Tsuyoshi Idé(“Ide-san”; 井手 剛)

Ph.D., Head of Data Science, IBM
Semiconductors at IBM Research

IBM T.J.Watson Research Center
1101 Kitchawan Rd.
Yorktown Heights, NY 10598, USA
B tide@us.ibm.com
https://ide-research.net

Summary
I am passionate about modeling real-world business problems using advanced machine learning methods. I
have a strong track record of successfully leading numerous customer engagements to deliver first-of-a-kind
AI solutions across various industries. Many of the core algorithms I developed in these projects have been
published in top AI and data mining conferences such as AAAI, IJCAI, ICDM, and SDM, establishing me as aleading expert in AI-powered industry solution development at IBM.
In particular, I am recognized as an expert in anomaly and change detection, as evidenced by the two textbooks
I have authored. My primary interest lies in extracting actionable insights from AI algorithms. For example,
you can read my thoughts on the explainability of AI in my LinkedIn article, which is based on my recent
papers published in AAAI 2021 and KDD 2023. My other recent research interests include causal discovery
from stochastic event data for AIOps (NeurIPS 2021) and graph neural networks (AAAI 2022, ICASSP 2023).
Currently, I serve as the Head of Data Science for IBM Semiconductors at the IBM Research Division. In thisrole, I lead several strategic initiatives, including defect causal analysis of the state-of-the-art 2-nanometer
semiconductor fabrication process and simulation-based fab-wide optimization.
I am a Japanese citizen and a U.S. Green Card holder.

Professional Experience
Present - Head of Data Science, IBM Semiconductors, IBM Research.
2023/04/01— Formulate R&D roadmap for technical differentiation of IBM’s Manufacturing Execution
System (MES) using AI/ML technologies.
— Lead technology development with data scientists for Fab-wide optimization in semiconductor
manufacturing.
— Provide consulting based on deep AI/ML expertise to address clients’ pain points insemiconductor manufacturing.

2023/03/31 - Senior Technical Staff Member, IBM Research, T.J. Watson Research Center.
2015/01/01— Perform basic and applied machine learning research as an independent contributor to
publish research outcomes in world premier conferences and journals as well as patents.
— Lead customer engagements and provide technical guidance.
— Play a role of technical evangelist to influence IBM’s technical roadmap based on a broad
range of experiences on real business.

2014/12/31 - Manager, Service Delivery & Risk Analytics, IBM Research, T.J. Watson Research
2013/09/04 Center.
— Engaged in people, project, and research strategy management.
— Proposed new AI-based approaches to IT (information technology) system development,
which resulted in two IBM Outstanding Technical Achievement Awards .

2013/09/03 - Manager, Analytics & Optimization , IBM Research – Tokyo, Japan.
2010/11/01— Represented the entire area of AI at IBM Research – Tokyo.
— Successfully established an organizational management model that achieves an optimalbalance between business and academic contributions.
— Major successful projects include the development of intelligent transportation system in
Kenya and a monitoring system for ocean-going vessels. IBM Japan’s General Manager
Award was given to the latter.

2010/10/31 - Senior/Advisory/Staff Researcher, IBM Research – Tokyo, Japan.
2005/01/01— Led basic and applied research in AI as a technical leader.
— Pioneered a new research field of sensor data analytics, which eventually evolved into IBM’s
IoT Business Unit.
— Major research achievements include the establishment of dependency-based anomaly
detection method, which was awarded Outstanding Technical Achievement Award later.

2004/12/31 - Researcher, IBM Research – Tokyo, Japan.
2000/04/01— Engaged in improving existing IBM products using mathematical science technologies.
— Major contributions include a major improvement of luminance uniformity of IBM ThinkPad
displays and the development anomaly detection solution of computer systems.

Industry knowledge— Semiconductor manufacturing— Financial (change detection, blockchains, federated/collaborative learning, etc.)— Automotive (sensor analytics, simulation, failure analysis, etc.)— Condition-based monitoring (petroleum, AIOps, mining, etc.)— Transportation (railway, maritime, intelligent transportation system, traffic simulation, etc.)— Human behavior modeling (questionnaire data analysis through psychometrics, trajectory analysis, etc.)— Healthcare (diabetes data analytics)— Quantitative project risk management— Display optics design— Power grid failure analysis

Language— Japanese (native)— English (professional proficiency)

Awards2023 Outstanding Technical Achievement Award, IBM Corporation.
For Scientific Contribution to AI for Combinatorial Sparsity

2021 High Value Patent Award, IBM Corporation.
For the invention titled "Learning pattern dictionary from noisy numerical data in distributed
networks"

2019 Research Division Award, IBM Corporation.
For SROM–Smarter Resources and Operations Management Framework

2018 Outstanding Technical Achievement Award, IBM Corporation.
For Business and Technical Leadership in Anomaly Analyzer of Correlational Data

2017 Best Author Award, The Japan Society for Industrial and Applied Mathematics .
For his article titled “Predicting project risks using latent trait model”

2016 Outstanding Technical Achievement Award, IBM Corporation.
For Financial Risk Analytics for Strategic Outsourcing

Outstanding Technical Achievement Award, IBM Corporation.
For End-to-end Contract Profitability Analytics for ITS

2015 Outstanding Technical Achievement Award, IBM Corporation.
For Fundamental Contributions to Anomaly Detection

Research Division Award, IBM Corporation.
For Strategic Outsourcing T&T Repeatable Model Release 11 : Cost Analysis and Optimization2013 General Manager Award, IBM Japan.
For sucsessful development of cloud-based vessel monitoring system

2007 Winner, ICDM Data Mining Contest, IEEE ICDM 2007, The 2007 Seventh IEEE
International Conference on Data Mining.

2006 JSAI Annual Conference Award, The Japanese Society for Artificial Intelligence.
For his paper titled “Theoretical basis for subsequence time-series clustering” presented at the 20th
Annual Conference of the Japanese Society for Artificial Intelligence2004 JSAI Annual Conference Award, The Japanese Society for Artificial Intelligence.
For his paper titled “Eigenspace Approach to Anomaly Detection in Computer Systems” presented
at the 18th Annual Conference of the Japanese Society for Artificial Intelligence

1993 Hatakeyama Award, The Japan Society of Mechanical Engineers.
1990 Hatakeyama Award, The Japan Society of Mechanical Engineers.

Academic degrees
2000/03/31 Ph.D. University of Tokyo, Department of Physics, Graduate School of Science.
Thesis : “Theoretical Study on Nonlocal Effects in Resonant X-Ray Emission Spectra of StronglyCorrelated Systems” (Supervisor : Prof. Kotani Akio)

1997/03/31 MSc. in Physics, University of Tokyo, Department of Physics, Graduate School of
Science.
1993/03/31 BEng in Mechanical Engineering, Tohoku University, Department of Mechanical Engineering, School of Engineering.

Publications : Conference proceedings (refereed)
1. Decentralized Collaborative Learning Framework with External Privacy Leakage Analysis
Tsuyoshi Idé, Rudy Raymond, Dzung T. Phan,
Proceeding of 2023 International workshop Blockchain Kaigi (BCK 23, Oct. 28-29, 2024, Kobe, Japan),
JPS Conference Proceedings, pp. 10.7566, 2024.
2. Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes
Dongxia Wu, Tsuyoshi Idé, Georgios Kollias, Jiri Navratil, Aurelie Lozano, Naoki Abe, Yian Ma, andRose Yu,
Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024,
May 2-4, 2024, Valencia, Spain), pp. 415-423, 2024.
3. Generative Perturbation Analysis for Probabilistic Black-Box Anomaly Attribution
Tsuyoshi Idé, Naoki Abe,
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD
2023, August 6-10, 2023, Long Beach, California, USA), pp.845–856, 2023.
4. Direction aware positional and structural encoding for directed graph neural networks
Yonas Sium, Georgios Kollias, Tsuyoshi Idé, Payel Das, Naoki Abe, Aurelie Lozano,
Proceedings of the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing(ICASSP 2023, June 4-10, 2023, Rhodes Island, Greece), pp.1–5, 2023.
5. Directed Graph Auto-Encoders
Georgios Kollias, Vasileios Kalantzis, Tsuyoshi Idé, Aurelie Lozano, Naoki Abe,
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 22, February 22-March
1, 2022, virtual), pp.7211–7219, 2022.
6. Cardinality-Regularized Hawkes-Granger Model
Tsuyoshi Idé, Georgios Kollias, Dzung T. Phan, Naoki Abe,
Advances in Neural Information Processing Systems 34 (NeurIPS 2021), pp.2682-2694, 2021.
7. Decentralized Collaborative Learning with Probabilistic Data Protection
Tsuyoshi Idé, Rudy Raymond
Proceedings of the 2021 IEEE International Conference on Smart Data Services (SMDS 21, September
5-10, 2021, virtual), pp.234-243, 2021.
8. Anomaly Attribution with Likelihood Compensation
Tsuyoshi Idé, Amit Dhurandhar, Jiří Navrátil, Moninder Singh, Naoki Abe
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 21, February 2-9, 2021,
virtual), pp.4131–4138, 2021.

9. Predicting Nocturnal Hypoglycemia under Free-Living Conditions from Continuous Glucose
Monitoring Data with Extended Prediction Horizon
Long Vu, Sarah Kefayati, Tsuyoshi Idé, Venkata Pavuluri, Gretchen Jackson, Lisa Latts, Yuxiang Zhong,Pratik Agrawal, and Yuan-chi Chang
Proceedings of the AMIA 2019 Annual Symposium (AIMA 19), pp.874-882, 2019.
10. Efficient Protocol for Collaborative Dictionary Learning in Decentralized Networks
Tsuyoshi Idé, Rudy Raymond, and Dzung T. Phan
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI 19),
pp. 2585–2591, 2019.
11. `0 -Regularized Sparsity for Probabilistic Mixture Models
Dzung T. Phan and Tsuyoshi Idé
Proceedings of the SIAM International Conference on Data Mining (SDM 19), pp.172-180, 2019.
12. Tensorial Change Analysis using Probabilistic Tensor Regression
Tsuyoshi Idé
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), pp.3902–3909,
2019.
13. Collaborative Anomaly Detection on Blockchain from Noisy Sensor Data
Tsuyoshi Idé
Proceedings of 2018 IEEE International Conference on Data Mining Workshops (ICDMW) ; Workshop
on Blockchain Systems for Decentralized Mining (BSDM), pp.120-127, 2018.
14. Multi-task Multi-modal Models for Collective Anomaly Detection
Tsuyoshi Idé, Dzung T. Phan, and Jayant Kalagnanam
Proceedings of 2017 IEEE International Conference on Data Mining (ICDM 17), pp. 177-186, 2017.
15. A Novel `0 -constrained Gaussian Graphical Model for Anomaly Localization
Dzung T. Phan, Tsuyoshi Idé, Jayant Kalagnanam, Matt Menickelly, and Katya Scheinberg
Proceedings of the 17th International Conference on Data Mining Workshops (ICDMW 2017), pp.830-833,
2017.
16. Sparse Gaussian Markov random field mixtures for anomaly detection
Tsuyoshi Idé, Ankush Khandelwal, and Jayant Kalagnanam
Proceedings of 2016 IEEE International Conference on Data Mining (ICDM 16), pp. 955-960, 2016.
17. Unsupervised object counting without object recognition
Takayuki Katsuki, Tetsuro Morimura, and Tsuyoshi Idé
Proceedings of the 23rd International Conference on Pattern Recognition (ICPR 2016), pp. 3616-3621,
2016.
18. Change detection using directional statistics
Tsuyoshi Idé, Dzung T. Phan, and Jayant Kalagnanam
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI 16), pp.
1613-1619, 2016.
19. Informative prediction based on ordinal questionnaire data
Tsuyoshi Idé and Amit Dhurandhar
Proceedings of 2015 IEEE International Conference on Data Mining (ICDM 15), pp.191-200, 2015.
20. Probabilistic text analytics framework for information technology service desk tickets
Kuan-Yu Chen, Ee-Ea Jan, and Tsuyoshi Idé
Proceedings of the 14th IFIP/IEEE International Symposium on Integrated Network Management (IM2015), pp.870-873, 2015.
21. Latent trait analysis for risk management of complex information technology projects
Tsuyoshi Idé, Sinem Güuven, Ee-Ea Jan, Sergey Makogon, and Alejandro Venegas
Proceedings of the 14th IFIP/IEEE International Symposium on Integrated Network Management (IM2015), pp.305-312, 2015.
22. Probabilistic two-level anomaly detection for correlated systems
Bin Tong, Tetsuro Morimura, Einoshin Suzuki, and Tsuyoshi Idé
Proceedings of the 21st European Conference on Artificial Intelligence (ECAI 2014),pp.21-23, 2014.

23. Mining for gold : How to predict service contract performance with optimal accuracy basedon ordinal risk assessment data
Sinem Güuven, Mathias Steiner, Tsuyoshi Idé, Sergey Makogon, and Alejandro Venegas
Proceedings of the 11th IEEE International Conference on Services Computing (IEEE SCC 2014),
pp.315-322, 2014.
24. Solving inverse problem of Markov chain with partial observations
Tetsuro Morimura, Takayuki Osogami, and Tsuyoshi Idé
Proceedings of Neural Information and Processing Systems (NIPS 2013), pp.1655-1663, 2013.
25. Monitoring entire-city traffic using low-resolution web cameras
Tsuyoshi Idé, Takayuki Katsuki, Tetsuro Morimura, and Robert Morris
Proceedings of ITS World Congress Tokyo 2013, Number 3143, 2013.
26. Identifying the optimal road closure with simulation
Takayuki Osogami, Hideyuki Mizuta, and Tsuyoshi Idé
Proceedings of ITS World Congress Tokyo 2013,Number 3178, 2013.
27. Predicting battery life from usage trajectory patterns
Toshiro Takahashi and Tsuyoshi Idé
Proceedings of the 19th International Conference on Pattern Recognition (ICPR 2012), pp.2946-2949,
2012.
28. X10-based massive parallel large-scale traffic flow simulation
T. Suzumura, S. Kato, T. Imamichi, M. Takeuchi, H. Kanezashi, Tsuyoshi Idé, and T. Onodera
Proceedings of the ACM SIGPLAN 2012 X10 Workshop (X10 12), pp.3 :1-3 :4, 2012
29. Nonlinear optimization to generate non-overlapping random dot patterns
Takashi Imamichi, Hidetoshi Numata, Hideyuki Mizuta, and Tsuyoshi Idé
Proceedings of the Winter Simulation Conference 2011 (WSC 11), pp.2419-2430, 2011.
30. Trajectory regression on road networks
Tsuyoshi Idé and Masashi Sugiyama
Proceedings of AAAI Conference on Artificial Intelligence (AAAI 11), pp.203-208, 2011.
31. Proximity-based anomaly detection using sparse structure learning
Tsuyoshi Idé, Aurelie C. Lozano, Naoki Abe, and Yan Liu
Proceedings of 2009 SIAM International Conference on Data Mining (SDM 09), pp.97-108, 2009.
32. Travel-time prediction using Gaussian process regression : A trajectory-based approach
Tsuyoshi Idé and Sei Kato
Proceedings of 2009 SIAM International Conference on Data Mining (SDM 09), pp.1185-1196, 2009.
33. Semi-supervised local Fisher discriminant analysis for dimensionality reduction
Masashi Sugiyama, Tsuyoshi Idé, Shinichi Nakajima, and Jun Sese
Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 08),
pp.333-344, 2008.
34. Unsupervised change analysis using supervised learning
Shohei Hido, Tsuyoshi Idé, Hisashi Kashima, Harunobu Kubo, and Hirofumi Matsuzawa
Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 08),
pp.148-159, 2008.
35. Computing correlation anomaly scores using stochastic nearest neighbors
Tsuyoshi Idé, Spiros Papadimitriou, and Michail Vlachos
Proceedings of the Seventh IEEE International Conference on Data Mining (ICDM 07), pp.523-528,
2007.
36. Change-point detection using Krylov subspace learning
Tsuyoshi Idé and Koji Tsuda
Proceedings of 2007 SIAM International Conference on Data Mining (SDM 07), pp.515-520, 2007.
37. Why does subsequence time-series clustering produce sine waves ?
Tsuyoshi Idé

Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in
Databases (PKDD 06), pp.311-322, 2006.
38. Network-based problem detection for distributed systems
H. Kashima, T. Tsumura, Tsuyoshi Idé, T. Nogayama, R. Hirade, H. Etoh, and T. Fukuda
Proceedings of the 21st International Conference on Data Engineering (ICDE 2005), pp.978-989, 2005.
39. Pairwise symmetry decomposition method for generalized covariance analysis
Tsuyoshi Idé
Proceedings of the Fifth IEEE International Conference in Data Mining (ICDM 05), pp.657-660, 2005.
40. Knowledge discovery from heterogeneous dynamic systems using change-point correlations
Tsuyoshi Idé and Keisuke Inoue
Proceedings of 2005 SIAM International Conference on Data Mining (SDM 05), pp.571-575, 2005.
41. Eigenspace-based anomaly detection in computer systems
Tsuyoshi Idé and Hisashi Kashima
Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD04), pp.440-449, 2004.
42. Moiré-free collimating light guide with low-discrepancy dot patterns
Tsuyoshi Idé, H. Numata, H. Mizuta, Y. Taira, M. Suzuki, M. Noguchi, and Y. Katsu
Digest of Technical Papers of Society for Information Display 2002 (SID 02), pp.1232-1235, 2002.

Publications : Journal (refereed)
1. Diagnostic Spatio-temporal Transformer with Faithful Encoding
Jokin Labaien, Tsuyoshi Idé, Ekhi Zugasti, Xabier De Carlos
Knowledge-Based Systems, Vol. 274 (110639), 2023.
2. Signal and noise extraction from analog memory elements for neuromorphic computing
N. Gong, Tsuyoshi Idé, S. Kim, I. Boybat, A. Sebastian, V. Narayanan, and T. AndoNature Communications, Vol. 9, pp.2102, 2018.
3. City-wide traffic flow estimation from limited number of low quality cameras
Tsuyoshi Idé, Tetsuro Morimura Takayuki Katsuki, and Robert Morris
IEEE Transactions on Intelligent Transportation Systems, Vol. 18, No. 4, pp.950-959, 2017.
4. Supervised item response models for informative prediction
Tsuyoshi Idé and Amit Dhurandhar
Knowledge and Information Systems, 51 :235-257, 2017.
5. Toward simulating entire cities with behavioral models of traffic
Takayuki Osogami, Takaashi Imamichi, Hideyuki Mizuta, and Tsuyoshi Idé
IBM Journal of Research and Development, 57 :6 :1-6 :10, 2013.
6. Vehicle near-miss situation prediction from probe-car data using statistical machine learning
Tetsuro Morimura, Yusuke Tanizawa, Shinya Yamasaki, and Tsuyoshi Idé
Journal of Information Processing, 43 :573-578, 2012.
7. Modeling patent quality : A system for large-scale patentability analysis using text mining
Shohei Hido, Shoko Suzuki, Risa Nishiyama, Takashi Imamichi, Rikiya Takahashi, Tetsuya Nasukawa,
Tsuyoshi Idé, Yusuke Kanehira, Rinju Yohda, Takeshi Ueno, Akira Tajima, and Toshiya Watanabe
Journal of Information Processing, 20 :667-671, 2012.
8. Trajectory regression for travel-time prediction
Tsuyoshi Idé and Sei KatoTransactions of the Japanese Society for Artificial Intelligence, 25 :377-382, 2010.
9. Unsupervised change analysis using supervised learning
H. Matsuzawa, S. Hido, Tsuyoshi Idé, and H. Kashima
The IEICE Transactions on Information and Systems, E93-D :816-825, 2010.
10. Semi-supervised local Fisher discriminant analysis for dimensionality reduction
M. Sugiyama, Tsuyoshi Idé, S. Nakajima, and J. Sese
Machine Learning, 78 :35-61, 2010.

11. Recent advances and trends in large-scale kernel methods
H. Kashima, Tsuyoshi Idé, T. Kato, and M. Sugiyama
IEICE Transactions on Information and Systems, E92-D :1338-1353, 2009.
12. Network-based problem detection for distributed systems
H. Kashima, T. Tsumura, Tsuyoshi Idé, T. Nogayama, R. Hirade, H. Etoh, and T. Fukuda
IEICE Transactions on Information and Systems, J89-D :183-198, 2006.
13. Dot pattern generation technique using molecular dynamics
Tsuyoshi Idé, H. Mizuta, H. Numata, Y. Taira, M. Suzuki, M. Noguchi, and Y. Katsu
Journal of the Optical Society of America A20 :242-255, 2003.
14. A novel dot-pattern generation to improve luminance uniformity of an LCD backlight
Tsuyoshi Idé, H. Numata, H. Mizuta, Y. Taira, M. Suzuki, M. Noguchi, and Y. Katsu
Journal of the Society for Information Display, 11 :659-665, 2003.
15. Nonlocal screening effect in Cu 4p-1s resonant X-ray emission spectra of Nd2 CuO4
Tsuyoshi Idé and Akio Kotani
Journal of the Physical Society of Japan, 69 :3107-3114, 2000.
16. Interplay between raman and uorescence-like components in resonant X-ray emission spectraof degenerate d0 and d1 systems
Tsuyoshi Idé and Akio Kotani
Journal of the Physical Society of Japan, 69 :1895-1906, 2000.
17. Polarization and momentum dependence of a charge-transfer excitation in Nd2 CuO4
K. Hämäläinen, J. P. Hill, S. Huotari, C. C. Kao, L. E. Berman, A. Kotani, Tsuyoshi Idé, J. L. Peng,and R. L. Greene
Physical Review, B61 :1836-1840, 2000.
18. Local and nonlocal excitations in Cu 4p-1s resonant X-ray emission spectra of Nd2 CuO4
Tsuyoshi Idé and Akio Kotani
Journal of the Physical Society of Japan, 68 :3100-3109, 1999.
19. Theoretical study on cluster size effects on X-ray absorption and resonant X-ray emission
spectra in d and f electron systems
Akio Kotani and Tsuyoshi Idé
Journal of Synchrotron Radiation, 6 : 208-309, 1998.
20. A model study on cluster size effects of resonant X-ray emission spectra
Tsuyoshi Idé and Akio Kotani
Journal of the Physical Society of Japan, 67 :3621-3629, 1998.

Publications : Book
Author
1. Introduction to Anomaly Detection using Machine Learning — A Practical Guide with R
Tsuyoshi Idé, Corona Publishing (in Japanese), 2015.
2. Anomaly Detection and Change Detection
Tsuyoshi Idé and Masashi Sugiyama, Kodansha (in Japanese), 2015.

Translation
1. Computer Age Statistical Inference
H. Fujisawa, Tsuyoshi Idé, Kyoritsu, 2020 (Japanese translation of the book of the same title by Bradley
Efron and Trevor Hastie, Cambridge University Press, 2016).
2. The Elements of Statistical Learning
M. Sugiyama, Tsuyoshi Idé, T. Kamishima, T. Kurita, and E. Maeda, Kyoritsu, 2014 (Japanese translationof the book of the same title by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie, Springer,2001).
3. Pattern Recognition and Machine Learning
H. Motoda, et al., Maruzen, 2012, Chap. 12 (Japanese translation of the book of the same title by
Christopher Bishop, Springer, 2006).

Chapter contributions
1. Decentralized Collaborative Learning and Risk Management
Tsuyoshi Idé, in From Anomaly Detection to Risk Management, Saiensu-Sha, Chap. 4, 2022.
2. Making relationships simple – A story of graphical lasso
Tsuyoshi Idé, in Iwanami Data Science, Vol.5, pp.48-63, 2016.
3. Continuous Latent Variables
Tsuyoshi Idé, in Pattern Recognition and Machine Learning, H. Motoda, T. Kurita, T. Higuchi, Y.
Matsumoto, and N. Murata, ed., Maruzen (in Japanese), Chap.12, 2014.
4. Change detection from heterogeneous data sources
Tsuyoshi Idé, in Pattern Data Mining for Service, K. Yada, ed., Springer, pp.221-243, 2014.

Patents
I have 50+ granted patents in the US, Japan, Taiwan, China, and other countries.