Formal approach for discovering work transference networks from workflow logs

Ahn, Hyun; Kim, Kyoung-Sook

doi:10.1016/j.ins.2019.11.036

Cited by 10 publications

(4 citation statements)

References 15 publications

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“…The second is to create a new valueC t to add to the current state using equation (3). The two values are then combined to calculate the output memory of the current block C t using equation (4). Finally, the output state is calculated using equations ( 5) and (6).…”

Section: Basic Concepts a Recurrent Neural Network And Long Short-term Memory Networkmentioning

confidence: 99%

“…Process-Aware Enterprise Social Network Concept: Process-aware ESN is not a social network created from Internet social media such as Facebook or Twitter. It is a work transference network [4] or handover of work network [3] discovered from a process-aware system event log, which focuses on the relationships between performer and performer and can be used for social analysis (e.g., degree centrality, closeness centrality, etc.). In other words, it describes the relationships formed between two or more people who perform activities related to business processes within an organization.…”

Section: B Prediction Of Variants Of a Process-aware Enterprise Social Networkmentioning

confidence: 99%

“…From the discovered information using process mining techniques, we also obtain knowledge regarding the handover of work [3] or the work transference network [4], which form a process-aware enterprise social network (ESN). Through this network, we can analyze metrics such as degree centrality, betweenness centrality, and closeness centrality to understand how different performers are involved in each activity.…”

Section: Introductionmentioning

confidence: 99%

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Process-Aware Enterprise Social Network Prediction and Experiment Using LSTM Neural Network Models

2021

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Process mining that exploits system event logs provides significant information regarding operating events in an organization. By discovering process models and analyzing social network metrics created throughout the operation of the information system, we can better understand the roles of performers and characteristics of activities, and more easily predict what will occur in the next operation of a system. By using accurate and valuable predicted information, we can create effective environments, provide suitable materials to perform activities better, and facilitate more efficient operations. In this study, we apply the long short-term memory, a variant of the recurrent neural network, to predict the enterprise social networks that are formed through information regarding a business system's operation. More precisely, we apply the multivariate multi-step long short-term memory model to predict not only the next activity and next performer, but also all the variants of a process-aware enterprise social network based on the next performer predictions using a probability threshold. Furthermore, we conduct an experimental evaluation on the real-life event logs and compare our results with some related researches. The results indicate that our approach creates a useful model to predict an enterprise social network and provides metrics to improve the operation of an information system based on the predicted information.INDEX TERMS Process mining, long short-term memory neural network, process-aware enterprise social network, next event prediction.

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Section: Basic Concepts a Recurrent Neural Network And Long Short-term Memory Networkmentioning

confidence: 99%

Section: B Prediction Of Variants Of a Process-aware Enterprise Social Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Process-Aware Enterprise Social Network Prediction and Experiment Using LSTM Neural Network Models

2021

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“…To solve time inconsistency and consensus bias, Viriyasitavat and Hoonsopon [19] suggested an architecture of business processes in blockchain era, which allows business partner to select nodes in performing consensus and inherits the merits of blockchain, namely, persistency, validity, auditability, and disintermediary. Ahn and Kim [20] devised a series of formal definitions and algorithms for discovering a work transference network from a workflow procedure, and from its enactment histories in event logs; the proposed algorithms can graphically and mathematically represent workflow procedures, promoting enterprise management and evaluation of human resources. Oukfif et al [21] presented a reliability-aware method based on discrete particle swarm optimization (RDPSO) for workflow scheduling in multiple and heterogeneous cloud datacenters; simulation results show that the RDPSO greatly outperforms the reliability aware heterogeneous earliest finish time (HEFT) method in makespan, transferred data and reliability.…”

Section: Literature Reviewmentioning

confidence: 99%

An Optimal Scheduling Path Algorithm for Enterprise Resource Allocation Based on Workflow

Guo¹

2020

JESA

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In many enterprises, the work efficiency is suppressed by the irrational allocation of enterprise resources. To solve the problem, this paper puts forward the optimal scheduling path algorithm for enterprise resource workflow (ERWOSA). To measure the task efficiency of each person after project issuance, the ERWOSA models enterprise resource workflow, and quantifies each node in the model. Then, novel attributes like mean execution accuracy, mean execution time, and node weight were calculated. Based on the calculation result, the optimal scheduling path was solved, which is highly accurate and efficient. In this way, the ERWOSA manages to bring more benefits to the enterprise.

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