Kyung Hee University · Computer Science
Professor Jae-Yoon Jung's research lab specializes in intelligent systems and data-driven process analytics, focusing on business process management, edge computing for smart manufacturing, and advanced data mining techniques for industrial and urban applications. The lab develops innovative methodologies for process clustering, fault detection in smart factories using edge devices, and imbalanced data classification in quality control. It also explores integrated analysis of human mobility and urban spatial patterns using big data from transportation systems. The research emphasizes practical applications in smart manufacturing, process optimization, and urban informatics through the integration of AI, IoT, and process-aware information systems.
Figures are computed from collected data and may differ slightly.
Monitoring the status of the facilities and detecting any faults are considered an important technology in a smart factory. Although the faults of machine can be analyzed in real time using collected data, it requires a large amount of computing resources to handle the massive data. A cloud server can be used to analyze the collected data, but it is more efficient to adopt the edge computing concept that employs edge devices located close to the facilities. Edge devices can improve data processi
We describe a proposed methodology for business process choreography. We focus on two types of business processes (contract and executable) and provide an interface protocol to represent interoperability patterns between them. The approach is designed to let existing processes, usually managed by an enterprise's own internal workflow management system, collaborate.
Data-driven quality control techniques are being actively developed for implementation in smart factories. Quality prediction during manufacturing processes is a good example of how big data analytics can influence advanced manufacturing environments. In this paper, the problem of classifying manufacturing process conditions into normal and defective products according to defect types is dealt with. Such a quality analysis data set is generally unbalanced because the defective rate is quite low
Abstract. Business process is collection of standardized and structured tasks inducing value creation of a company. Nowadays, it is recognized as one of significant intangi-ble business assets to achieve competitive advantages. We introduce a novel approach to business process analysis, which has more and more significance as process-aware in-formation systems that are spreading widely over a lot of companies. In this paper, a methodology of business process clustering based on process similarit
Identifying zones and movement patterns of people is crucial to understanding adjacent regions and the relationship in urban areas. Most previous studies addressed zones or movement patterns separately without analysing simultaneously the two issues. In this article, we propose an integrated approach to discover directly both zones and movement patterns among the zones, referred to as movement patterns between zones (MZPs), from historical boarding behaviours of passengers in subway networks by
Open papers in the app to read, cite, and organize with AI.
