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Sang‐Wook Kim

Hanyang University · Computer Science

About the Lab

Professor Sang-Wook Kim's research lab specializes in data management, information retrieval, and systems optimization with a focus on large-scale data processing, particularly in time-series databases, network influence propagation, and adaptive streaming. The lab develops advanced indexing and similarity search techniques that support complex transformations such as time warping, scaling, and shifting, while ensuring high efficiency and accuracy without false dismissals. Key research directions include scalable algorithms for influence maximization in social networks, robust video streaming adaptation, and efficient B+-tree construction for database systems. The lab emphasizes practical system design that balances theoretical guarantees with real-world performance in dynamic and resource-constrained environments.

time-series retrievalinfluence maximizationadaptive streamingdatabase indexingsimilarity search

Research Overview

Papers
481
Total Citations
4,917
Papers (5y)
86
Primary Field
Computer Science

Research Output Trend

Figures are computed from collected data and may differ slightly.

Publications per year (5y)
86total
2022
2023
2024
2025
2026
Citations per year (5y)
838total
20222023202420252026

Selected Papers

15
1
Article|310 citations·2002
An index-based approach for similarity search supporting time warping in large sequence databases
Sang‐Wook Kim, Sanghyun Park, Wesley W. Chu

This paper proposes a new novel method for similarity search that supports time warping in large sequence databases. Time warping enables finding sequences with similar patterns even when they are of different lengths. Previous methods for processing similarity search that supports time warping fail to employ multi-dimensional indexes without false dismissal since the time warping distance does not satisfy the triangular inequality. Our primary goal is to innovate on search performance without p

Signal ProcessingComputer Science
2
Article|45 citations·2018
Efficient and effective influence maximization in social networks: A hybrid-approach
Yunyong Ko, Hwa Jin Cho, Sang‐Wook Kim
SJR Q1Information SciencesOA

Influence Maximization (IM) is the problem of finding a seed set composed of k nodes that maximize their influence spread over a social network. Kempe et al. showed the problem to be NP-hard and proposed a greedy algorithm (referred to as SimpleGreedy ) that guarantees 63% influence spread of its optimal solution. However, SimpleGreedy has two performance issues: at a micro level , it estimates the influence spread of a single node by running Monte-Carlo (MC) simulations that are fairly expensiv

Statistical and Nonlinear PhysicsPhysics and Astronomy
3
Article|43 citations·2002
Plume Study of a 1.35-kW SPT-100 Using an ExB Probe
Sang‐Wook Kim, Alec D. Gallimore
SJR Q1Journal of Spacecraft and RocketsOA

Covers advancements in spacecraft and tactical and strategic missile systems, including subsystem design and application, mission design and analysis, materials and structures, developments in space sciences, space processing and manufacturing, space operations, and applications of space technologies to other fields.

Astronomy and AstrophysicsPhysics and Astronomy
4
Book Chapter|42 citations·2007
Path Prediction of Moving Objects on Road Networks Through Analyzing Past Trajectories
Sang‐Wook Kim, Jung-Im Won, Jong-Dae Kim, Miyoung Shin, Junghoon Lee, Hanil Kim
SJR Q2Lecture notes in computer science
Signal ProcessingComputer Science
5
Article|39 citations·2015
SimCC: A novel method to consider both content and citations for computing similarity of scientific papers
Masoud Reyhani Hamedani, Sang‐Wook Kim, Dong-Jin Kim
SJR Q1Information Sciences
Artificial IntelligenceComputer Science
6
Article|37 citations·2003
Efficient processing of similarity search under time warping in sequence databases: an index-based approach
Sang‐Wook Kim, Sanghyun Park, Wesley W. Chu
SJR Q1Information Systems
Signal ProcessingComputer Science
7
Article|37 citations·2017
On identifying k -nearest neighbors in neighborhood models for efficient and effective collaborative filtering
Dong‐Kyu Chae, Sang‐Chul Lee, Si-Yong Lee, Sang‐Wook Kim
SJR Q1Neurocomputing
Information SystemsComputer Science
8
Article|35 citations·2015
Efficient recommendation methods using category experts for a large dataset
Won-Seok Hwang, Ho-Jong Lee, Sang‐Wook Kim, Youngjoon Won, Min-soo Lee
SJR Q1Information Fusion
Information SystemsComputer Science
9
Article|33 citations·2007
Using multiple indexes for efficient subsequence matching in time-series databases
Seung-Hwan Lim, Heejin Park, Sang‐Wook Kim
SJR Q1Information Sciences
Signal ProcessingComputer Science
10
Article|30 citations·2015
C-Rank: A link-based similarity measure for scientific literature databases
Seok-Ho Yoon, Sang‐Wook Kim, Sunju Park
SJR Q1Information Sciences
Statistical and Nonlinear PhysicsPhysics and Astronomy
11
Article|28 citations·2015
A community-based sampling method using DPL for online social networks
Seok-Ho Yoon, Ki Nam Kim, Jiwon Hong, Sang‐Wook Kim, Sunju Park
SJR Q1Information Sciences
Statistical and Nonlinear PhysicsPhysics and Astronomy
12
Article|26 citations·2014
An analysis on information diffusion through BlogCast in a blogosphere
Jiwoon Ha, Sang‐Wook Kim, Sang‐Wook Kim, Sang‐Wook Kim, Sang‐Wook Kim, Christos Faloutsos, Sunju Park
SJR Q1Information Sciences
Statistical and Nonlinear PhysicsPhysics and Astronomy
13
Article|26 citations·2020
M-BPR: A novel approach to improving BPR for recommendation with multi-type pair-wise preferences
Yeon-Chang Lee, Taeho Kim, Jaeho Choi, Xiangnan He, Sang‐Wook Kim
SJR Q1Information Sciences
Information SystemsComputer Science
14
Article|23 citations·2019
Autoencoder-based personalized ranking framework unifying explicit and implicit feedback for accurate top-N recommendation
Dong‐Kyu Chae, Sang‐Wook Kim, Jung-Tae Lee
SJR Q1Knowledge-Based Systems
Information SystemsComputer Science
15
Article|23 citations·2017
JacSim: An accurate and efficient link-based similarity measure in graphs
Masoud Reyhani Hamedani, Sang‐Wook Kim
SJR Q1Information Sciences
Artificial IntelligenceComputer Science

Research Areas

Information SystemsArtificial IntelligenceSignal ProcessingStatistical and Nonlinear PhysicsComputer Networks and CommunicationsComputer Vision and Pattern Recognition

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