Selected publications

Multiple Dynamic Outlier-Detection from a Data Stream by Exploiting Duality of Data and Queries (SIGMOD'21)

TBD

[Paper][Slide][Code][Video]

Ultrafast Local Outlier Detection from a Data Stream with Stationary Region Skipping (KDD'20)

A density-based local outlier is a data point having a relatively lower density than its neighbors. We proposed an efficient way of updating the local density distribution from a data stream to detect local outliers continuously. Both exact and approximate solutions are provided.

[Paper][Slide][Poster][Code][Video(full/poster/promotion)]

NETS: Extremely Fast Outlier Detection from a Data Stream via Set-Based Processing (VLDB'19)

A distance-based outlier is a data point having very few neighbors nearby. We proposed an efficient and accurate way of updating the neighbor information from a data stream to detect outliers continuously.

[Paper][Slide][Poster][Code][Video]

Full publications

International publications

  • Yoon, S., Shin Y., Lee, J., and Lee, B. S., Multiple Dynamic Outlier-Detection from a Data Stream by Exploiting Duality of Data and Queries, In Proc. 2021 Int'l Conf. on Management of Data (ACM SIGMOD), June 2021. (To appear)

  • Yoon, S., Lee, J., and Lee, B. S., Ultrafast Local Outlier Detection from a Data Stream with Stationary Region Skipping, In Proc. 26th Int'l Conf. on Knowledge Discovery and Data Mining (ACM KDD), Aug. 2020. [Paper][Slide][Poster][Code][Video(full/poster/promotion)]

  • Yoon, S., Lee, J., and Lee, B. S., NETS: Extremely Fast Outlier Detection from a Data Stream via Set-Based Processing, In Proc. 40th Int'l Conf. on Very Large Data Bases (VLDB) / Proc. of The VLDB Endowment (PVLDB), Aug. 2019. [Paper][Slide][Poster][Code][Video]

  • Park, D., Yoon, S., Song H., and Lee, J., MLAT: Metric Learning for kNN in Streaming Time Series, In Proc. 5th Workshop on Mining and Learning from Time Series (MiLeTS) in conjunction with KDD'19, Aug. 2019. [Paper][Slide][Poster]

  • Shin, Y., Yoon, S., Trirat, P., and Lee, J., CEP-Wizard: Automatic Deployment of Distributed Complex Event Processing, In Proc. 35th Int'l Conf. on Data Engineering (IEEE ICDE), Macau, China, Apr. 2019 (demo). [Paper][Poster][Video]

  • Jung, J.*, Yoon, S.*, Kim, S.*, Park, S., Lee, K., and Lee, U., Social or Financial Goals? Comparative Analysis of User Behaviors in Couchsurfing and Airbnb, In Proc. 2016 Int'l Conf. on Human Factors in Computing Systems (ACM CHI), May 2016 (late-breaking work) (* equal contribution). [Paper]

Domestic publications

  • Lee, Y., Yoon, S., Lee, J., A Survey on Handling Missing Values in Multivariate Time Series Data Using Deep Learning, SIGDB, Vol. 35 No. 3, 2019, pp. 54-65.

  • Trirat, P., Yoon, S., Lee, J., Spatial and Temporal Analysis of Dangerous Driving Behavior Using DTG Data, SIGDB, Vol. 35 No. 3, 2019, pp. 40-53.

  • Trirat, P., Yoon, S., Lee, J., Study on Driving Behavior of Different Vehicle Types Using DTG Data, SIGDB, Vol. 35 No. 2, 2019, pp. 78-92.

  • Yoon, S., Lee, J., Load Balancing for Distributed Processing of Real-time Spatial Big Data Stream, Journal of KIISE, Vol. 44, No. 11, 2017, pp. 1584-1589.

  • Choi, H., Yoon, S., and Lee.J., Distributed Processing of a RETE Network based on Graph Partitioning, SIGDB, Vol. 33 No. 2, 2017, pp.113-126.

  • Yoon, S., Lee, J., ASKG: Adaptive Spatial Key Grouping for Distributed Processing of Big Spatial Data Stream in Real Time, 2017 Korea Computing Congress (Best Paper Award).

  • Yoon, S., Choi, H., Kim J., and Lee.J., Design and Application of the Complex Event Processing System for Real-Time Spatial Big Data Analysis, SIGDB, Vol.31 No. 3, 2015, pp.136-146.