data because it is too large. To solve this problem, there should be a lightweight process mining algorithm, or the event log must be divided and processed partly. In this paper, we suggest a set of operations that control and edit XES based event logs for process mining. They are designed based on relational algebra, which is used in database management systems. We designed three operations for tailoring XES event logs. Select operation is an operation that gets specific attributes and excludes others. Thus, the output file has the same structure and contents of the original file, but each element has only the attributes user selected. Union operation makes two input XES files into one XES file. Two input files must be from the same process. As a result, the contents of the two files are integrated into one file. The final operation is a slice. It divides anXES file into several files by the number of traces. We will show the design methods and details below.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.544-547
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• 2004

Current increasingly information systems log historic information in a systematic way. Not only workflow management systems, but also ERP, CRM, SCM, and B2B systems often provide a so-called 'event log'. Unfortunately, the information in these event logs is rarely used to analyze the underlying processes. Process mining aims at improving this problem by providing techniques and tools for discovering process, control, data, organizational, and social structures from event logs. This paper focuses on the mining social networks. This is possible because event logs typically record information about the users executing the activities recorded in the log. To do this we combine concepts from workflow management and social network analysis. This paper introduces the approach and presents a tool to mine social networks from event logs.

• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.87-96
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• 2019

In this paper, we propose a MapReduce-supported clustering technique for collecting and classifying distributed workflow enactment event logs as a preprocessing tool. Especially, we would call the distributed workflow enactment event logs as Workflow BIG-Logs, because they are satisfied with as well as well-fitted to the 5V properties of BIG-Data like Volume, Velocity, Variety, Veracity and Value. The clustering technique we develop in this paper is intentionally devised for the preprocessing phase of a specific workflow process mining and analysis algorithm based upon the workflow BIG-Logs. In other words, It uses the Map-Reduce framework as a Workflow BIG-Logs processing platform, it supports the IEEE XES standard data format, and it is eventually dedicated for the preprocessing phase of the ${\rho}$-Algorithm that is a typical workflow process mining algorithm based on the structured information control nets. More precisely, The Workflow BIG-Logs can be classified into two types: of activity-based clustering patterns and performer-based clustering patterns, and we try to implement an activity-based clustering pattern algorithm based upon the Map-Reduce framework. Finally, we try to verify the proposed clustering technique by carrying out an experimental study on the workflow enactment event log dataset released by the BPI Challenges.

• Journal of Internet Computing and Services
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• v.20 no.2
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• pp.101-108
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• 2019

Workflow management systems (WfMSs) automate and manage workflows, which are implementations of organizational processes operated in process-centric organizations. In this paper, wepropose an algorithm to discover temporal work transference networks from workflow execution logs. The temporal work transference network is a special type of enterprise social networks that consists of workflow performers, and relationships among them that are formed by work transferences between performers who are responsible in performing precedent and succeeding activities in a workflow process. In terms of analysis, the temporal work transference network is an analytical property that has significant value to be analyzed to discover organizational knowledge for human resource management and related decision-making steps for process-centric organizations. Also, the beginning point of implementinga human-centered workflow intelligence framework dealing with work transference networks is to develop an algorithm for discovering temporal work transference cases on workflow execution logs. To this end, we first formalize a concept of temporal work transference network, and next, we present a discovery algorithm which is for the construction of temporal work transference network from workflow execution logs. Then, as a verification of the proposed algorithm, we apply the algorithm to an XES-formatted log dataset that was released by the process mining research group and finally summarize the discovery result.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.83-89
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• 2018

In workflow process intelligence and systems, workflow process mining and analysis issues are becoming increasingly important. In order to improve the quality of workflow process intelligence, it is essential for an efficient and effective data center storing workflow enactment event logs to be provisioned in carrying out the workflow process mining and analytics. In this paper, we propose a three-dimensional process-aware datacube for organizing workflow enterprise data centers to efficiently as well as effectively store the workflow process enactment event logs in the XES format. As a validation step, we carry out an experimental process mining to show how much perfectly the process-aware datacubes are suitable for discovering workflow process patterns and its analytical knowledge, like enacted proportions and enacted work transferences, from the workflow process enactment event histories. Finally, we confirmed that it is feasible to discover the fundamental control-flow patterns of workflow processes through the implemented workflow process mining system based on the process-aware data cube.

• Journal of Internet Computing and Services
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• v.7 no.1
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• pp.91-99
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• 2006

This paper proposes a control path analysis mechanism to be used in the workflow mining framework maximizing the workflow traceability and re discoverability by analyzing the total sequences of the control path perspective of a workflow model and by rediscovering their runtime enactment history from the workflow log information. The mechanism has two components One is to generate the total sequences of the control paths from a workflow mode by transforming it to a control path decision tree, and the other is to rediscover the runtime enactment history of each control path out of the total sequences from the corresponding workflow's execution logs. Eventually, these rediscovered knowledge and execution history of a workflow model make up a control path oriented intelligence of the workflow model. which ought to be an essential ingredient for maintaining and reengineering the qualify of the workflow model. Based upon the workflow intelligence, it is possible for the workflow model to be gradually refined and finally maximize its qualify by repeatedly redesigning and reengineering during its whole life long time period.