• 인터넷정보학회논문지
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• 제20권3호
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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.

• 인터넷정보학회논문지
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• 제20권2호
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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.

• 한국정보통신학회논문지
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• 제6권8호
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• pp.1325-1335
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• 2002

자동화된 업무 처리를 위한 워크플로우 시스템이 일반화되었다. 본 연구는 워크플로우 로그 로부터 워크플로우 명세를 탐사하는 방안을 제안한다. 제안한 기법은 과업간 순환, AND 그리고 OR 제어 흐름을 고려하여 워크플로우 명세를 탐사한다. 또한 로그 생성 시에 워크플로우 명세를 탐사할 수 있는 동적 탐사 기법을 제안한다.

• 인터넷정보학회논문지
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• 제20권1호
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• pp.87-96
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• 2019

본 논문에서는 분산 워크플로우 실행 이벤트 로그를 수집하고 분류하기 위한 사전 처리 도구로서 맵-리듀스기반 클러스터링 기법을 제안한다. 특히 우리는 볼륨, 속도, 다양성, 진실성 및 가치와 같은 BIG 데이터의 5V 속성에 만족하고 잘 충족되어 있기 때문에 분산 워크플로우 실행 이벤트 로그를 특별히 워크플로우 빅-로그(Workflow BIG-Logs)라고 정의한다. 이 논문에서 개발하는 클러스터링 기술은워크플로우 빅-로그를 기반으로 하는 특정 워크플로 프로세스 마이닝 및 분석 알고리즘의 사전 처리 단계에 적용하기 위한 목적으로 고안된 것이다. 즉, 맵리듀스(Map-Reduce) 프레임워크를 워크플로우 빅-로그 처리 플랫폼으로 사용하고, IEEE XES 표준 데이터 형식을 지원하며, 결국 본 연구에서 개발중에 있는 구조적 정보제어넷기반 워크플로우 프로세스 마이닝 알고리즘인 ${\rho}$-알고리즘의 사전 처리 단계 전용으로 사용되도록 구현된 것이다. 보다 자세하게 말하자면, 워크플로우 빅-로그의 클러스터링 패턴은 단위업무액티버티 기반 클러스터링 패턴과 단위업무 수행자 기반 클러스터링 패턴으로 분류되는데, 특별히 단위업무 액티버티 패턴의 하나인 시간적 워크케이스 패턴과 그의 발생 건수를 재발견하는 맵리듀스 기반 클러스터링 알고리즘을 설계하고 구현하고자 한다. 마지막으로, 우리는 BPI 챌린지에서 공개한 워크플로우 실행 이벤트 로그 데이터세트에 대해 일련의 실험을 수행함으로써 제안된 클러스터링 기법의 기술적 타당성을 검증한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권8호
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• pp.4108-4122
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• 2019

Workflow process mining is becoming a more and more valuable activity in workflow-supported enterprises, and through which it is possible to achieve the high levels of qualitative business goals in terms of improving the effectiveness and efficiency of the workflow-supported information systems, increasing their operational performances, reducing their completion times with minimizing redundancy times, and saving their managerial costs. One of the critical challenges in the workflow process mining activity is to devise a reasonable approach to discover and recognize the bottleneck points of workflow process models from their enactment event histories. We have intuitively realized the fact that the iterative process pattern of redo-activities ought to have the high possibility of becoming a bottleneck point of a workflow process model. Hence, we, in this paper, propose an algorithmic approach and its implementation to discover the redo-activities and their performers' involvements patterns from workflow process enactment event logs. Additionally, we carry out a series of experimental analyses by applying the implemented algorithm to four datasets of workflow process enactment event logs released from the BPI Challenges. Finally, those discovered redo-activities and their performers' involvements patterns are visualized in a graphical form of information control nets as well as a tabular form of the involvement percentages, respectively.

• 인터넷정보학회논문지
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• 제20권6호
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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.

• 한국컴퓨터산업학회논문지
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• 제6권3호
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• pp.437-446
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• 2005

워크플로우 마이닝은 워크플로우 엔진의 실행 정보를 포함하고 있는 워크플로우 로그 파일을 이용하여 워크플로우 모델 즉, 비즈니스 프로세스를 재설계하기 위한 방법을 제공하는 워크플로우 분야에서 새로운 연구 분야이다. 본 논문에서 제안된 워크케이스 마이닝을 위한 실행계열분석 알고리즘은 빌드타임에서 정의된 워크플로우 모델의 액티비티 실행순서와 런 타임에서 실제 실행된 모델의 실행순서와 비교하여 빌드 타임의 워크플로우 모델과 실행 모델과의 차이를 최소화하여 비즈니스 프로세스의 생산성을 향상 및 새로운 비즈니스 모델을 정의할 수 있는 기준을 제시한다. 이와 같은 작업은 빌드타임에 정의된 워크플로우 프로세스를 액티비티 의존넷을 이용하여 최소의 액티비티 집합으로 작성하고, 작성된 최소의 액티비티 집합과 실제 워크플로우 엔진에서 실행된 프로세스의 실행결과를 제안된 실행계열 분석 알고리즘을 이용하여 비교함으로써 수행할 수 있다.

• 인터넷정보학회논문지
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• 제20권3호
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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.

• 인터넷정보학회논문지
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• 제4권6호
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• pp.43-55
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• 2003

워크플로우 마이닝이란 워크플로우 관리시스템에서 생성되고 실행되어진 비즈니스 프로세스의 인스턴스의 실행경로를 분석하여 새로운 정보론 추출하는 과정을 의미한다. 본 논문에서는 빌드타임(Build Time)에 정의된 모델의 실행경로와 런타임(Run Time)에서 실행된 워크케이스의 실행경로를 비교/분석하여 새로운 워크플로우 모델의 제시 또는 작성된 비즈니스 프로세스의 개선을 위한 작업으로서 액티비티(activi쇼) 의존성을 이용한 워크케이스 마이닝에 관하여 기술하겠다. 제안된 워크케이스 마이닝을 위하여 각 액티비티의 의존성을 분석하여 중요경로(Essential Path)를 결정하는 액티비티 의존 넷 알고리즘(Activity-Dependent Net Algorithm)과 워크케이스 실행 정보를 이용하여 중요 경로와 일치여부를 결정하는 실행계열 분석 알고리즘(E-Walk Series Analysis Algorithm)을 제안한다.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2004년도 춘계공동학술대회 논문집
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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.