Welcome to the LB_Keogh homepage!

  • LB_Keogh makes retrieval of time-warped time series feasible even for large data sets. Muller et. al. SIGGRAPH 05.

  • LB_Keogh is fast, because it cleverly exploits global constraints that appear in dynamic programming. Christos Faloutsos et al. PODS 2005

  • LB_Keogh, the best-so-far known method to lower bound DTW.. Capitani and Ciaccia SEBD05.

  • The powerful lower-bounding method LB Keogh for efficient time series matching...  Papapetrou et al TODS 2011

  • Due to its ease of implementation and relative computational efficiency (when optimized) we chose to use DTW. For more information on the particular methods used in MAGIC, see (LB_Keogh) Ashbrook and Starner CHI 2010

  • The best lowerbounding method for constrained DTW distance and fixed-length time series so far is considered LB Keogh. Bartos & Skopal 2012

  • (LB_Keogh) makes DTW indexable by approximating time series with bounding envelopes. Lian et. al. TKDE 2009

  • The sliding window approach is speeded up by the LB Keogh lower-bounding method, often by orders of magnitude.  Lijffijt et al PETRA 2010

  • Although DTW has a time complexity of O(n2), (LB_Keogh) bounding can effectively make DTW run in O(n). Smith and Craven csb2008

  • The standard DTW algorithm has a quadratic time and space complexity. For performance reason we implemented a FastDTW based on the ideas of LB_Keogh..and therefore the algorithm performs nearly in linear time. Juffinger, Granitzer and Lex WWW 2009

  • The LB_Keogh method speeds up the sliding window approach, often by orders of magnitude, by computing an efficient lower bound of the matching cost.. Panagiotis Papapetrou 2009.

  • LB_Keogh  can significantly speed up DTW. Suzuki et al. ICML 2003.

  • We use LB_Keogh to speed up the algorithm.. Xu et al. 2011

  • In order to speed-up the calculations, we used the LB Keogh lowerbounding technique. Buza et al PAKDD 2011

  • Keogh et al. and showed that their tight lower bound LB_Keogh could also be used in rotation-invariant image matching and provided a novel solution for the DTW distance as well as the Euclidean distance. Yang-Sae Moon et al 2010

  • (for) the problem of searching for similar acoustic data over unstructured decentralised P2P networks LB_Keogh can be effectively used for pruning, resulting in considerably less number of DTW computations. Karydis et. al. ICEIS05

  • Thus in this paper we use anenvelope-based lower bound proposed by Keogh (LB_Keogh) to improve the performance of rotation-invariant image matching. Kim et al DEXA 2011

  • by exploiting recent results on metric access structures and LB_Keogh, we can still guarantee the indexability of DFT coefficients extracted from large data sets. Bartolini et. al. IEEE PAMI.

  • LB_Keogh is preferred (for motion capture) since it offers the tightest lower bounds, is readily indexible with MBRs. Marc Cardle University of Cambridge Thesis

  • Keogh has pioneered many of the recent ideas in the indexing of dynamic time warping". Cole, Shasha, and Zhao SIGKDD 2005.

  • LB_Keogh performs uniformly the best. Chen and Ng. VLDB 2005.

  • Among the existing lower bound functions, LB_Keogh is the best in terms of the tightness of the lower bound. Zhou, M. and Wong, Information Sciences 2005

  • Despite the fact that DTW distance still does not satisfy the triangle inequality, LB_Keogh exactly index(s) the DTW distance. Wang, Z.
    LB_Keogh has provided a convincing lower bound that can be tuned to provide high tightness to the actual DTW distance.
    Toni Rath

  • We exploit (LB_Keogh) in the context of reverse engineering of dynamic feature behavior to detect similarities between feature traces. This technique is ideally suited to handling large amounts of data. Kuhn and Greevy

  • Recently, Keogh et al. presented an algorithm, based on the LB_Keogh function, which dramatically reduced the time complexity of the calculation of the Euclidean Distance measure. This speed up was further achieved by allowing indexing. Frentzos et al. ICDE 2007

  • Exact indexing of DTW has been proposed in the literature,.. the LB_Keogh. Employing the indexing method allows us to reduce the computation (needed for query by humming). Roger Jang, PCM 2006.

  • To reduce the computational time and (quadratic) complexity inherent in dynamic time warping, we use (LB_)Keogh minimum bounds to quickly determine candidates for the set of nearest neighbors. Dalal and Olson. SPECTS07

  • (we use) LB_Keogh for efficient similarity range query processing in music databases. Ruxanda & Jensen 2006

  • LB_Keogh.. has become a popular solution for indexing DTW because of its performance. Zhou &Wong ICDE 2007

  • To speed up computations we could utilize spatiotemporal access methods similar to LB_Keogh" Zeinalipour Yazti , Lin and Gunopulos CIKM 2006

  • EDR incurs huge computational cost due to the lack of pruning techniques such as LB_Keogh lower bound Chen et al. ICDE07.

  • Since DTW is relatively slow to calculate... We therefore use (LB_)Keogh minimum bounds. Dalal, Musicant, Olson, McMenamy, Benzaid, Kazez and Bolan. icc2007.

  • Because DTW is not a metric, we use LB_Keogh to make indexing possible (for our query by humming system) Leung Tat-wan. 2005.

  • In order to use the SBR representation in a multidimensional index, we must have a distance function that lower bounds the distance between a query object and a group of time series data. Therefore we can use LB_Keogh.. Li, Lopez and Moon TKDE 2004

  • Keogh proposed LB Keogh and LB PAA, tight lower bounds under time-warping ... our solutions exploit these two lower bounds.. Han et al VLDB 2007

  • (LB_Keogh) significantly speeds up the DTW (for music similarity). Joan Serra 2007. Master Thesis UPF. Barcelona

  • For fault diagnosis (LB_keogh) substantially reduce the computational expense required. Rajshekhar, et. al 2007

  • (we use) the tightest existing lower bound.. (for) exact indexing of Dynamic Time Warping, LBKeogh. Assent, Krieger, Afschari and Seidl EDBT 2008

  • (LB_Keogh) has significantly increased the accuracy of time series classification while reducing the computational expense required. Kumar, Gupta, Jayaraman & Kulkarni 2008

  • Exact indexing of DTW has been proposed in the literature.. using LB_Keogh.. Employing the indexing method allows us to reduce the computation (of query by humming). Jang and Lee 2008

  • the best lowerbound function in terms of tightness is the LB_Keogh... It cleverly exploits the warping window.. Zhou and Wong ICDE08

  • we describe how to further reduce matching time using a lower bound function based on LB_Keogh. Yoon-Sik Tak and Eenjun Hwang 2008

  • we use LB Keogh to obtain of two orders of magnitude over the brute force algorithm when doing fast correlation analysis.. Nguyen and Shiri CIKM 08

  • showed that their tight lower bound LB Keogh could also be used in rotation-invariant image matching and provided a novel solution for the DTW distance. Their solution is excellent.. Kim et all DEXA08

  • The disadvantage of DTW is its quadratic time complexity but we address this issue by using (LB_Keogh) lower bound based pruning that provides approximately linear time complexity. Chandola, Cheboli, and Vipin Kumar 2009

  • the whole process of finding the nearest neighbours can be sped up by using a lower-bounding function called LB Keogh that can be computed in linear time. T. Knorr et al. 2009.

  • LB_Keogh substantially reduce the computational expense required.  Rajshekhar et al 2007

  • To reduce the computational time and (quadratic) complexity inherent in DTW, we apply Keogh minimum bounds, to quickly determine candidates for the set of nearest neighbors. Dalal , Kawaler, and Tucker 2009

  • a simple but nearly optimal way to compute a lower bound to the DTW between any two times series, called LB_Keogh.. Daniel Lemire 2008

  • Since we use the DTW as our matching method, we can use the LB_Keogh distance.. Kim &  Tak & Hwang 2009

  • (for )handling complex video histograms (we us) LBKeogh, a DTW filter technique. Assent, Kremer, Seidl . DASFAA 2010

  • We used the LB_Keogh function as in [10] as it has been proven to guarantee no false dismissals and returns results close to the real distance function. IJCAI09  Heras et al

  • The tightest lower bound of DTW, between a query envelope E(Q) and a data sequence S, is known as LBKeogh Han et al SIGMOD 2011

  • To our best knowledge, LB_Keogh is the best lower bounding technique for DTW approaches.. Volny, Novak and Zezul 2011.

  • we hierarchically used several low bound functions, such as Fourier_Dist, MINDIST and LB_Keogh (to reterive 3D shapes). Tak and Hwang MMEDIA 2011

  • More advanced scaling techniques include lower-bounding, like LB Keogh. Buza,et al.  Healthcare Informatics, Imaging and Systems Biology 2011

  • However, the computation of the MSE becomes efficient for large databases of shape descriptors using the lower bound and early abandon criteria suggested by Keogh. Huber-Mork et al. MVA 2011

  • We chose LBKeogh as the basis of our MultiDTW.. Kremer, Gunnemann,  Ivanescu, Assent and Seidl. SSDBM 2011

What is LB_Keogh?

LB_Keogh is tool for lower bounding various time series distance measures. It was introduced in 2002 as the first non trivial lower bound for Dynamic Time Warping (DTW), and it is still the fastest known technique for indexing DTW (see [9]). It can also be used to index under uniform scaling, various other distortions, and it can be used for efficient processing of streaming time series [8] and for indexing shapes [12].

To the right is a visual intuition of LB_Keogh, a protective envelope is built around the red time series, the Euclidean distance between the blue time series and the closest part of the protective envelope is a (tight) lower bound to the DTW. For indexing under uniform scaling or processing of streaming time series etc,  the definition of the envelope  differs, but the LB_Keogh definition is unchanged.

Note that LB_Keogh takes one line of code in matlab! Suppose you have a query Q and you have created U and L according to the problem you are interested in (DTW, uniform scaling etc).

U = (eq 6 of VLDB02 paper for DTW, eq 6 of VLDB04 paper for uniform scaling, or..)
L = (eq 7 of VLDB03 paper for DTW, eq 7 of VLDB04 paper for uniform scaling, or..)
Q = (any time series query)
Then LB_Keogh can be expressed in the following single line of matlab...

    LB_Keogh = sqrt(sum([[Q > U].* [Q-U]; [Q < L].* [L-Q]].^2));


 Videos of LB_Keogh for uniform scaling (in motion capture data)

 Video of LB_Keogh for DTW (in motion capture data)

  Images of LB_Keogh for shape indexing

Note that the entire development of LB_Keogh was  made possible by funding from an NSF Career Award 0237918.



Some papers by Keogh and collaborators that use LB_Keogh. (in random order) 

In [1]  I introduced LB_Keogh and showed how it could be used for exact indexing of DTW. In [2] I showed how LB_Keogh could be used for exact indexing of uniform scaling. In [3] we consider both  DTW and uniform scaling at the same time. In [4] we showed how to use LB_Keogh on a bit level represention of time series. Paper [5] extends LB_Keogh to mulit-dimensional time series. In [6] we consider applications to motion capture problems. In [7] we show how use LB_Keogh to improve classification accuracy. In paper [7] we see the first paper on using LB_Keogh for processing streams. Many people have tried to beat LB_Keogh, in paper [7] we show with 8 billion experiments that this is not possible. Paper [10] considers the importance of uniform scaling in motion capture problems, and shows how LB_Keogh can efficiently address the problem. Paper [11] shows how to do fast classification of time series using numerosity reduction, we use LB_Keogh to make this idea tractable.  Paper [12] shows that LB_Keogh can be used to index shapes!

  1. Keogh, E. (2002). Exact indexing of dynamic time warping. In 28th International Conference on Very Large Data Bases. Hong Kong. pp 406-417. [Conference version pdf , Journal version pdf , slides ]. Also a KAIS journal paper.

  2. Keogh, E. (2003).  Efficiently Finding Arbitrarily Scaled Patterns in Massive Time Series Databases. PKDD 2003: 253-265

  3. Ada Wai-chee Fu, Eamonn Keogh, Leo Yung Hang Lau and Chotirat Ann Ratanamahatana (2005). Scaling and Time Warping in Time Series Querying.  VLDB 2005. [pdf

  4. Ratanamahatana, C., Keogh, E., Bagnall, T.  and Lonardi, S. (2005). A Novel Bit Level Time Series Representation with Implications for Similarity Search and Clustering. PAKDD 05. [pdf ]

  5. Vlachos, M., Hadjieleftheriou, M., Gunopulos, D. & Keogh. E. (2003). Indexing Multi-Dimensional Time-Series with Support for Multiple Distance Measures. In the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. August 24 - 27, 2003. Washington, DC, USA. pp 216-225. [ pdf]

  6. M. Cardle, M. Vlachos, S. Brooks, E. Keogh, D. Gunopulos (2003). Fast Motion Capture Matching with Replicated Motion Editing. In Proc. of SIGGRAPH 2003, San Diego, Technical Sketches & Applications July 27-31, 2003. San Diego, CA. USA.  [ pdf]

  7. Ratanamahatana, C. A. and Keogh. E.  (2004). Making Time-series Classification More Accurate Using Learned Constraints. In proceedings of SIAM International Conference on Data Mining (SDM '04), Lake Buena Vista, Florida, April 22-24, 2004. pp. 11-22. [pdf, slides]

  8. L. Wei, E. Keogh, H. Van Herle, and A. Mafra-Neto (2005). Atomic Wedgie: Efficient Query Filtering for Streaming Time Series. In Proc. of the 5th IEEE International Conference on Data Mining (ICDM 2005), pp. 490-497, Houston, Texas, Nov 27-30, 2005. [pdf] [slides].

  9. Ratanamahatana, C. A. and Keogh. E. (2005). Three Myths about  Dynamic Time Warping. In proceedings of SIAM International Conference on Data Mining (SDM '05), Newport Beach, CA, April 21-23,  pp. 506-510  [pdf , slides].   Also appeared as a workshop paper with the following unlikely title...Ratanamahatana, C. A. and Keogh. E. (2004). Everything you know about Dynamic Time Warping is Wrong. Third Workshop on Mining Temporal and Sequential Data, in conjunction with the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004), August 22-25, 2004 - Seattle, WA. [pdf , slides 

  10. Keogh, E., Palpanas, T., Zordan, V., Gunopulos, D. and Cardle, M. (2004) Indexing Large Human-Motion Databases. In proceedings of the 30th International Conference on Very Large Data Bases, Toronto, Canada. [pdf, slides ]

  11. Xiaopeng Xi, Eamonn Keogh, Christian Shelton, Li Wei & Chotirat Ann Ratanamahatana (2006). Fast Time Series Classification Using Numerosity Reduction. ICML. [pdf]

  12. Eamonn Keogh, Li Wei, Xiaopeng Xi, Sang-Hee Lee and Michail Vlachos  (2006) LB_Keogh Supports Exact Indexing of Shapes under Rotation Invariance with Arbitrary Representations and Distance Measures. VLDB 2006 [pdf]

  13. Hui Ding, Goce Trajcevski, Peter Scheuermann, Xiaoyue Wang and Eamonn Keogh (2008) Querying and Mining of Time Series Data: Experimental Comparison of Representations and Distance Measures VLDB 2008 [pdf]

  14. Xiaoyue Wang, Abdullah Mueen, Hui Ding, Goce Trajcevski, Peter Scheuermann and Eamonn Keogh (2012) Experimental comparison of representation methods and distance measures for time series data. DATA MINING AND KNOWLEDGE DISCOVERY

  15. Thanawin Rakthanmanon, Bilson Campana, Abdullah Mueen, Gustavo Batista, Brandon Westover, Qiang Zhu, Jesin Zakaria, Eamonn Keogh (2012). Searching and Mining Trillions of Time Series Subsequences under Dynamic Time Warping SIGKDD 2012. Best paper award


Some papers by other researchers that use LB_Keogh. (in no particular order)  (I have stopped maintaining this section of the page, it is too hard to keep up)

In [A][C]  LB_Keogh is used for handwriting retrieval. In [B] LB_Keogh is used for music retrieval. In [D] LB_Keogh is used for subsequence matching. In [E] LB_Keogh is used for indexing under Euclidean distance. Paper [F] considers LB_Keogh for rotation invariance indexing. Paper [G] uses APCA and LB_Keogh for DTW (but see [9] above). Paper [H] uses LB_Keogh for indexing images. Paper [I] uses LB_Keogh for oil well mining (in both senses of "mining").  Paper [J] [L] uses LB_Keogh for peer to peer music retrieval . Paper [K] also uses APCA and LB_Keogh for DTW (but see [9] above). Paper [M] is yet another work that uses APCA and LB_Keogh for DTW (but see [9] above).  In paper [N] the authors adapt LB_Keogh for monitoring streams. Paper [O] uses DTW to index 3D shapes and note "..an advantage of this model is that has been proved that Dynamic Time Warping technique can be indexed with LB_Keogh". Paper [P] uses LB_Keogh to index dance notation. Paper [Q] uses LB_Keogh to explore software traces. [R] uses a modified LB_Keogh for music indexing. Paper[S] also uses a modified LB_Keogh for music indexing. Paper [T] uses the LB_Keogh for indexing shapes. Paper [U] uses the notation of nested wedges (see Atomic Wedgie) to index XML doucuments! Paper [V] is yet another paper that uses LB_Keogh for indexing music. Papers [W] and [X] use the LB_Keogh for Predicting User-Perceived Quality Ratings from Streaming Media Data. Paper [y] uses LB_Keogh to do something (I am honestly not sure what!) Paper [z] uses LB_Keogh to support range queries in time series. Paper [AA] uses LB_keogh for online fault diagnosis .Paper [AB] uses LB_Keogh for shape indexing. Paper [AC] uses the LB_Keogh to make a medical data mining task tractable.  Paper [AE] uses LB_Keogh for predicting video stream quality from partial stream information. Paper [AF] uses LB_Keogh for indexing 3D shapes. Paper [AG] uses LB_Keogh for Speeding up Complex Video Copy Detection Queries. Paper [AH] uses LB_Keogh for Fast Detection of Suspicious Stream Patterns. Paper [HI] uses LB_Keogh for gesture recognition. Paper [AJ] uses LB_Keogh for shape indexing. Paper [AK] uses LB_Keogh for anomaly detection. Paper [AL] uses LB_Keogh to support fast approximate searching. Paper [KM] has an interesting twist on LB_Keogh, they only use the upper envelope. They note "To prove that L(Q, S) ≤ DTW(Q, S) for posteriorgram sequences Q and S, we follow the strategies that are used in (Keogh)". Paper [AN] does something similar to Atomic Wedgie. Paper [AO] used LB_Keogh and LB_PAA to do something. [AP] use LB_Keogh for speech indexing

  1. T. M. Rath and R. Manmatha (2002): Lower-Bounding of Dynamic Time Warping Distances for Multivariate Time Series. Technical Report MM-40, Center for Intelligent Information Retrieval, University of Massachusetts Amherst.
  2. Yunyue Zhu, Dennis Shasha (2003). Query by Humming: a Time Series Database Approach, SIGMOD 2003.
  3. Manmatha, R.and Rath, T.M., "Indexing Handwritten Historical Documents - Recent Progress," to appear in the Proc. of the Symposium on Document Image Understanding (SDIUT'03).
  4. Wong Siu Fung and Man Hin Wong (2003). Efficient Subsequence Matching for Sequences Databases under Time Warping . The 7th International Database Engineering and Application Symposium Hong Kong
  5. Skyline Index for Time Series Data (2004). Quanzhong Li, Ines Fernando Vega Lopez and Bongki Moon, to appear in IEEE Transactions on Knowledge and Data Engineering
  6. M. Vlachos, D. Gunopulos, G. Das: "Rotation Invariant Distance Measures for Trajectories", In Proc. of 10th International Conf. on Knowledge Discovery & Data Mining (SIGKDD), Seattle, WA, 2004
  7. Yutau Shou, Nikos Mamoulis and David W. Cheung. (2005). Fast and exact warping of time series using adaptive segmental approximations. Machine Learning.
  8. Ilaria Bartolini, Paolo Ciaccia, Marco Patella  (2005). WARP: Accurate Retrieval of Shapes Using Phase of Fourier Descriptors and Time Warping Distance. IEEE PAMI Vol. 27, No. 1
  9. Steven Zoraster, Ramoj Paruchuri, and Steve Darby (2004). Curve Alignment for Well-to-Well Log Correlation. SPE Annual Technical Conference and Exhibition held in Houston, Texas, U.S.A., 26-29 September 2004.
  10. Y. Karydis, A. Nanopoulos, A.N. Papadopoulos and Y. Manolopoulos: "Music Retrieval in P2P Networks Under the Warping Distance", Proceedings of the 7th International Conference on Enterprise Information Systems (ICEIS 2005) , Miami, FL, USA, 24-28 May, 2005.
  11. Satoshi OOMOMO, Hanxiong CHEN, Kazutaka FURUSE, and Nobuo OHBO (2005) Efficient Search of Similar Time Series under Time Warping with Dimensionality Reduction. DEWS05
  12. Y. Karydis, A. Nanopoulos, A.N. Papadopoulos and Y. Manolopoulos: "Evaluation of Similarity Searching Methods for Music Data in Peer-to-Peer Networks", International Journal of Business Intelligence and Data Mining , 2005
  13. Yasushi Sakurai, Masatoshi Yoshikawa, Christos Faloutsos: FTW: Fast Similarity Search under the Time Warping Distance. PODS 2005
  14. Paolo Capitani, Paolo Ciaccia: Efficiently and Accurately Comparing Real-valued Data Streams. SEBD 2005: 161-168
  15. A. Angeles-Yreta, J. Figueroa-Nazuno: Computing Similarity Among 3D Objects Using Dynamic Time Warping. CIARP 2005: 319-326
  16. Tao Yu, Xiaojie Shen, Qilei Li, Weidong Geng (2005) Motion retrieval based on movement notation language. Computer Animation and Virtual Worlds Volume 16, Issue 3-4 , Pages 273 - 282
  17. Adrian Kuhn and Orla Greevy, Exploiting the Analogy Between Traces and Signal Processing, Proceedings of International Conference on Software Maintenance (ICSM 2006), IEEE Computer Society Press, September 2006.
  18. Hong-Ru Lee Ching Chen Jang, J.S.R. (2005). Approximate lower-bounding functions for the speedup of DTW for melody recognition
  19. Maria M. Ruxanda Christian S. Jensen (2006) Effcient Similarity Retrieval in Music Databases COMAD 2006
  20. Selina Chu 1 , Shrikanth Narayanan 1,2 , and C.-C. Jay Kuo  (2006) Efficient Rotation Invariant Retrieval of Shapes using Dynamic Time Warping with Applications in Medical Databases. CBMS 2006
  21. Mirella M. Moro, Petko Bakalov, Vassilis J. Tsotras (2007) Early Profile Pruning on XMLaware Publish/Subscribe Systems. VLDB 2007
  22. Leung Tat-wan. 2005. Polyphonic Song Analysis and Representation for Query by Humming Systems.
  23. A. Csizmar Dalal and J. Olson. "Feature Selection for Prediction of User-Perceived Streaming Media Quality." In Proceedings of the 2007 International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS).
  24. A. Csizmar Dalal, D. Musicant, J. Olson, B. McMenamy, S. Benzaid, B. Kazez, E. Bolan. "Predicting User-Perceived Quality Ratings from Streaming Media Data". In Proceedings of the IEEE International Conference on Communications (ICC 2007), Glasgow, Scotland, June 2007. Han, W.,
  25. Lee, J., Moon, Y., and Jiang, H., "Ranked Subsequence Matching in Time-series Databases," In Proc. 33rd Very Large Data Bases(VLDB), Vienna, Austria, Sept. 2007
  26. Jinsoo Lee, Wook-Shin Han, Yang-Sae Moon, and Wooseong Kwak, "Range Search under Dynamic Time Warping," In /Proc. of the 7th Int  Conf. on Applications and Principles of Information Science/ (/APIS2008/), Auckland, New Zealand, pp. 67-70, Jan. 2008
  27. Rajshekhar , Ankur Gupta, A. N. Samanta, B. D. Kulkarni1 V. K. Jayaraman (2007)  Fault Diagnosis Using Dynamic Time Warping. Pattern Recognition and Machine Intelligence.
  28. Pruning and Matching Scheme for Rotation Invariant Leaf Image Retrieval. Yoon-Sik Tak and Eenjun Hwang (2008) KSII
  29. Identifying Patients at Risk: Mining Dialysis Treatment Data, (2009) T. Knorr, L. Schmidt-Thieme, and C. Johner
  30. Andreas Juffinger, Michael Granitzer, Elisabeth Lex: Blog credibility ranking by exploiting verified content. WICOW 2009: 51-58
  31. A. Csizmar Dalal, E. Kawaler, S. Tucker. "Towards Real-Time Stream Quality Prediction: Predicting Video Stream Quality from Partial Stream Information". In Proceedings of The Sixth International ICST Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness (QShine), Las Palmas de Gran Canaria, Spain, November 2009.
  32. Shape-based indexing scheme for camera view invariant 3-D object retrieval Multimedia Tools and Applications by Hyoung Kim, Yoon-Sik Tak, Eenjun Hwang
  33. Assent I.,Kremer H., Seidl T.: Speeding up Complex Video Copy Detection Queries, Proc. of the 15th International Conference on Database Systems for Advanced Applications (DASFAA 2010), Tsukuba.
  34. Assent I.,Kremer H., Gunnemann S., Seidl T.: Pattern Detector: Fast Detection of Suspicious Stream Patterns for Immediate Reaction Proc. International Conference on Extending Database Technology (EDBT/ICDT 2010, ), Lausanne, Switzerland
  35. Daniel Ashbrook and Thad Starner. MAGIC: A Motion Gesture Design Tool. In Proceedings of SIGCHI conference on Human Factors in Computing Systems (CHI), Atlanta, GA, 2009, 10 pages.
  36. Yang-Sae Moon,  Bum-Soo Kim, Min Soo Kim, and Kyu-Young (2010)  Scaling-invariant boundary image matching using time-series matching techniques. Data & Knowledge Engineering.
  38. Approximate Indexing of Dynamic Time Warping. (2011) Romain Tavenard, Laurent Amsaleg
  40. Efficient Processing of Multiple DTW Queries in Time Series, Hardy Kremer, Stephan Gunnemann, Anca-Maria Ivanescu, Ira Assent and Thomas Seidl. SSDBM 2011
  41. A New Approach for Processing Ranked Subsequence Matching Based on Ranked Union. Han et al SIGMOD 2011
  42. A Piecewise Aggregate Approximation (PAA) Lower-Bound Estimate for Posteriorgram-based Dynamic Time Warping., Zhang and Glass, Interspeech 2011.
  43. 3D Object Retrieval and Pose Estimation for a Single-view Query Image in a Mobile Environment, Yoon-Sik Tak and Eenjun Hwang. MMedia 2011