Page 84 - 2017 Brochure
P. 84
研究員
葉彌妍 Mi-Yen Yeh
Associate Research Fellow
Ph.D., Electrical Engineering, National Taiwan University
Tel: +886-2-2788-3799 ext. 1412 Fax: +886-2-2782-4814
Email: miyen@iis.sinica.edu.tw
http://www.iis.sinica.edu.tw/pages/miyen
• Associate Research Fellow, Institute of Information Science, Academia Sinica (2014-present)
• Associate Professor (Joint appointed), Computer Science and Information Engineering, National Cheng Kung University (2015-present)
• Assistant Research Fellow, Institute of Information Science, Academia Sinica (2009-2014)
• Assistant Professor (Joint appointed), Computer Science and Information Engineering, National Cheng Kung University (2014-2015)
• Visiting Researcher, Microsoft Research, Redmond, WA, USA (2015-2016)
• Visiting Researcher, Simon Fraser University, Canada (2012)
• Visiting Researcher, Microsoft Research Asia, Beijing, China (2011)
Research Description I am also interested in issues related to data management and
mining with non-volatile memory (NVM) based storage systems.
My research interests cover the area of data mining and databases. NVM has become a popular storage medium for mobile devices
I have been focusing on designing effective and efficient data and even servers due to its rapidly increasing capacity and low
analysis methods for various data types, including social networks, idle power. It has even become a popular alternative to DRAM as
time series, and location trajectories. For example, I have designed main memory to enable in-memory computation for large-scale
algorithms to reduce random errors for similarity searches, data applications. However, the write operation of NVM has longer
to identify object uniqueness from peers based on character latency and higher energy consumption, which motivates the NVM-
sequences and social networks, and to analyze moving behavior friendly design of data management and mining algorithms. My
from trajectories. In addition, acknowledging the fast-growing scale team has been working on designing such algorithms including
of the modern social networks and the challenges imposed in database indexing, logging and recovery, caching over flash
analyzing them, I have been focused on designing sampling and memory, and pattern mining.
summarization algorithms for heterogeneous social networks to
preserve or extract their key parameters, structure and semantic
information. Recently, we are also interested in how machine
learning techniques can be applied in the real-time bidding of
online advertising.
Publications 7. Hua-Wei Fang, Mi-Yen Yeh, and Tei-Wei Kuo, “MLC-Flash-Friendly
Logging and Recovery for Databases,” Proc. of the 28th ACM
1. Wush Chi-Hsuan Wu, Mi-Yen Yeh, and Ming-Syan Chen, “Predicting Symposium on Applied Computing (SAC-2013), March 18-22, 2013.
Winning Price in Real Time Bidding with Censored Data,” Proc. of (Best paper award)
the 21st ACM SIGKDD International Conf. on Knowledge Discovery
and Data Mining (KDD-2015), August, 2015. 8. Wush Chi-Hsuan Wu, Mi-Yen Yeh, and Jian Pei, “Random Error
Reduction in Similarity Search on Time Series: A Statis tical
2. Pei-Lun Suei, Mi-Yen Yeh, Tei-Wei Kuo, “Endurance-Aware Flash- Approach,” Proceedings of the 28th IEEE International Conference on
Cache Management for Storage Servers,” IEEE Trans. on Computers, Data Engineering (ICDE-2012), April 1-5, 2012.
Volume 63, Issue 10, pp. 2416-2430, 2014.
9. Mi-Yen Yeh, Kung-Lung Wu, Philip S. Yu, and Ming-Syan Chen,
3. Yi-Chen Lo, Jhao-Yin Li, Mi-Yen Yeh, Shou-De Lin, Jian Pei, “What “LEEWAVE: Level-Wise Distribution of Wavelet Coefficients for
Distinguish One from Its Peers in Social Networks,” Data Mining and Processing kNN Queries over Distributed Streams,” Proceedings of
Knowledge Discovery, special issue of ECML/PKDD 2013 journal the 34th International Conference on Very Large Data Bases (VLDB-
track, Volume 27, Issue 3, pp. 396-420, Springer, July, 2013. 2008), August 24-30, 2008.
4. Huey-Ru Wu, Mi-Yen Yeh, and Ming-Syan Chen, “Profiling Moving 10. Mi-Yen Yeh, Bi-Ru Dai, and Ming-Syan Chen, “Clustering over
Objects by Dividing and Clustering Trajectories Spatiotemporally,” Multiple Evolving Streams by Events and Correlations,” IEEE
IEEE Trans. on Knowledge and Data Engineering, Vol. 25 , Issue 11, Transactions on Knowledge and Data Engineering, Vol. 19, No. 10,
pp.2615 – 2628, Nov. 2013. pp. 1349-1362, October 2007.
5. Chia-Chen Yen, Mi-Yen Yeh, and Ming-Syan Chen, “An Efficient
Approach to Updating Closeness Centrality and Average Path
Length in Dynamic Networks,” Proc. of the 2013 IEEE International
Conference on Data Mining (ICDM-2013), December 2013.
6. Jian Pei, Wush Chi-Hsuan Wu, and Mi-Yen Yeh, “On Shortest Unique
Substring Queries,” Proc. of the 29th IEEE International Conference
on Data Engineering (ICDE-2013), April 8-12, 2013.
82 研究人員 Research Faculty
葉彌妍 Mi-Yen Yeh
Associate Research Fellow
Ph.D., Electrical Engineering, National Taiwan University
Tel: +886-2-2788-3799 ext. 1412 Fax: +886-2-2782-4814
Email: miyen@iis.sinica.edu.tw
http://www.iis.sinica.edu.tw/pages/miyen
• Associate Research Fellow, Institute of Information Science, Academia Sinica (2014-present)
• Associate Professor (Joint appointed), Computer Science and Information Engineering, National Cheng Kung University (2015-present)
• Assistant Research Fellow, Institute of Information Science, Academia Sinica (2009-2014)
• Assistant Professor (Joint appointed), Computer Science and Information Engineering, National Cheng Kung University (2014-2015)
• Visiting Researcher, Microsoft Research, Redmond, WA, USA (2015-2016)
• Visiting Researcher, Simon Fraser University, Canada (2012)
• Visiting Researcher, Microsoft Research Asia, Beijing, China (2011)
Research Description I am also interested in issues related to data management and
mining with non-volatile memory (NVM) based storage systems.
My research interests cover the area of data mining and databases. NVM has become a popular storage medium for mobile devices
I have been focusing on designing effective and efficient data and even servers due to its rapidly increasing capacity and low
analysis methods for various data types, including social networks, idle power. It has even become a popular alternative to DRAM as
time series, and location trajectories. For example, I have designed main memory to enable in-memory computation for large-scale
algorithms to reduce random errors for similarity searches, data applications. However, the write operation of NVM has longer
to identify object uniqueness from peers based on character latency and higher energy consumption, which motivates the NVM-
sequences and social networks, and to analyze moving behavior friendly design of data management and mining algorithms. My
from trajectories. In addition, acknowledging the fast-growing scale team has been working on designing such algorithms including
of the modern social networks and the challenges imposed in database indexing, logging and recovery, caching over flash
analyzing them, I have been focused on designing sampling and memory, and pattern mining.
summarization algorithms for heterogeneous social networks to
preserve or extract their key parameters, structure and semantic
information. Recently, we are also interested in how machine
learning techniques can be applied in the real-time bidding of
online advertising.
Publications 7. Hua-Wei Fang, Mi-Yen Yeh, and Tei-Wei Kuo, “MLC-Flash-Friendly
Logging and Recovery for Databases,” Proc. of the 28th ACM
1. Wush Chi-Hsuan Wu, Mi-Yen Yeh, and Ming-Syan Chen, “Predicting Symposium on Applied Computing (SAC-2013), March 18-22, 2013.
Winning Price in Real Time Bidding with Censored Data,” Proc. of (Best paper award)
the 21st ACM SIGKDD International Conf. on Knowledge Discovery
and Data Mining (KDD-2015), August, 2015. 8. Wush Chi-Hsuan Wu, Mi-Yen Yeh, and Jian Pei, “Random Error
Reduction in Similarity Search on Time Series: A Statis tical
2. Pei-Lun Suei, Mi-Yen Yeh, Tei-Wei Kuo, “Endurance-Aware Flash- Approach,” Proceedings of the 28th IEEE International Conference on
Cache Management for Storage Servers,” IEEE Trans. on Computers, Data Engineering (ICDE-2012), April 1-5, 2012.
Volume 63, Issue 10, pp. 2416-2430, 2014.
9. Mi-Yen Yeh, Kung-Lung Wu, Philip S. Yu, and Ming-Syan Chen,
3. Yi-Chen Lo, Jhao-Yin Li, Mi-Yen Yeh, Shou-De Lin, Jian Pei, “What “LEEWAVE: Level-Wise Distribution of Wavelet Coefficients for
Distinguish One from Its Peers in Social Networks,” Data Mining and Processing kNN Queries over Distributed Streams,” Proceedings of
Knowledge Discovery, special issue of ECML/PKDD 2013 journal the 34th International Conference on Very Large Data Bases (VLDB-
track, Volume 27, Issue 3, pp. 396-420, Springer, July, 2013. 2008), August 24-30, 2008.
4. Huey-Ru Wu, Mi-Yen Yeh, and Ming-Syan Chen, “Profiling Moving 10. Mi-Yen Yeh, Bi-Ru Dai, and Ming-Syan Chen, “Clustering over
Objects by Dividing and Clustering Trajectories Spatiotemporally,” Multiple Evolving Streams by Events and Correlations,” IEEE
IEEE Trans. on Knowledge and Data Engineering, Vol. 25 , Issue 11, Transactions on Knowledge and Data Engineering, Vol. 19, No. 10,
pp.2615 – 2628, Nov. 2013. pp. 1349-1362, October 2007.
5. Chia-Chen Yen, Mi-Yen Yeh, and Ming-Syan Chen, “An Efficient
Approach to Updating Closeness Centrality and Average Path
Length in Dynamic Networks,” Proc. of the 2013 IEEE International
Conference on Data Mining (ICDM-2013), December 2013.
6. Jian Pei, Wush Chi-Hsuan Wu, and Mi-Yen Yeh, “On Shortest Unique
Substring Queries,” Proc. of the 29th IEEE International Conference
on Data Engineering (ICDE-2013), April 8-12, 2013.
82 研究人員 Research Faculty
