At APL, we study the problem of composing and orchestrating large-scale distributed intelligent applications effectively and easily. Our interest spans across many interdisciplinary research subjects such as complex event processing, service choreography, automatic application composition, distributed messaging middlewares, applied data mining and even information visualization! Our studies can be applied to various fields such as Web of Things (WoT), real-time omni-channel marketing on O2O application platforms, context-aware personalized services and enterprise application integration.



[Integrated Medical Care Services Platform]
In collaboration with VHS Medical Center in South Korea we develop an event-driven service choreography platform for patient-oriented and integrated medical care services. In the core of our platform lies a content-based distributed publish/subscribe system as an enterprise service bus (ESB). We also seek new technologies for improved service recommendation to patients through Big Data analytics. In collaboration with Professor Haesung Seok and Professor Yunchul Kang from the Department of Industrial Engineering.
Polymash service composition [ Intelligent IoT/WoT Application Composer ]
We study new ways of mashing up things connected on the Internet and the Web. Given unstructure description of the things, we aim to devise an engine that can accept application mashup requests issued in natural language and even over voice. This project introduces many interesting cross-disciplinary research problems in the areas of semantic interoperability, natural language understanding, dialogue management and seamless biometric-based speaker recognition.
Secret Forwarding II [ Secure Information Network for Autonomous Vehicles ]
In collaboration with CUBE, we are designing a private blockchain network for the purpose of securely and reliably deliverying software updates to autonomous vehicles. In addition, we conduct research on efficient and scalable ways to collect on-board diagonisis and passenger information for Big Data analytics.
Sports Analytics [ Poor Men's Sports Analytics based on Deep Learning! ]
Based on visual object recognition technologies such as Yolo and R-CNN technology, we automatically extract complex network information that reflects how team sports athelets perform and interact each other. We rely on single camera view for a cost-effective analytics. We aim to maintain quality of the analytics with novel calibration techniques. We work closely with the team led by Professor Jinha Hwang from the Department of Meterials Science and Engineering.
HAPPY Traffic [ Traffic Congestion Prediction using Deep Learning ]
Based on Deep Learning we predict traffic condition on road systems of Seoul Metropolitan area. We correlate traffic information with various data such as weather, public transit usage, gas price, sporting events, accidents in order to provide more accurate and contextual analytics.
WowSan Publish/Subscribe [ Distributed Publish/Subscribe Messaging Substrate ]
We are developing an ultra-high-throughput and low-latency complex event processing system that runs on a dynamically reconfigurable content-based publish/subcribe overlay network. We are also designing a new expressive publish/suibscribe language that supports subscriptions to binary data such as multimedia data.
NetBig big data platform [ Big Data Processing and Analytics Platform ]
With our industrial partner NetcoreTech Inc., we are building a high-performance, fault-tolerant Big Data platform for analyzing network and security situations. We adapt various AI techniques and devise novel information visualization tools in order to enhance context-awareness and accelerate security control procedures.
This project is supported by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education until October 2019.
NetBig big data platform [ Smart Electronic Commerce and Marketing Platform ]
We study adapted machine learning techniques for automatically extracting key information from product descriptions specified in natural language. We also investigate new techniques for real-time targeted advertizing during instant messaging sessions. This project was supported in part by WISET.


Prof. Young Yoon
Ph.D. in Computer Engineering, University of Toronto
M.S. in Computer Sciences, University of Texas at Austin
B.A. in Computer Sciences, University of Texas at Austin

Wonjae Kim
Yongjun Choi
Kyunghwan Sung
Jaeryeol Lee

Junggul Kim
Junghwan Cha
Junoh Moon
Donghyun Kim
Alumni Hana Lee
M.Eng ('18)
Hyunwoo Jung
B.Eng ('18)
Dahee Kim
B.Eng ('18)
Jinwoo Park
B.Eng ('17)
Samsung Electronics
Younghoon Shim
Hana INS
Kyungsoo Park
B.Eng ('17)
Samsung SDS


Hana Lee, Young Yoon (2018) Interest Recognition from Online Instant Messaging Sessions using Text Segmentation and Document Embedding Techniques, to appear at IEEE ICCC

Young Yoon, Wonjae Kim (2018) Bluff-Forwarding: A Practical Protocol for Delivering Refreshed Symmetric Keys on a Multi-Path Big Data Ingestion System, to appear at IEEE Access soon!

Yoon Y, Jung H, Lee H (2018) Abnormal network flow detection based on application execution patterns from Web of Things (WoT) platforms. PLoS ONE 13(1): e0191083. https://doi.org/10.1371/journal.pone.0191083 Open Access

Yoon Y (2017) Per-service supervised learning for identifying desired WoT apps from user requests in natural language. PLoS ONE 12(11): e0187955. https://doi.org/10.1371/journal.pone.0187955 Open Access

Hana Lee, Young Yoon, Engineering doc2vec for Automatic Classification of Product Descriptions on O2O Applications, Electronic Commerce Journal, DOI :10.1007/s10660-017-9268-5, 2017 Link

Young Yoon, YongJun Choi, Suchul Shin: Multilateral Context Analysis based on the Novel Visualization of Network Tomography: Poster. ACM DEBS 2017: 343-344 Link

Hana Lee, Eunsoo Lim, Younghin Cho, Young Yoon, Automatic Classification of Product Data for Natural General-Purpose O2O Application Interface, Proceedings of the 2016 KIPS Annucal Fall Conference, Busan, South Korea (Best Paper Runner-up) Open Access

Young Yoon, Performance Analysis of CRF-Based Learning for Processing WoT Application Requests Expressed in Natural Language. SpringerPlus 5(1):1324, doi:10.1186/s40064-016-3012-9, 2016 Open Access

Yoon Y, Kim BH (2016) Secret Forwarding of Events over Distributed Publish/Subscribe Overlay Network. PLoS ONE 11(7): e0158516. doi: 10.1371/journal.pone.0158516 Open Access

Young Yoon, Nathan Robinson, Vinod Muthusamy, Sheila A. McIlraith, Hans-Arno Jacobsen: Planning the transformation of overlays. SAC 2016: 500-507 Link

Young Yoon, Daehyun Ban, Sungwon Han, Donghyek An, Eunho Heo, Sangho Shin, Device/Cloud Collaboration Framework for Intelligence Applications, Internet of Things: Principes and Paradigms 2016 pp 49 - 60 Link

For past projects and publications, refer to director's c.v.


For thos who are interested in collaboration or joining our lab, feel free to contact us. Office: T808, Hongik University
Lab: T815 and T816, Hongik University
Office Phone: +82 2-320-1659

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