Project title: Development of eXplainable AI Framework for Process Predictive Monitoring (2022. 06. 01 ~ 2025. 02. 28)

프로세스 예측 모니터링용 eXplainable AI 프레임워크 개발

Project title: AI Research for Process Mining Based on Streaming Data (2021. 01. 01 ~ 2022. 12. 31)

스트리밍 데이터 기반의 프로세스 마이닝을 위한 AI 연구

Project title : Development of a platform for process mining in port logistics (2019.05 ~ )

-프로세스 마이닝 용 항만공사 플랫폼 개발

Extended : Development of port logistics optimization service using big data-based container terminal congestion and import/export volume prediction system (2020.09 ~)

-빅데이터 기반 컨테이너 터미널 혼잡도·수출입 물동량 예측 시스템을 활용한 항만물류 최적화 서비스 개발

• Company : Ulsan Port Authority
• The objective of this project is to develop a platform for process mining in port logistics. While, in fact, process mining has been extensively applied in domains such as health care and public administration, applications of process mining in logistics and, particularly, port logistics, remain limited. This implies that the logistic industry is lagging behind on the potential savings and operational improvements normally accrued by the application of process mining techniques in real world settings. Therefore, a thorough analysis of the potential of process mining applied to the analysis of port logistics data is needed. This platform aims at identifying critical aspects concerning event log collection and analysis in (port) logistics, critical use cases of process mining in different reference business processes in (port) logistics, and demonstrating the effectiveness of different process mining techniques using real world port logistics data publicly available and, possibly, collected from Ulsan Port Authority (UPA). As far as process mining techniques are concerned, the focus will be mainly on predictive process monitoring techniques, an emerging branch of process mining that aims at adapting data mining techniques for predicting aspects of interests in business processes using event log data.

Project title : Development of a Hospital System Optimization Platform: Operating Room Scheduling and Outpatient Service Process Optimization (2018.10 ~ 2019.02)

-병원 시스템 최적화 플랫폼 개발: 수술실 스케줄러 및 최적의 외래 서비스 프로세스 개발을 중심으로

Extended : Development of a Outpatient Waiting Time Prediction Model and Improvement of Outpatient Service Processes (2019.10 ~ 2020.07)

-외래환자 대기시간 예측 모델 개발 및 외래 서비스 프로세스 개선

• Company : Pusan National University Hospital
• The objective of this project is to derive optimal outpatient service process and identify additional hospital system optimization issues. The concrete list is as follows : Identify outsourcing service process through process mining; Diagnosis and simulation of outpatient service processes; Derivation and verification of reduction of waiting time for medical treatment through simulation analysis; Improving the quality of event logs based on machine learning using data currently held by Pusan ​​National University Hospital and additional collected data; Development of Real-Time Waiting Time Prediction Model; Try to estimate and minimize the number of days spent in a particular patient group; Develop predictive machine learning model to predict remaining time using patient logs.

Project title : Blockchain-based System Engineering (2018.01 ~ 2019.12)

-블록체인 기반 시스템 구축

• UNIST Research Find (Excellent research idea discovery project)
• Although blockcain is recognised as a potentially disruptive technology for all industries, deployment of blockchain-based solutions outside finance and, specifically, cryptocurrencies and related applications, is still largely experimental. This is particularly true when blockchain is seen as a tool for engineering information exchange among a set of collaborating parties, or a “business network”. Many emerging applications are showing that there are a variety of design choices available while (re-)engineering a system using blockchain, such as the size and content of blocks, consensus rules to create new blocks, or the nature and shape of the smart contracts to be embedded in blocks. However, a scientific approach to exhaustively identify these design choices and guide designers during implementations is currently lacking.
  This project aims at filling this gap, by devising a methodology for reengineering systems using the blockchain technology. This methodology will comprise an analysis of the blockchain-based system design space, ie., the set of design choices available to designers, and a set of guidelines to support designers of a system during implementation.

Project title : Blockchain Platform with Business Models towards Cross-domain Interoperability (2018.06 ~ 2020.02)

-크로스 도메인 호환성을 위한 블록체인 플랫폼 및 비즈니스 개발

• IBRC POSTECH (대학ICT연구센터육성지원사업)
• The Intelligent Enterprise Lab at UNIST, chaired by Prof Marco Comuzzi, conducts research in the areas of enterprise systems, business process management and blockchain-based systems design. The Lab has received a seed funding from UNIST for the year 2018 to develop a methodology for blockchain-based system design using case studies of real blockchain-based application development in the UNIST campus. The expertise developed within this seed funding research will constitute fundamental background knowledge for the research to be conducted in this project. In particular, the developed methodology will be extended to the case of interoperability of multiple blockchains. The Lab research in business process management also contributes to build important background knowledge for this project. Prof Comuzzi has a strong track record at UNIST and his previous research in the area of cross-organisational business process management modelling and enactment. This project represents an opportunity to transfer this knowledge into the context of blcockhain-based system interoperability design and enactment.

Project title : Development of Data Analytics Methods to Identify the Sources of Oder (2017.10 ~ 2018.03)

-악취 원인 규명을 위한 데이터 분석 기법 연구

• Company : TAE SUNG ENVIRONMENT INSTITUTE
• The project objectives of this project are to be compressed into the following three. (1) Establish a big data integration platform related to odor. (2) Development of artificial intelligence for odor analysis. (3) Design of Ulsan Metropolitan City Odor Monitoring Service System based on Artificial Intelligence. Among them, the pure research goal excluding the platform and the design part of the system is related to the second business objective ‘deodorization artificial intelligence development’. Specifically, it is necessary to develop a high-performance odor recognition artificial intelligence model by identifying and solving various problems arising in the data collection-processing-analysis stage through the repeated experiment process of applying and testing various algorithms on odor data. It is a goal.

Project title : Development of techniques for improving quality of event logs for process mining (2017.09 ~ 2019.08)

-프로세스 마이닝 용 이벤트로그 품질 제고 기술 개발

• National Research Fund
• The objective of this project is to design and implement new techniques to improve the quality of process event logs and to study how they impact the quality of process mining outcomes.

Year 1: To revise the literature and develop preliminary models of event log cleaning and imputation techniques.

Year 2: To develop event log cleaning and imputation techniques and assess their impact on the quality of process mining outcomes.

Year 3: To evaluate the developed techniques in real world cases and refine them using the feedback collected from practitioners.

Project title: Techniques and tools for controlled change management in ERP post-implementation (2016. 04. 01 ~ 2017. 09. 30)

• The purpose of this research project is to develop techniques for the controlled change management of modifications of ERP systems in the post-implementation phase. The content is as follows : Develop a genetic conceptual model of ERP systems to determine the dependencies among the different components consituting the system, introduce a taxonomy of possible post-implementation modifications of ERP systems, Define a methodology to assess the impact of different types of change, by considering in particular the ripple effects implied by specifice dependencies, Define metrics to estimate the depth of impact ERP post-implementation change, possibly based on the strategy selected to implementation the identified change. Implement a software tool, i. e., a decision support system, embodying the identified models, methods and metrics to support business analysts in the controlled management of ERP post-implementation change.