- Acknowledgement . . . . 4 matches
==== Decision Mining ====
==== Reality Mining ====
==== uMining ====
==== uBPM & uMining ====
- BPM . . . . 1 match
==== Process mining ====
* [http://bpm.khu.ac.kr/wiki/wiki.php/ProM ProM] - Process Mining Tool
- BPM Lab. . . . . 13 matches
BPM Laboratory conducts research on developing and implementing business innovation techniques for operational excellence on the basis of business process modeling and analysis techniques in a variety of business area such as e-Biz (electronic business), i-Biz (internet business), and u-Biz (ubiquitous business). We envision the next generation of business process management which can embrace the future technologies, '''Big Data''', '''Social Network Analysis''', and the '''Internet of Things'''.
[http://bpm.khu.ac.kr/wiki/wiki.php/ProM Process Mining]
* '''Process Mining'''
Process mining techniques are able to extract knowledge from event logs commonly available in today's information systems. These techniques provide new means to discover, monitor, and improve processes in a variety of application domains (IEEE TFoPM, 2011). [http://www.win.tue.nl/ieeetfpm]
* [http://www.win.tue.nl/ieeetfpm/doku.php?id=shared:jung_jae-yoon '''IEEE Task Force on Process Mining''']
* [http://www.win.tue.nl/ieeetfpm/doku.php?id=shared:process_mining_manifesto '''Process Mining Manifesto (English, Korean, and other languages)''' ]
* [http://www.processmining.org/book/start '''Process Mining Book''']
==== Product Design Support System Using Cognitive and Emotional Databases ====
* '''Overview''' : Recently, product design is becoming more important to increase the competitiveness in their market. Although cognition and emotion are one of critical factors in product design, small and medium-sized enterprises have difficulty in reflecting and assessing the aspects of cognition and emotion by themselves. To deal with this problem, this research project is developing the product design support systems by incorporating the congnitive and emotional database. In this project, our team plays a role of developing the semantic web-based database to store the congnitive and emotional data.
* '''Keywords''' : '''''product design''''', user experience (UX), semantic web, database design
==== Smart Factory Applications for Big Data in Manufacturing ====
* '''Overview''' : Smart factory means the advanced manufacturing environment in production and operations by embracing information technologies such as the Internet of Things (IoT), the cloud, big data analytics and cyber-physical systems. In the smart factory of the future, the convergence of information technology and factory automation pursues the advanced manufacturing in most activities such as demand forecasting, production planning and control, scheduling, inventory and logistics management. We are developing smart factory systems and applications based on big manufacturing data, collaborating with KITECH and ECMiner. For example, several models for production quality analysis, such as classification models, prediction models, factor analysis, and pattern discovery, are being developed and implemented in form of a manufacturing data mining library.
* '''Keywords''' : '''''smart factory''''', Industry 4.0, manufacturing data analytics, data mining library, IoT
==== Reality Mining for Regional Big Data ====
* '''Full title''' : Development of Reality Mining Models and Systems from Regional Big Data for Comprehension of Individual and Social Behaviors
* '''Overview''' : In this research, ''we aim at developing reality mining models to understand individual's life and social mobility behaviors by analyzing regional big data''. Traditional approaches of social science intended to interpret macro social behaviors through statistical analysis, while the new approach of data science enables computational social science by analyzing raw big data related to human behaviors and understanding macro social behaviors. In this research, for the purpose of investigating location-based data, we will perform interdisciplinary studies by combining a variety of accessible open data to comprehend social phenomena.
* '''Keywords''' : '''''big data analytics''''', computational social science, spatio-temporal data analysis, geographical information science
<Raw public transp. data in Seoul> <Discovered movement patterns in Seoul> <Convenience map in Seoul>
attachment:BPM%20Lab./kbigdata2017.jpg
2015.6.25 AP-BPM 2015 Process Mining Competition에서 '''Grand Award(Josue,Berny) 및 Second-Prize Award(최서현) 수상'''
- BigData . . . . 29 matches
* The 4V’s of Big Data [http://newcontext.com/the-4vs-of-big-data/ link]
* NYT (2012) - The Age of Big Data [http://www.nytimes.com/2012/02/12/sunday-review/big-datas-impact-in-the-world.html?pagewanted=print link]
* McKinsey (2011) - Big data : The next frontier for innovation, competition, and productivity [http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation link]
* EMC - Big Data: Big Opportunities to Create Business Value [http://www.emc.com/microsites/cio/articles/big-data-big-opportunities/LCIA-BigData-Opportunities-Value.pdf pdf]
* IBM (2012) - Analytics: The real-world use of big data [http://www-935.ibm.com/services/us/gbs/thoughtleadership/ibv-big-data-at-work.html link]
* The Deciding Factor: Big Data & Decision Making [http://www.capgemini.com/resource-file-access/resource/pdf/The_Deciding_Factor__Big_Data___Decision_Making.pdf pdf]
* Business Data Lake [https://www.youtube.com/watch?feature=player_embedded&v=z7LS7Tm31Co Youtube]
* (See also "Worldwide Big Data Technology and (IT) Services 2012-2016 Forecast")
* Big Data, Analytics and the Path from Insight to Value [http://sloanreview.mit.edu/article/big-data-analytics-and-the-path-from-insights-to-value/ MITSloan2011]
* Business Intelligence and Analytics - From Big Data to Big Impact [http://www.misq.org/skin/frontend/default/misq/pdf/V36I4/SI_ChenIntroduction.pdf MISQ2012]
* Data Science, Predictive Analytics, and Big Data [http://onlinelibrary.wiley.com/doi/10.1111/jbl.12010/abstract JBL2013]
* Davenport - Big Data in Big Companies [http://www.sas.com/resources/asset/Big-Data-in-Big-Companies.pdf SAS2013] [http://www.sas.com/content/dam/SAS/en_us/doc/research1/big-data-big-companies-executive-summary-106462.pdf summary]
* Davenport - At the Big Data Crossroads: turning towards a smarter travel experience [http://www.amadeus.com/web/binaries/blobs/830/891/Amadeus_Big_Data.pdf pdf]
* Big data: Challenges and opportunities, Infosys Labs Briefings. [http://www.infosys.com/infosys-labs/publications/infosyslabs-briefings/Pages/bigdata-challenges-opportunities.aspx 11 (1), 2013]
* Worldwide Big Data Technology and (IT) Services 2012-2016 Forecast [https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&ved=0CEcQFjAB&url=http%3A%2F%2Fdownload.microsoft.com%2Fdownload%2F7%2FB%2F8%2F7B8AC938-2928-4B65-B1B3-0B523DDFCDC7%2FIDC%2520Report-Worldwide%2520Big%2520Data%2520Technology%2520and%2520Services.pdf&ei=CrcAU8_-L8LcyQHV44CQDA&usg=AFQjCNHgiCBhHSmKlBmKAuep27fGT867pQ&sig2=SVsmgJMfZ82OgRL8pTJyfA&bvm=bv.61535280,d.aWc pdf]
* 빅데이터국가포럼 빅데이터 사례 [http://www.bigdataforum.or.kr/?bid=report&category=2 link]
* IBM Big Data Hub [http://www.ibmbigdatahub.com/ link]
* Oracle Data Warehousing and Big Data [http://www.oracle.com/technetwork/database/bi-datawarehousing/index.html link]
* Use Cases [http://practicalanalytics.wordpress.com/2011/12/12/big-data-analytics-use-cases/ site]
* IBM Framework - Better business outcomes with IBM Big Data & Analytics [http://www.ibm.com/connect/ibm/attachments/G119542M94960B53/SW_Business_Analytics_YTW03101USEN.pdf pdf]
- Courses . . . . 15 matches
Database
Database Theory & Practices
Database Theory & Practices
[https://jjyjung.github.io/pmining/ Process Mining (graduate)]
[https://classroom.google.com/w/NTMwMzczMTM4NzZa/t/all Database Theory & Practices]
[https://classroom.google.com/w/NjI1NjQ1MTc5Mjda/t/all Process Mining]
[http://web.khu.ac.kr/~jung/bbs/zboard.php?id=db19 Database Theory & Practices]
[http://web.khu.ac.kr/~jung/bbs/zboard.php?id=db18 Database Theory & Practices] * 2
Database Theory & Practices * 2
Process Mining (graduate)
Database Theory & Practices * 2
[http://web.khu.ac.kr/~jung/bbs/zboard.php?id=pmining15 Process Mining (graduate)]
[http://web.khu.ac.kr/~jung/bbs/zboard.php?id=pmining Process Mining (graduate)]
[http://web.khu.ac.kr/~jung/bbs/zboard.php?id=mining10 Data Mining]
- Data Mining . . . . 5 matches
=== 2009 Data Mining Study ===
1. '''Data mining 경진대회'''
- http://sasmining.co.kr
=== 2009 Data Mining Study(2) ===
- History . . . . 3 matches
attachment:BPM%20Lab./kbigdata2017.jpg
2015.6.25 AP-BPM 2015 Process Mining Competition에서 '''Grand Award(Josue,Berny) 및 Second-Prize Award(최서현) 수상'''
2012.6.28-8.20. 김애경 - 네덜란드 Eindhoven University of Technology 초청연구 (CurriM: Curriculum Mining)
2012.2.15. 첫 한국인 석사학위(김애경) 취득 [Thesis: A Process Mining Technique for Performer Recommendation Using Decision Tree]
- IAI Lab. . . . . 12 matches
* ''' Operational Intelligence''' (w/ Process Mining and Decision Science)
* '''Business Intelligence''' (w/ Data Warehousing and Data Mining)
* '''Process Mining'''
Process mining techniques enable to extract knowledge from event logs, which can be collected easily in today's IT systems. The techniques provide new means to discover, monitor, and improve processes in a variety of application domains (IEEE TFoPM, 2011). [http://www.win.tue.nl/ieeetfpm]
* [http://www.win.tue.nl/ieeetfpm/doku.php?id=shared:jung_jae-yoon '''IEEE Task Force on Process Mining''']
* [http://www.win.tue.nl/ieeetfpm/doku.php?id=shared:process_mining_manifesto '''Process Mining Manifesto (English, Korean, and other languages)''' ]
* '''Process Mining Book''' ([https://www.kyobobook.co.kr/product/detailViewEng.laf?ejkGb=BNT&mallGb=ENG&barcode=9783662498507 '''2nd Edition'''] [https://www.kyobobook.co.kr/product/detailViewKor.laf?ejkGb=KOR&mallGb=KOR&barcode=9788936318727 '''Korean Translation'''])
* Development of quality inspection technology based on on-device generative AI using small-scale data, 2024.7.1~2027.2.28, KEIT/MOTIE.
* (BK21 Four)Sustainable Big Data New Business Leading Human Resources Education Research Group, 2020.9~2027.8, NRF/MOE.
attachment:BPM%20Lab./kbigdata2017.jpg
2015.6.25 AP-BPM 2015 Process Mining Competition에서 '''Grand Award(Josue,Berny) 및 Second-Prize Award(최서현) 수상'''
2012.6.28-8.20. 김애경 - 네덜란드 Eindhoven University of Technology 초청연구 (CurriM: Curriculum Mining)
2012.2.15. 첫 한국인 석사학위(김애경) 취득 [Thesis: A Process Mining Technique for Performer Recommendation Using Decision Tree]
- JYJung . . . . 16 matches
||<|4>attachment:Members/jung9.jpg||<width="80%">'''Affiliation''': [[br]]Professor, Industrial & Management Systems Engineering (IE), KHU, 2007.9~present [[br]]Adjunct Professor, Artificial Intelligence (AI), KHU, 2022.9~present [[br]]Adjunct Professor, Big Data Analytics (BDA), KHU, 2020.9~present [[br]]Adjunct Professor, Software Convergence (SWCon), KHU, 2017.3~present [[br]]Dean of Information Service and Strategy, KHU, 2021.9~present [[br]]Department Head, BDA, KHU, 2021.9~2022.8, 2024.3~present [[br]] Department Head, IE, KHU, 2015.2~2018.1 [[br]]'''Address''': [[br]]1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104, Republic of Korea [[br]]'''Tel''': +82 31 201 2537 / '''E-mail''': jyjung_at_khu.ac.kr[[br]][http://iai.khu.ac.kr/wiki/wiki.php/JYJung] [[br]][http://web.khu.ac.kr/~jung] [[br]][http://web.khu.ac.kr/~jung/paper/Jung_CV_201310.pdf '''Curriculum vitae''']||
Dr. Jae-Yoon Jung (정재윤, 鄭在倫) is a professor in the department of industrial and management systems engineering at Kyung Hee University (KHU), Korea, and also an adjunct professor of the department of artificial intelligence, the department of the department of big data analytics (BDA), and the department of software convergence (SWCon), KHU. He is leading Industrial AI Lab at KHU.
He received the B.S., M.S., and Ph.D. degrees in Industrial Engineering at Seoul National University (SNU), in 1999, 2001, and 2005, respectively. In SNU, he was supervised by prof. Suk-Ho Kang and Yeongho Kim in Intelligent Manufacturing Systems Lab. After that, he visited the Process Mining Group at Eindhoven University of Technology (TU/e) in the Netherland, supervised by prof. Wil van der Aalst. Before joining in KHU, he worked for u-Computing Innovation Center (uCIC), directed by Prof. Jinwoo Park, and he also studied in the Information Management Lab. at SNU, supervised by prof. Jonghun Park. His current research interests include Industrial AI, Big Data Analytics, Process Mining, and Smart Factory.
* 2019.9 - present, Adjunct Professor, Department of Big Data Analytics (BDA), KHU
* 2005.12 - 2006.11, Visiting Scholar, Process Mining Group, Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, The Netherlands
* Member of [http://www.win.tue.nl/ieeetfpm/doku.php?id=shared:jung_jae-yoon IEEE Task Force on Process Mining]
* Board member of KBDS (Korea Bigdata Society), 2013~present
* 2016.1 - 2019.12, Editorial Board for [http://www.kbigdata.kr/intro09.html Korean Journal of Big Data]
* Best Paper Award, The 6th International Conference on Big Data Applications and Services (BigDAS 2018).
* Best Paper Award, 2016 Korea Big Data Society Conference.
* '''Data Mining'''
1. '''Hoonseok Park''' and Jae-Yoon Jung*, "'''SAX-ARM: Deviated Event Pattern Discovery''' from Multivariate Time-Series Using Symbolic Aggregate Approximation and Association Rule Mining", Expert Systems with Applications, Vol. 141, Mar 2020, 112950. (SCIE, IF=5.452, 1.8%, OR/MS)
1. '''Kwanho Kim''', Kyuhyup Oh, Yeong Kyu Lee, SungHo Kim, and Jae-Yoon Jung*, "An Analysis on '''Movement Patterns between Zones''' Using Smart Card Data in Subway Networks", International Journal of Geographical Information Science, Vol. 28, No. 9, Sep 2014, pp. 1781-1801. [http://web.khu.ac.kr/~jung/paper/Jung_IJ_2014_IJGIS.pdf pdf]
* '''Process Mining'''
1. '''Josue Obregon''', Minseok Song, and Jae-Yoon Jung*, "'''InfoFlow''': Mining Information Flow Based on User Community in Social Networking Services", IEEE Access, Vo. 7, Apr 2019, pp. 48024-48036. (SCIE, IF=4.098, 14%, CS/IS) [https://ieeexplore.ieee.org/document/8681519?arnumber=8681519 link] [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8681519 pdf]
- Members . . . . 22 matches
||<|4>attachment:jung9.jpg?width=120||<width="80%">[http://bpm.khu.ac.kr/wiki/wiki.php/JYJung '''Jae-Yoon JUNG (정재윤, 鄭在倫)'''] [[br]]Professor, Dept. of Industrial & Management Systems Engineering (IE), KHU, 2018.9~present [[br]]Dean of Information Service and Strategy, KHU, 2021.9~present [[br]]Adjunct Professor, Dept. of Artificial Intelligence (AI), KHU, 2022.9~present [[br]]Adjunct Professor, Dept. of Big Data Analytics (BDA), KHU, 2020.9~present [[br]]Adjunct Professor, Dept. of Software Convergence (SWCon), KHU, 2017.3~present [[br]]Department Head, BDA, KHU, 2021.9~2022.8, 2024.3~ [[br]]Graduate Program Director, IE, KHU, 2018.2~2020.1 [[br]]Department Head, IE, KHU, 2015.2~2018.1 [[br]]Director, Graduate Program for Smart Manufacturing Intelligence (SMI), KHU, 2017.3~2020.2[[br]]Associate/Assistant Professor, IE, KHU, 2007.9~2018.8 [[br]]Ph.D./M.S./B.S., Industrial Engineering, Seoul National University, 2005, 2001, 1999 [[br]] (Office) 430-Ho, Eng. Bld., (Tel) +82-31-201-2537, (E-mail) jyjung_at_khu.ac.kr [[br]](Lab) http://iai.khu.ac.kr/wiki/wiki.php/JYJung [[br]](Personal) http://web.khu.ac.kr/~jung [[br]] [https://drive.google.com/file/d/1MgIE0Cbr949q1uHZRneCGj181f_i7KKq/ Curriculum vitae] [https://scholar.google.com/citations?user=iuRrCpIAAAAJ&hl=en Google scholar] [https://www.scopus.com/authid/detail.uri?authorId=55575978400 Scopus profile] ||
||attachment:shchoi2.jpg?width=120||<width="80%">[https://seohyunc.github.io/tabs/about '''Seohyun CHOI (최서현)'''] [[br]]Ph.D. Candidate, IAI Lab., 2021.3~ [[br]]M.S., BPMLab., 2014.3~2016.2 [[br]]Research Assistant, BPMLab., 2012.1~2013.2[[br]]Industrial Engineering, Kyung Hee University[[br]]Netmarble Corp. (넷마블)[[br]]E-mail - choish91_at_khu.ac.kr[[br]]'''Master Thesis''': A Process Mining Technique based on Bayesian Networks for Inference of Probabilistic Event Patterns (확률적 이벤트 패턴 추론을 위한 베이지안 네트워크 기반 프로세스 마이닝 기법)||
||attachment:leo.jpg?width=120||<width=100%>'''[https:// Beomseok SEO (서범석)]''' (part-time) [[br]][[br]]M.S. student, Big Data Analytics, 2022.9~ [[br]]B.S., Applied Mathmatics, Kyung Hee University[[br]]E-mail - beomseok_at_khu.ac.kr||
||attachment:abc.jpg?width=120||<width=100%>'''[https://kimignis.github.io/ Minsik KIM (김민식)][[br]]''' [[br]]M.S. student, Big Data Analytics, 2023.3~ [[br]]Research Assistant, IAI Lab., 2022.7~2023.02 [[br]]Industrial Engineering, Kyung Hee University[[br]]E-mail - 2017100869_at_khu.ac.kr||
||attachment:mjshin.jpg?width=120||<width=100%>'''[https:// Minjong SHIN (신민종)][[br]]''' [[br]]M.S. student, Big Data Analytics, 2024.3~ [[br]]Mechanical Engineering, Inha University[[br]]E-mail - shinmj_at_khu.ac.kr||
||attachment:sjeong.jpg?width=120||<width=100%>'''[https://aauhsoj.github.io/ Sol JEONG (정솔)][[br]]''' [[br]]M.S. student, Big Data Analytics, 2024.3~ [[br]]Industrial and Management Systems Engineering, Kyung Hee University[[br]]E-mail - bohomi1995j_at_khu.ac.kr||
||attachment:cyc.jpg?width=120||<width=100%>'''[https:// Yuchan CHOI(최유찬)][[br]]''' [[br]]M.S. student, Big Data Analytics, 2024.9~ [[br]]Biotechnology, Korea University[[br]]E-mail - oksusu_at_khu.ac.kr||
||attachment:jang.jpg?width=120||<width="80%">'''Donghyuk JANG (장동혁)''' (MS, 2024.2)[[br]]OCI, 2024.9~ [[br]]M.S., Big Data Analytics, 2022.3~2024.2 [[br]]B.S., Industrial Engineering, Hankuk University of Foreign Studies[[br]]E-mail - hyuk5930_at_khu.ac.kr [[br]]'''Master Thesis''': Interpretable open-set recognition for fault diagnosis of rotating machines (회전기기 이상진단을 위한 설명가능한 오픈셋 인식)||
||attachment:CHO.jpg?width=120||<width="80%">'''Han-Ik CHO (조한익)''' (MS, 2024.2)[[br]]SK Magic (SK매직), 2024.1~[[br]]M.S., Big Data Analytics, 2022.3~2024.2 [[br]]B.S., Mathematics, Kangwon National University[[br]]E-mail - johanik_at_khu.ac.kr [[br]]'''Master Thesis''': Supervised Contrastive Learning for Effective Fault Classification (효과적 고장 분류를 위한 대조적 지도학습)||
||attachment:hspark3.jpg?width=120||<width="80%">'''[https://cacaotree.github.io/ Hoonseok PARK]''' (PhD, 2022.8)[[br]]https://cacaotree.github.io/ [[br]]Samsung Elec. 2023.1~ [[br]]Ph.D., IAILab., 2022.8[[br]]M.S., IAI Lab., Kyung Hee University, 2018.2[[br]]B.S., Industrial Engineering, Kyung Hee University, 2016.2 [[br]]E-mail -hoonseok_at_khu.ac.kr [[br]]'''Doctoral Thesis''': Explainable Artificial Intelligence for Spatiotemporal Data Analysis [[br]]'''Master Thesis''': Frequent Pattern Discovery and Analysis from Multivariate Time Series in Manufacturing (다변량 제조 시계열 데이터의 규칙 발견 및 패턴 분석 연구)||
||attachment:11.jpg?width=120||<width="80%">'''Aekyung KIM''' (PhD, 2018)[[br]]Senior Researcher, Samsung Electronics (삼성전자), 2019.3~ [[br]]Post-Doctor, IAILab., 2018.9~2019.1 / Ph.D., IAILab., 2018.8 / M.S., IAI Lab., 2012.2 [[br]]Research assistant, IAI Lab., 2009.6~2010.8 [[br]]B.S., Industrial Engineering, Kyung Hee University, 2010.8 [[br]]Research Interests - Dynamic Process Patterns, Process Mining [[br]]E-mail -akim1007_at_khu.ac.kr[[br]]https://sites.google.com/site/akkim7979/ [[br]]'''Doctoral Thesis''': Ensemble-based Quality Classification and Deep Reinforcement Learning-based Production Scheduling (앙상블 기반 품질 분류 및 심층강화학습 기반 생산 스케줄링)[[br]]'''Master Thesis''': A Process Mining Technique for Performer Recommendation Using Decision Tree (의사결정나무 기반의 프로세스 마이닝을 이용한 업무 담당자 추천 기법)||
||attachment:berny_new.jpg?width=120||<width="80%">'''Berny Alfonso CARRERA GORDILLO (PhD, 2019)''' [[br]]Associate Instructor, University of Utah Asia Campus, 2024.8~ [[br]]Post-Doctor/Research Prof., Inchen National University, 2019.11~/2023.1~ [[br]]Ph.D., Industrial Engineering, Kyung Hee University, 2011.9~2019.2[[br]]M.S., Reengineering and Assurance Technologies, Galileo University, 2010 [[br]]M.S., Database Systems, Galileo University, 2010 [[br]]B.S., Computer Engineering, Galileo University, 2010[[br]]Research Interests - Business process optimization, Web Services, Social Networks [[br]]Hobby - Swimming, Read, Online Games [[br]]E-mail - bernyh2o_at_gmail.com[[br]]https://sites.google.com/site/bernyh2o/ [[br]] '''Doctoral Thesis''': Predictive and Descriptive Analytics for Smart Energy and Social Networks||
||attachment:sg7.jpg?width=120||<width="80%">'''Seul-Gi KIM''' (MS, 2019)[[br]] Researcher, SK주식회사 C&C, 2019.05~[[br]]Researcher, 포스코 ICT, 2018.11~2019.04 [[br]]M.S., IAI Lab., 2017.3~2019.2[[br]]Research Interests - BigData analytics, Machine Learning [[br]]Hobby - soccer [[br]]E-mail - super_man_at_sk.com [[br]] '''Master Thesis''': 단일 클래스 분류 기법을 이용한 진동 데이터 기반 시스템 고장 감지 분석: 마할라노비스 거리 기반 분류 기법을 중심으로||
||attachment:parul2.jpg?width=120||<width="80%">'''Parul SINGH''' (MS, 2018)[[br]]M.S., IAILab., 2014.9~2018.2[[br]]E-mail - osg_singhs_at_yahoo.com[[br]]'''Master Thesis''': Flow Orientation Analysis for Major Activity Regions Based on Smart Card Transit Data||
||attachment:oh.jpg||<width="80%">'''KyuHyup OH''' (PhD, 2017)[[br]]Senior Researcher, KTL (Korea Testing Laboratory, 한국산업기술시험원), 2018.3~[[br]]Post-doc, IAILab., 2017.9~2018.2[[br]]MS-PhD student, IAILab.(7.5), 2010.3~2017.8[[br]]Research Assistant(2007.12~2010.2), BPMLab.[[br]]Dept. of Industrial Engineering, Kyung Hee University[[br]]Doctoral Thesis: Techniques and Applications of Classification and Clustering of Big Time-Series Data[[br]]Research Interests - Process Mining, Semantic Web, Web Security, Log Analysis [[br]]Hobby - Linux, Mac [[br]]E-mail - k8383_at_khu.ac.kr[[br]]'''Doctoral Thesis''': Techniques and Applications of Classification and Clustering of Big Time-Series Data (시계열 빅데이터 분류 및 클러스터링 기법과 응용)||
||attachment:jslee.jpg||<width="80%">'''Jinsung LEE''' (PhD, 2017)[[br]]Senior Researcher, KETI (Korea Electronics Technology Institute, 전자부품연구원), 2019.2~[[br]]Senior Researcher, KOSF (Korea Smart Factory Foundation, 스마트공장추진단), 2015.1~2019.1[[br]]Ph.D, MIS Lab., 2010.3~2017.8[[br]]Master of Science(2007.9~2010.2), MIS Lab.[[br]]Industrial Engineering, Kyung Hee University[[br]]E-mail - jinsl127_at_khu.ac.kr[[br]]'''Doctoral Thesis''': Data-driven Operational Analytics based on Process Mining Techniques (데이터 기반의 프로세스 운영 분석) [[br]]'''Master Thesis''': Fuzzy-AHP 기법을 이용한 IT 아웃소싱 평가지표 우선순위 수립에 관한 연구||
||attachment:gj.jpg||<width="80%">'''Gwangjin HEO''' (MS, 2016) [[br]]Master of Science at BPMLab., 2014.9~2016.2[[br]]Research Assistant at BPMLab., 2013.6~2014.8[[br]]Industrial Engineering, Kyung Hee University[[br]] LS Cable & System Ltd. (LS전선)->LG Display [[br]]Research Interests - Business Analytics for Supply Chain, Database Management[[br]]E-mail - jin89_at_khu.ac.kr[[br]]https://sites.google.com/site/gjheo89/ [[br]]'''Thesis''': Analysis of Critical Path and Bottleneck in Business Process Execution||
##||attachment:kkm.jpg||<width="80%">'''Kangmin KIM''' (RA, 2013) [[br]]Research Assistant at BPMLab., 2013.2~12[[br]]Industrial Engineering, Kyung Hee University[[br]]크래프타 대표[[br]]Research Interests - Web Programming, Web Usage Mining, Social Network Analysis [[br]]E-mail - kis-yh_at_hanmail.net||
- ProM . . . . 4 matches
ProM is an extensible framework that supports a wide variety of process mining techniques in the form of plug-ins. It is platform independent as it is implemented in Java, and can be downloaded free of charge. The ProM framework receives input logs in the Mining XML (MXML) format.
There are mining (process discovery) plugins, such as:
*Plugins supporting control-flow mining techniques (such as the Alpha algorithm, Genetic mining, Multi-phase mining, ...)
*Plugins dealing with the data perspective (such as the Decision miner, ...)
*Plugins for mining less-structured, flexible processes (such as the Fuzzy Miner)
*Elaborate data visualization plugins (such as the Cloud Chamber Miner)
* DTMiner: A Tool for Decision Making Based on Historical Process Data
* A Framework of Process Mining for RFID Event Analysis
* A Framework of Process Mining for RFID Event Analysis
- Publications . . . . 50 matches
1. '''Donghyun Park, Junmo An''', and Jae-Yoon Jung*, "Performance Comparison of One-class Classification Tech-niques for Fault Detection Based on Vibration Data", ''Processes'', to be submitted. (SCIE, IF=2.8, 47%(80/170), CE) - 완성
1. '''Young-Suk Han''' and Jae-Yoon Jung, "Imitation learning of job dispatching data combining classification and ranking", ''CAIE'', submitted 241010. (SCIE, IF=6.7, 11.5%(20/169), CS/IA)
1. Sanghyun Choo, '''Hoonseok Park''', Jae-Yoon Jung, Kevin Flores, and Chang S. Nam*, "Improving Classification Performance of Motor Imagery BCI through EEG Data Augmentation with Conditional Generative Adversarial Networks", ''Neural Network'', vol. 180, Dec 2024, 106665. (SCIE, IF=6.0, 10%, Neuro) [https://doi.org/10.1016/j.neunet.2024.106665 doi]
1. Sun Hur, Jae-Yoon Jung, and '''Josue Obregon''', "Special Issue on Application of Big Data Analysis and Advanced Analytics in Sustainable Production Process", ''Processes'', 10(4), Mar 2022, 670. (SCIE, IF=3.352, 48.2%, Chem). [https://www.mdpi.com/2227-9717/10/4/670/htm link] [https://www.mdpi.com/2227-9717/10/4/670/pdf?version=1648619572 pdf]
1. Hee-Sun Choi, '''Junmo An''', Jin-Gyun Kim, Jae-Yoon Jung, Juhwan Choi, Grzegorz Orzechowski, Aki Mikkola, Jin Hwan Choi*, "Data-driven Simulation for General Purpose Multibody Dynamics Using Deep Neural Networks", ''Multibody System Dynamics'', Vol. 51, No. 4, Apr. 2021, pp. 419-454. (SCIE, IF=3.109, 39%, Mec)[https://link.springer.com/article/10.1007/s11044-020-09772-8 link] [http://web.khu.ac.kr/~jung/paper/IJ_202104_MSD.pdf pdf]
1. '''Berny Carrera''', '''Min Kyu Sim'''*, and Jae-Yoon Jung, "PVHybNet: A Hybrid Framework for Predicting Photovoltaic Power Generation Using Both Weather Forecast and Observation Data", ''IET Renewable Power Generation'', Vol. 14, No. 12, Sep. 2020, pp. 2192-2201. (SCIE, IF=3.894, 21%, EEE) [https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-rpg.2018.6174 link] [https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-rpg.2018.6174 pdf]
1. '''Min-Woo Baek''', '''Min Kyu Sim''' and Jae-Yoon Jung*, "Wind Power Generation Prediction Based on Weather Forecast Data Using Deep Neural Networks", ''ICIC Express Letters, Part B: Applications'', Vol. 11, No. 9, Sep. 2020, pp. 863-868. [http://www.icicelb.org/ellb/contents.html link] [http://www.icicelb.org/ellb/contents/2020/9/elb-11-09-07.pdf pdf]
1. '''Hoonseok Park''' and Jae-Yoon Jung*, "SAX-ARM: Deviated Event Pattern Discovery from Multivariate Time-Series Using Symbolic Aggregate Approximation and Association Rule Mining", ''Expert Systems with Applications'', Vol. 141, Mar 2020, 112950. (SCIE, IF=5.452, __1.8%(2/83)__, OR/MS)
1. '''Josue Obregon''', Minseok Song, and Jae-Yoon Jung*, "InfoFlow: Mining Information Flow Based on User Community in Social Networking Services", ''IEEE Access'', Vo. 7, Apr 2019, pp. 48024-48036. (SCIE, IF=4.098, 14%) [https://ieeexplore.ieee.org/document/8681519?arnumber=8681519 link] [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8681519 pdf]
1. '''Seul-Gi Kim''', Jae-Yoon Jung, and '''Min Kyu Sim'''*, "A Two-Step Approach to Solar Power Generation Prediction Based on Weather Data Using Machine Learning", ''Sustainability'', Vol. 11, No. 5, Mar 2019, 1501. (SSCI, IF=2.075, 42%, G&S) [https://www.mdpi.com/2071-1050/11/5/1501 link] [https://www.mdpi.com/2071-1050/11/5/1501/pdf pdf]
1. Jinkyun Park, Jae-Yoon Jung, Gyunyoung Heo, Yochan Kim, Jaewhan Kim, and Jaehyun Cho, "Application of Process Mining Techniques to Identifying Information Navigation Characteristics of Human Operators Working in a Digital Main Control Room", ''Reliability Engineering and System Safety'', Vol. 175, Jul 2018, pp. 38-50. (SCI, IF=3.153, 11%, OR/MS,IE) [https://www.sciencedirect.com/science/article/pii/S0951832017307147 link] [https://ac.els-cdn.com/S0951832017307147/1-s2.0-S0951832017307147-main.pdf?_tid=cc1e9788-c9de-438b-af65-1d187ab458da&acdnat=1530367917_a0d9fddbcc15533faa8a63f6178980c9 pdf]
1. '''Gwangjin Heo''', '''Jinsung Lee''', and Jae-Yoon Jung, "Analyzing Bottleneck Resource Pools of Operational Process Using Process Mining", ICIC Express Letters, Part B: Applications, Vol. 9, No. 5, May 2018. pp. 437-441. [http://www.ijicic.org/elb-9(5).htm link] [http://www.icicelb.org/ellb/contents.html pdf]
1. '''Parul Singh''', '''Kyuhyup Oh''', and Jae-Yoon Jung*, "Flow Orientation Analysis for Major Activity Regions based on Smart Card Transit Data", International Journal of Geo-Information, Vol. 6, No. 10, 318, Oct 2017. [http://www.mdpi.com/2220-9964/6/10/318 link] [http://www.mdpi.com/2220-9964/6/10/318/pdf pdf]
1. '''Berny Carrera, JinSung Lee'''*, and Jae-Yoon Jung, "Discovery of Gatekeepers on Information Diffusion Flows Using Process Mining", International Journal of Industrial Engineering- Theory, Applications and Practice, Vol 23, No 4, 2016, pp. 253-269. [http://journals.sfu.ca/ijietap/index.php/ijie/article/view/2809 link] [http://journals.sfu.ca/ijietap/index.php/ijie/article/download/2809/616 pdf]
1. Jinkyun Park, Jae-Yoon Jung, and Wondea Jung, "The Use of a Process Mining Technique to Characterize the Work Process of Main Control Room Crews: a Feasibility Study", Reliability Engineering & System Safety, Vol. 154, pp. 31-41, Oct 2016. [http://www.mdpi.com/1424-8220/18/7/2110 link] [http://www.mdpi.com/1424-8220/18/7/2110/pdf pdf]
1. '''Kwanho Kim''', '''Kyuhyup Oh''', Yeong Kyu Lee, SungHo Kim, and Jae-Yoon Jung*, "An Analysis on Movement Patterns between Zones Using Smart Card Data in Subway Networks", International Journal of Geographical Information Science, Vol. 28, No. 9, Sep 2014, pp. 1781-1801. [http://www.tandfonline.com/doi/abs/10.1080/13658816.2014.898768 link] [http://web.khu.ac.kr/~jung/paper/Jung_IJ_2014_IJGIS.pdf pdf]
1. Kwanho Kim, Beom-Suk Chung, Jae-Yoon Jung, and Jonghun Park*, "Revenue Maximizing Itemset Construction for Online Shopping Services", Industrial Management & Data Systems, Vol. 113, No. 1, Jan 2013, pp.96-116. [http://www.emeraldinsight.com/journals.htm?articleid=17076425 link]
1. Bokyoung Kang, Jae-Yoon Jung*, Nam Wook Cho, and Suk-Ho Kang, "Real-time Business Process Monitoring Using Formal Concept Analysis", Industrial Management & Data Systems, Vol. 111, No. 5, Jun 2011, pp. 225-230. [http://www.emeraldinsight.com/journals.htm?articleid=1926724 link] [http://web.khu.ac.kr/~jung/paper/Jung_IJ_201106_IMDS.pdf pdf]
1. '''YoungSuk Han''', Jae-Yoon Jung*, "Three-stage data-driven approach to fast and accurate job dispatching using learning-to-rank techniques", In Proc. of the 27th International Conference on Production Research (ICPR 2023), Cluj-Napoca, Romania, Jul. 23-28, 2023. (Ack: AIC)
1. '''Hoonseok Park''', Myoungjin Oh, Jae-Yoon Jung*, "Deep-Learning-Based Sensor Fusion for Machine Fault Detection Using Infrared Thermal Images and Multiple Sensor Data", In Proc. of the 13th International Conference on Internet (ICONI 2021), Dec. 12-14, 2021.
- RapidMiner . . . . 2 matches
==== DataMining ====
- Research . . . . 25 matches
##||uBPM ||RFID Process Mining on ProM Framework||__오규협__, 정재윤||IMDS||uMining||
##||uBPM||Process Mining for Location-based Services||마황현||TBD||uMining||
* '''Overview''' : This program aims to cultivate the experts for smart factory. The master or doctoral students in industrial and management systems engineering, mechanical engineering, and computer engineering can join this program. They will take the course specialized in smart factory and conduct industry-academy research projects. The students can learn operational technology (demand forecasting, production planning and control, production quality analysis, scheduling, inventory and logistics management), manufacturing technology (additive manufacturing, production systems, manufacturing processes, industrial robots, prognostics and health management), and analytics technology (big data analytics, artificial intelligence, machine learning, data mining, process mining, computer vision, and time-series analysis).
* '''Full title''' : Developing fault diagnostics and prognostics technology based on sensory data using one-class classification techniques
* '''Development of Reverse Engineering Techniques Using CAD Models and 3D Scan Data''', 2018.7~2019.2, Hyundai NGV.
* '''Smart Factory Applications for Big Data in Manufacturing'''
* '''Overview''' : Smart factory means the advanced manufacturing environment in production and operations by embracing information and communication technologies (ICT) such as the Internet of Things (IoT), the cloud, cyber-physical systems (CPS) and big data analytics. In the smart factory of the future, the convergence of information technology and factory automation pursues the advanced manufacturing in most activities such as demand forecasting, production planning and control, scheduling, inventory and logistics management. We are developing smart factory systems and applications based on big manufacturing data, collaborating with KITECH and ECMiner. For example, several models for production quality analysis, such as classification models, prediction models, factor analysis, and pattern discovery, are being developed and implemented in form of a manufacturing data mining library.
* '''Keywords''' : '''''smart factory''''', Industry 4.0, manufacturing data analytics, data mining library, IoT
* '''Product Design Support Systems Using Cognitive and Emotional Databases'''
* '''Keywords''' : '''''product design''''', user experience (UX), semantic web, database design
* '''Development of Process Mining Tools for Analyzing Main Control Rooms in Nuclear Plants''', 2016.2~2016.7, KAERI.
* '''Research and Business Development of Distribution/Logistics Support Technology Using Public Big Data in Gyeonggi''', 2016.3~2016.6, GSTEP.
* '''Reality Mining for Regional Big Data''', 2013.6~2016.5, NRF.
* '''Full title''' : Development of Reality Mining Models and Systems from Regional Big Data for Comprehension of Individual and Social Behaviors
* '''Overview''' : In this research, ''we aim at developing reality mining models to understand individual's life and social mobility behaviors by analyzing regional big data''. Traditional approaches of social science intended to interpret macro social behaviors through statistical analysis, while the new approach of data science enables computational social science by analyzing raw big data related to human behaviors and understanding macro social behaviors. In this research, for the purpose of investigating location-based data, we will perform interdisciplinary studies by combining a variety of accessible open data to comprehend social phenomena.
* '''Keywords''' : '''''big data analytics''''', computational social science, spatio-temporal data analysis, geographical information science
<Raw public transp. data in Seoul> <Discovered movement patterns in Seoul> <Convenience map in Seoul>
* '''Decision Mining for Business Processes'''
* '''Overview''' : Business process and decision modeling is an important aspect in the management of every organization. Real-life business and operational processes can become very complex, so that creating a normative model ensures the desired process execution. A business process can often be regarded as the implementation of business decisions in a way that optimizes a set of business criteria and does not violate a set of process controls. Decisions are typically based upon a number of business rules that describe the premises and possible outcomes of a specific situation. Business decisions are important, but thus currently often remain hidden in process flows, process activities or in the head of employees, so that they need to be discovered using state-of-art data and process mining techniques.
* '''Keywords''' : '''''decision science''''', business analytics, data mining, process mining, business process management
- Seminar . . . . 1 match
* Kyuhyup Oh : Sensor data management
* 8/12(Thu) - Aekyung - Process Mining
- Seminar08 . . . . 2 matches
Introduction to ProM Mining Menu (2) : 박현미
Introduction to ProM Mining Menu (1) : 박현미
- Seminar09 . . . . 1 match
PAIS ch10(Process Mining) : 김애경, 마황현
- Seminar10 . . . . 1 match
* 3/15(Mon) : Kyuhyup (Process mining of RFID-based supply chain), Kyuri (Process instantiation)
* 8/12(Thu) - Aekyung - Process Mining
- Seminar15 . . . . 10 matches
* 교재: DATA MINING AND BUSINESS ANALYTICS WITH R
* Big Data Team: 규협, 현구, 동현, 애경 (주로 교통데이터 사용)
* Process Mining Team: 진성, 서현, 광진 (주로 통신사데이터 사용)
* R (Data Mining) : 현구, 진성, 서현, 규협, Jung => 목 10:00
* 진성: Outsourcing (제출), WIP-Process Mining (방학 때 한글로 작성)->ProM
* 서현 : Twitter를 ProM으로 분석해보고 Process Discovery for SNS Data (e.g. Twitter)
* 광진 : CF for Process Mining -> 주제 명확히
* 광진,진성 : 흐름율(Little's Law) for Process Mining (?)
* 교재: DATA MINING AND BUSINESS ANALYTICS WITH R
* Big Data Team: 규협, 현구, 동현, 애경 (주로 교통데이터 사용)
* Process Mining Team: 진성, 서현, 광진 (주로 통신사데이터 사용)
- Seminars . . . . 9 matches
* keyword : data-driven, process mining, GNN (ML/DL), dispatching
* 서현: Process mining + GNN
* Object-centric PMining https://www.youtube.com/watch?v=5PES99aVpW4
* Celonis https://www.celonis.com/blog/what-is-object-centric-process-mining-ocpm/
* "Transformer" -> Graph Transformer (OHT, AS/RS) -> Process Mining [연구재단과제] *** 이론
* data_preparation_for_machine_learning (1주)
* https://www.dataq.or.kr/www/accept/schedule.do
- 전류 data 기반 고장진단 Survey => "서현"
* data generation 검토 : 오늘 저녁
* pmining for dispatching
* 12/02 - Process Mining Simulation Papers 4편 (동현,영석,유리,지민) => YouTube 시청 및 토의
* 데이터 => DB => Feature Extraction => Training data file 생성 => 예측모형 학습 => 성능 평가
* Process mining (Graph 생성) => GNN**
* Choi : BN based Decision Mining
* 서현 : Decision mining with BBN. refer two papers of our lab.
* Cao, Z., Tian, Y., Le, T. D. B., & Lo, D. (2018). Rule-based specification mining leveraging learning to rank. Automated Software Engineering, 25(3), 501-530.
* Josue: 1) submission of RuleCOSI++ to ESWA, 2) paper preparation of SIMTech dataset to mfg journal
* 한국해양과학연구원 : BigData for Smart Ship, PHM/CBM/TBM
* 정언: PHM Challenge 2016 Data 소개 & DL 적용 방안
* FBHQ D3 (Data-Driven Design) - 준모
- Tools . . . . 1 match
==== Database Servers ====
- WikiSlide . . . . 1 match
* If two people edit a page simultaneously, the first can save normally, but the second will get a warning and should follow the directions to merge their changes with the already saved data.
* don't create arbitrary wikinames (`OracleDatabase`, ''not'' `OraCle`)
- WorkingPapers . . . . 3 matches
1. '''Young-Yong Jung''', '''Hoonseok Park''', Sang-Chul Kim, and Jae-Yoon Jung*, "Effect analysis among major factors affecting severe injury based on traffic accident data", ''Accident Analysis and Prevention''.
1. '''Parul Singh''', '''Min Kyu Sim''', and Jae-Yoon Jung*, "Analysis of Smart Card Data in Public Transportation: A Literature Review", ''TR-C'', under revision.
1. Jae-Yoon Jung et al., "Big Data in Supply Chain Management".
1. (Korean) Jae-Yoon Jung and '''Hwanghyun Ma''', "Process Mining for GPS", C&GIS: new SSCI (0.611)
- dataset . . . . 5 matches
* [https://archive.ics.uci.edu/ml/datasets.html UCI Machine Learning Repository]
* [https://www.kaggle.com/datasets Kaggle Datasets]
* [https://github.com/caesar0301/awesome-public-datasets#time-series Awesome Public Datasets]
* [https://datamarket.com/data/ DataMarket]
* [http://socialcomputing.asu.edu/pages/datasets Social Computing Datasets]
* [https://datamarket.com/data/list/?q=provider:tsdl Time Series Data Library]
* [http://www.cs.ucr.edu/~eamonn/time_series_data/ UCR Time Series Classification Archive]
- edUFlow . . . . 1 match
The project of edUFlow born aroused to help the contemporary organizations to face challenges about how to monitor real-time events and control these activities in the business processes, which can offer rich information for context-awareness. In this research aims at developing a real-time process mining and control system based on RFID and USN data. The RFID / USN data is exploited for such process mining techniques, instead of log data in information system. RFID/USN data can provide rich, but huge real-time event of physical objects such as humans, products, and locations. Thus, for obtain knowledge we uses de Complex Event Processing (CEP) that is a set of techniques, methods and tools that aim at monitoring events generated in diverse information systems; the tasks to be executed by a CEP Engine are specified in Event Pattern Language (EPL); then we address how CEP technology can be applied to business processes in ubiquitous enterprise services on based on context-awareness. To address the issue, we propose a method for semantic annotation of EPL in which the semantics of a situation drives the transformation EPL templates into executable EPL statements, ready to run on specific CEP Engine.
1. Kyuhyup Oh and Jae-Yoon Jung, "A Framework of Process Mining for RFID Event Analysis", In Proc. of the 2nd International Conference on Industrial Engineering and Operations Management (IEOM2011), Jan 2011.
- lab . . . . 4 matches
* [공통] Energy Data 및 Code 관리 (GitHub)
* Ship Data 분석
* GitHub for Smart Energy Research (Dataset, Code)
* Game Data Analytics
* Single/double pendulum data로 확장하여 실험 필요
- mbo2010 . . . . 2 matches
|| ||- Weka를 이용한 RFID Process Mining 구현 및 작성||
7,8월 : Workout, Writting, Grammar in Use, Process Mining
- mbo2011 . . . . 1 match
|| 9월 ||- ProcessMining 공부(수업복습 포함) || '''(O)''' || ||
- mbo2012 . . . . 1 match
* 국외 저널 2편 등재 --> Process Change Pattern / Process Mining Technique for Performer Recommendation / Process Descovery Algorithm for Parallelism Process
- mbo2013 . . . . 2 matches
* 국외 저널 2편 준비 --> Process Mining Technique for Performer Recommendation / Curriculum Recommendation (Education Mining)
- mbo2014 . . . . 5 matches
* SNS/opinion mining/GIS (with 광진, 서현)
||겨울 방학||x학술대회 - SNS/opinion mining/GIS (계획 수립,데이터수집,간략한실험/with 광진, 서현)||
||1학기||x학술대회 - SNS/opinion mining/GIS (논문 작성 및 발표/with 광진, 서현)||
|| 3월 ||학술대회 - SNS/opinion mining/GIS (계획 수립,데이터수집,간략한실험/with 광진, 서현)||-- ||X||
|| 4월 ||학술대회 - SNS/opinion mining/GIS (실험/with 광진, 서현)||-- ||--||
|| 5월 ||학술대회 - SNS/opinion mining/GIS (논문작성 및 제출/with 광진, 서현)||-- ||--||
|| ||- Search about similar approaches to HMM in Process Mining || O || ||
|| Jul||- Finish preparing experiments code and data || -- || -- ||
|| ||- Lee, I. Cai, G. Lee, K., "Exploration of geo-tagged photos through data mining approaches", Expert systems with applications, Vol.41, No.2, 2014, pp.397-405 ||
|| ||- Khan, F.H. Bashir, S. Qamar, U., "TOM: Twitter opinion mining framework using hybrid classification scheme". Decision support systems, Vol.57, 2014, pp.245-257 ||
||4월||연구 - 주제 확정 및 Data Mining기법공부 ||O ||-- ||
||5월||연구 - Data Mining기법공부 샘플데이터를 통한 방법론적 연구분석(R)||X ||-- ||
|| 5월 ||학부 Project 수행 잘하기(Especially, data mining)||-- ||-- ||
- mbo2015 . . . . 3 matches
SNS/opinion mining/GIS (with 광진, 서현) => 광진(전자거래학회지) : Reality 실적 => 확장 후 제출 AK
?? => SPC for Big Data
* Jae-Yoon Jung, Hwanghyun Ma, and Minseok Song, "Process Mining for GPS", C&GIS: new SSCI (0.611)
* Jae-Yoon Jung et al., "Big Data in Supply Chain Management", submitted to Journal.
- mbo2016 . . . . 4 matches
* 박사 논문 주제? Mfg Data Mining for Smart Factory 세부 주제 3개 - Data-driven Scheduling or Process Mining for Scheduling
1. Facebook data (~Nov. 7)
* Guatemalan data
* Ensemble process mining
- mbo2021 . . . . 2 matches
* [공통] Energy Data 및 Code 관리 (GitHub)
* Ship Data 분석
- meeting . . . . 28 matches
* 저널: [http://www.tandfonline.com/loi/tgis20#.VYbLxfmqpBd IJGIS (geo-science)] [http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=69 IEEE TKDE (algorithm)] [http://www.sciencedirect.com/science/journal/aip/03064379 IS (process mining)]
1. '''Mining Study'''
* Josue: 석사논문제출->DecisionMining연구 및 제출
* Berny: DecisionMining연구 및 제출
- Reality Mining : IEICE 논문게재료 처리 필요함 (영수증제출필요 by JSLee)
- 기존 WR : 이진성("서비스 모듈화"), 오규협("Spatial MZP"), 김애경("DT Miner)", Berny("HMM Mining"), Josue("Facebook Mining")
- 신규 WR: 문현구("교통편의성"), 최서현("Twitter Mining")
- Reality Mining: 연구원 참여 확인 필요. IEICE 논문게재료 처리 필요함 (Jung 결제완료).
- Reality Mining: 협약계획서 제출(Jung & JLee), 계약후 IEICE 논문게재료 처리 필요함.
- Process Mining Study Group: Progress?
- Reality Mining: 연구방향 점검
- Reality Mining: 연차보고서 작성 계획 -> 실적부분 채우기***(Jung), 인센티브 신청***(Jung)
- Reality Mining: 연차보고서 메일 확인
- Reality Mining: 여비를 부산 산업공학회에서 소진
1) Reality Mining 회의
- Reality Mining : 인센티브 신청(Jung), 과제비 조정(JS) -> 확인 필요
- Reality Mining: DB설계 검토*** (애경 등)
'''- Reality Mining : 인센티브 신청(Jung), 과제비 조정(JS)'''
* [DT] Process Mining using Decision Tree (AK/Josue)
* [Subway] Reality Mining in Subway Networks (KH)
- phm . . . . 2 matches
* [https://c3.nasa.gov/dashlink/projects/15/ NASA Prognostics Data Challenge]
* [http://www.phmsociety.org/references/ijphm-archives/2016/Sp5 IJPHM 2016 Special Issue on Smart Manufacturing PHM] / Special Issue Big Data and Analytics]
- summer09 . . . . 2 matches
1. '''Data Mining 스터디''' (7월중)
- test1 . . . . 19 matches
· Incorporation of comments clarifying the use of workflow relevant data within the basic model
called a process model, a process template, process metadata, or a process definition. For purposes of this
standardisation to enable the interchange of process definition data between different build-time tools and runtime
updating an orders database with a new record). Interaction with the process control software is necessary to
and pass the appropriate data, etc. There are several benefits in having a standardised framework for
Databases
· specifications for process definition data and its interchange
interaction with paper-based information, which may need to be captured as image data as part of an
automation process. Once paper based information has been captured electronically as image data, it is often
developed using transaction management facilities within TP monitors and/or Database Management software.
· various types of system definition and control data (shown unfilled) which are used by one or more
· applications and application databases (shown in light fill) which are not part of the workflow product, but
applications which may to be invoked, definition of any workflow relevant data which may need to be
External product/data
System control data
Data
data
Data
Data
control data either centralised or distributed across a set of workflow engines; this workflow control data
Found 38 matching pages out of 185 total pages
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