Selected Publications

If you have any problems viewing the files just let me know, and I will send you a copy. Email me at eamonn@cs.ucr.edu

My publications on DBLP are here, Google scholar is has this on me. My H-index is 31

My favorite venues: I have 12 ICDM papers, 12 SIGKDD papers, 6 VLDB papers, 8 SDM papers, 6 KAIS papers, 5 Data Mining and Knowledge Discovery papers, 5 PKDD....

Best paper awards: SIGMOD, KDD and ICDM.

 

Shashwati Kasetty, Candice Stafford, Gregory P. Walker, Xiaoyue Wang, Eamonn Keogh (2008). Real-Time Classification of Streaming Sensor Data. 20th IEEE Int'l Conference on Tools with Artificial Intelligence. [pdf]

Jin Shieh and Eamonn Keogh (2008) iSAX: Indexing and Mining Terabyte Sized Time Series. SIGKDD 2008.  [pdf] The iSAX page is here. [slides]

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][slides]

Xiaoyue Wang, Lexiang Ye, Eamonn Keogh and Christian Shelton. (2008). Annotating Historical Archives of Images. JCDL 2008 [pdf]

Lexiang Ye, Xiaoyue Wang, Dragomir Yankov and Eamonn Keogh (2008). The Asymmetric Approximate Anytime Join: A New Primitive with Applications to Data Mining. SDM 2008. [pdf] Datasets are here.

Dragomir Yankov, Eamonn Keogh, and Umaa Rebbapragada (2007). Disk Aware Discord Discovery: Finding Unusual Time Series in Terabyte Sized Datasets. ICDM 2007. [pdf] Best paper award.. Data and code is here: The powerpoint slides are here, but you should also download the movie and put it it the same folder as the talk. Expanded Journal Version. [pdf]

Dragomir Yankov, Eamonn Keogh, and Kin Kan (2007). Locally Constrained Support Vector Clustering. ICDM 2007. [pdf]

Dragomir Yankov, Eamonn Keogh, Jose Medina, Bill Chiu, and Victor Zordan (2007). Detecting Motifs Under Uniform Scaling. SIGKDD 2007. [pdf] [Powerpoint] Supporting webpage with video and datasets.

Xiaopeng Xi, Eamonn Keogh, Li Wei, Agenor Mafra-Neto (2007). Finding Motifs in Database of Shapes. SIAM International Conference on Data Mining (SDM'07). [pdf]

Dragomir Yankov, Eamonn Keogh, Li Wei, Xiaopeng Xi and Wendy Hodges (2007). Fast Best-Match Shape Searching in Rotation Invariant Metric Spaces. SIAM International Conference on Data Mining (SDM'07). [pdf] Now a  IEEE Trans Multimedia paper

Ken Ueno, Xiaopeng Xi, Eamonn Keogh, Dah-Jye Lee (2006). Anytime Classification Using the Nearest Neighbor Algorithm with Applications to Stream Mining. ICDM  2006. [pdf] [Powerpoint] Accepted to  PAA Journal.

Dragomir Yankov and Eamonn Keogh. (2006). Manifold Clustering of Shapes ICDM  2006. [pdf] [Powerpoint]

Eamonn Keogh, Li Wei, Xiaopeng Xi, Stefano Lonardi, Jin Shieh, Scott Sirowy  (2006). Intelligent Icons: Integrating Lite-Weight Data Mining and Visualization into GUI Operating Systems. ICDM  2006. [pdf]  [Powerpoint]

  • We've had some success in using your intelligent icon concept combined with MDL learning for intrusion detection - detection of zero day attacks. Scott C. Evans, PhD, GE Research

Li Wei, Eamonn Keogh and Xiaopeng Xi (2006) SAXually Explict Images: Finding Unusual Shapes. ICDM 2006. [Expanded version pdf] [Powerpoint] See also  HOT SAX  in ICDM 2005 below. Now a Data Mining and Knowledge Discovery  Journal paper.

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] [Powerpoint] For more information, see here. Now a VLDB Journal paper.

Li Wei and Eamonn Keogh  (2006) Semi-Supervised Time Series Classification. SIGKDD 2006. [pdf]

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

E. Keogh, J. Lin and A. Fu (2005). HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence. In Proc. of the 5th IEEE International Conference on Data Mining (ICDM 2005), pp. 226 - 233., Houston, Texas, Nov 27-30, 2005.  [pdf]. More info on discords and HOT SAX is here Also accepted to the  best of ICDM  special issue of KAIS journal.

  •  This (severe weather phenomena) rule-finding algorithm draws from the data structures for efficient detection of Discords described in Keogh et al  Amy McGovern et al. 2007

  •  Discords have great implications for fast and intelligent data mining  Ameen et al 2006.

  •   ..has been improved by designing the more intelligent Discord algorithms that can bypass most of the seemingly unnecessary parameters calculated  Basha et al 2007

  •  a seminal paper..the authors introduce the new problem of finding time series discords we confirm it can detect heartbeats anomalies  Mooi Choo 2007

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]. Also accepted to the  best of ICDM  special issue of KAIS journal.

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]  New! This paper has been accepted to the VLDB Journal.

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]  A greatly expanded version of this paper has been accepted to the Data Mining and Knowledge Discovery Journal, journal.

Kumar, N.,  Lolla  N.,  Keogh, E.,  Lonardi, S. , Ratanamahatana, C. A. and Wei, L. (2005). Time-series Bitmaps: A Practical Visualization Tool for working with Large Time Series Databases . In proceedings of SIAM International Conference on Data Mining (SDM '05), Newport Beach, CA, April 21-23. pp. 531-535 [pdf ]  The error rate for ECG (Euclidean distance) in table 3 should read 47.5, not 42.25.

  •  the time-series-bitmap algorithm gives us a tool to categorize these differences formally.  Bhatkar et al.

  •  we use SAX bitmap matrices to compute an anomaly score for acoustic signals, enabling the extraction of bird vocalizations and other acoustic events  Kasten, McKinley and Gage

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] 

Keogh, E., Lonardi, S. and Ratanamahatana, C. (2004). Towards Parameter-Free Data Mining. In proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Seattle, WA, Aug 22-25, 2004. [pdf, slides ] An expanded version of this paper appears in The Data Mining and Knowledge Discovery Journal.

  •  A feature-free approach to spam filtering, offered by CDM, has several advantages over a feature-based approach...its remarkable accuracy. A second advantage is its low set-up costs: feature extraction, selection and weighting are all unnecessary.  Delany and Bridge
  •  ...CDM compares favorably with the results from the conventional methods of Fetal heart rate (FHR) monitoring reported in International guidelines.  Santos et al.
  •  ...Keogh et. al  suggested a clever way of approximating the Kolmogorov complexity with the Lempel-Ziv compression length. .  Faloutsos and Megalooikonomou 2007.
  • ..inspired by (CDM) found to be reliable indicator of predictability  for a deployment of enterprise services.. Andrzejak et al DSOM 08
  • Through the use of the Compression-based Dissimilarity Measure CDM.. we have identified cases where .. Rost, Edsberg, Grimsmo and Nytro, 2008

Lin, J., Keogh, E., Lonardi, S., Lankford, J. P. & Nystrom, D. M. (2004). Visually Mining and Monitoring Massive Time Series. In proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Seattle, WA, Aug 22-25, 2004.
(This work also appears as a VLDB 2004 demo paper, under the title  VizTree: a Tool for Visually Mining and Monitoring Massive Time Series. ) [pdf ,slides] An expanded version of the paper appears in Information Visualization 4(2): 61-82 (2005)

  •  (VizTree).. a way to do such analysis more systematically  Edward Tufte (website)

  •  Your Viztree work has inspired our thinking around visualizing health care data using state machines.  Melanie Rosenthal, ergoHealthy.com

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 ]

  •  I have been playing around with uniform scaling and I am finding it very useful indeed... Parham Zolfaghari BNP Paribas Bank
 

Lin, J., Keogh, E., Lonardi, S. & Chiu, B. (2003) A Symbolic Representation of Time Series, with Implications for Streaming Algorithms. In proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery. San Diego, CA. June 13. [pdf, slides] An expanded version of this paper appears in The Data Mining and Knowledge Discovery Journal.

  • the performance SAX enables is amazing, and I think a real breakthrough. As an example, we can find similarity searches using edit distance over 10,000 time series in 50 milliseconds. Ray Cromwell, Timepedia.org
  • SAX represents the state-of-the-art in time series data streams analysis due to its generality   Gaber and Gama, Tutorial PKDD07
  • In order to characterize the expression waveforms we follow the basic SAX  formalism for time-series analysis presented by Keogh and Lin... Androulakis et al
  • (SAX based VizTree is).. a way to do such analysis more systematically Edward Tufte.
  • The method is based on the notion of so called time signature of the clusters, introduced in Lin & Keogh and obtained using a recent time series analysis method called the Symbolic Aggregate approximation (SAX). Pouget, M. Dacier, J. Zimmerman, A. Clark, and G. Mohay. Journal of Information Assurance and Security 1 (2006) 21-32
  • "Our goal is to identify those transcripts that share significant components of their expression patterns. In order to do so, we explore the SAX idea of Lin and Keogh... Yang et al..
  • we take SAX Motif developed by Keogh in order to support a medical expert in discovering interesting knowledge. Kitaguchi, S.
  • In order to symbolize a street data, we utilize the SAX approach. Jalili and Alipour. 
  • ...we examine another interesting query, the Time Relaxed Spatiotemporal Trajectory Join (TRSTJ)... we address the TRSTJ problem using SAX... Bakalov, Hadjieleftheriou and Tsotras. 
  • We have decided to use SAX (to detect sophisticated attack tools ).. SAX is a recent and popular method with interesting proven properties. F. Pouget, G. Urvoy-Keller, and M. Dacier 
  • ..we are currently using (Lin and Keoghs SAX) approach to creating discrete data from continuous data. Amy McGovern et al.
  • (to find repeated patterns in protein unfolding data) ...we adopted a two step approach called SAX Ferreira et al.
  • ..we use SAX and Keogh's Tarzan algorithm to do anomaly detection in network traffic. Kyoji Umemura et. al.
  • SAX has already prove efficient in a large variety of domains Fabian Pouget, Telecom Paris.
  • SAX representation of abstracted data makes analysis (of anterior-posterior center of pressure) more easy and accurate.   Bhatkar et al.
  • Our Symbolic Transformation (based on SAX method) can be use to discover novel gene relations by mining similar subsequences in time-series microarray data. Vincent Shin-Mu Tseng
  • ..we use SAX bitmap matrices to compute an anomaly score for acoustic signals, enabling the extraction of bird vocalizations and other acoustic events Kasten, McKinley and Gage. 2007
  • Using the time-series data as an input, it takes too much computation amount to extract motifs from the human motion information. Therefore, we use Symbolic Aggregate approXimation (SAX) .Araki , Arita and Taniguchi 2006
  • SAX has the advantage of dimensionality and noise reductions. It also allows real valued data to remain the original characteristics with only an infinitesimal time and space overhead...we therefore use it for to determine behavior of system... Lavangnananda and Wongwattanakarn. SMCai07.
  • SAX demonstrates some promising properties for the field of anomaly detection in a marine engine. Morgan, Liu, Turnbull, and Brown 2007.
  • ..motivated by recent advances in the symbolic representation of streaming data (SAX), effectively reduces the dimensionality of.. Annu. Rev. Biomed. Eng. 2007.
  • (we use SAX to create a) ..secure multiparty protocol for the privacy preserving pattern discovery problem. Costa da Silva and  Klusch 2007.
  • By using SAX with the sensor network data, we are able to detect such complex patterns with good accuracy .. SAX is a very mature and robust solution for mining time-series data. Zoumboulakis and Roussos 2007
  • we apply the (SAX based) motif discovery approach the analysis of responses obtained by tactile stimulation of different body areas. Fabri et al. IJCNN07
  • We extend the symbolic aggregate approximation (SAX) approach.. to support Query-by-Singing/Humming. Duda, Nurnberger and Stober (2007).
  • We use an algorithm based on SAX (Symbolic Aggregate approXimation) to discover human skills..  Makio, Tanaka, and Uehara 2007
  • symbolic aggregate approximation (SAX) outperform other dimensionality reduction techniques like singular value decomposition or discrete fourier transform (SVD, DFT) for time series data.. Assent, Krieger, Afschari and Seidl EDBT 2008
  1. More than 200 references, see the SAX page.

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]

  •  ..our recognition algorithm can also be viewed as ...their (RK-Band) algorithm. The average performance is better than all the other algorithms.  Veeraraghavan et. al

Lin, J., Vlachos, M., Keogh, E., & Gunopulos, D (2004). Iterative Incremental Clustering of Time Series. In proceedings of the IX Conference on Extending Database Technology. Crete, Greece. March 14-18, 2004. [pdf]

T. Palpanas, M. Vlachos, E. Keogh, D. Gunopulos, W. Truppel (2004). Online Amnesic Approximation of Streaming Time Series. In ICDE . Boston, MA, USA, March 2004. [ pdf] Now a TKDE paper

  •  Amnesic approximation of streaming time series was set forth.. offering the intuitive idea that data can be approximated with a precision proportional to its age.  Timos Sellis 2006.

E. Keogh, J. Lin, and W. Truppel. (2003). Clustering of Time Series Subsequences is Meaningless: Implications for Past and Future Research. In proceedings of the 3rd IEEE International Conference on Data Mining . Melbourne, FL. Nov 19-22. pp 115-122. [ pdf]. Appears as a journal paper in Knowl. Inf. Syst. 8(2): 154-177 (2005)

  •  Clustering of Time Series was a widely used technique in the Data Mining Community and beyond until the discovery by Keogh et. al. in 2003 that it is meaningless.  Chen 2006.

  •  Data mining and machine leaning communities were surprised when Keogh et al. (2003) pointed out that the k-means cluster centers in subsequence time-series clustering become sinusoidal pseudopatterns for almost all kinds of input time-series data.  Ide 2006.

  •  Keogh et al. considered clustering of streaming time series data, and claim that clustering such data is meaningless. We also found... (this to be true).  Singhal and Seborg 2006

Chiu, B. Keogh, E., & Lonardi, S. (2003). Probabilistic Discovery of Time Series Motifs. In the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. August 24 - 27, 2003. Washington, DC, USA. pp 493-498. [Expanded Version pdf]

  •  Motif extraction makes it easier for system developers to enumerate human actions without omission since they can choose human actions from extracted motifs . Arita et al

  • we take SAX Motif developed by Keogh in order to support a medical expert in discovering interesting knowledge . Kitaguchi, S.

  •  To analyze these ill-formed time-series data, we take another time-series data analysis method such .. and finding Motif based on SAX  Abe & Yamaguchi

  •  In our approach, the video database is pre-processed to classify the motion of the human figures and identify the movements of repeated sequences using motifs.  Celly & Zordan

  • Our proposed algorithm identifies genes that maximally contribute to changes in the overall transcriptional state of the system... The characteristic attribute will be defined using established methods of SAX based motifs..  Yang et al.

  •  Our notion of KNTN was inspired by (the Motif definition) who define the concept of trivial matches in data mining for univariate time-series  Maja J Mataric.

  • We use Keogh's motifs to compress data found in large system call data sets.. Wilson, Feyereisl and Aickelin 2007

  • we apply the SAX based motif discovery approach the analysis of responses obtained by tactile stimulation of different body areas. Fabri et al. IJCNN07

  • .We use time series motifs to discover skills from music performances..  Makio, Tanaka, and Uehara 2007

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]. An expanded version appears in the VLDB l vol:15 iss:1 pg:1 -20


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 ]

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

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

  •  (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

  •  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.

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

  •  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

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

  •  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

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

  •  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 et al. 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
  • (we use) the tightest existing lower bound.. (for) exact indexing of Dynamic Time Warping, LBKeogh. Assent, Krieger, Afschari and Seidl EDBT 2008
  • 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 best lowerbound function in terms of tightness is the LB_Keogh... It cleverly exploits the warping window.. Zhou and Wong ICDE08

Keogh, E. and Kasetty, S. (2002). On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. In the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. July 23 - 26, 2002. Edmonton, Alberta, Canada. pp 102-111. [pdf] An expanded version of this paper appears in The Data Mining and Knowledge Discovery Journal. [Journal Version pdf]

Keogh, E., Lonardi, S and Chiu, W. (2002). Finding Surprising Patterns in a Time Series Database In Linear Time and Space. In the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. July 23 - 26, 2002. Edmonton, Alberta, Canada. pp 550-556. [Expanded Version pdf]

  •  ..we use SAX and Keogh's Tarzan algorithm to do anomaly detection in network traffic . Kyoji Umemura et. al.

  •  To discover the patterns in the behavior of state statistics, we use the Tarzan algorithm for analyzing time series…  Emre Kıcıman  

Keogh, E. & Pazzani, M. (1999). Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches. In Uncertainty 99, 7th. Int'l Workshop on AI and Statistics, Ft. Lauderdale, FL, pp. 225--230. [ps]. 

  • superparent provides considerable decrease in prediction error . Geoff Webb.
  • superparent-one-dependence estimators have received a lot of attention because they offer a combination of high training efficiency, high classification efficiency and high classification accuracy. Y.Yang, et al. IEEE TKDE 2007.
  • an ensemble of SuperParent one-dependence estimators  can deliver very high classification accuracy. Ying Yang et al Machine Leaning 2007.

Keogh, E., Chu, S., Hart, D. & Pazzani, M. (2001). An Online Algorithm for Segmenting Time Series. In Proceedings of IEEE International Conference on Data Mining. pp 289-296. [ps, pdf]

  • This amazing result is achieved due to two factors. The SWAB can reduce the size of patterns to about 20% of the original size  Tang et al. SIGMOD 07

  • The SWAB algorithm [Keogh et al. 2001] was used to partition the time series given a user-specified threshold.. It is attractive for solving our problem because of its simplicity. Yin et al. ACM Transactions on Sensor Networks. 2008

  • In these results, we present the cost of performing model fitting, via an online segmentation-based algorithm (SWAB) to find a piecewise linear model to the input data .. is indeed able to support high-throughput stream processing. Ahmad et al ICDE'08

  • For the construction of approximate rank synopses we adopt the SWAB algorithm that we found to perform well in practice. SPIRE 2007 Berberich, Bedathur, and Weikum

  • For the motion segmentation the SWAB (Sliding window and bottom up) algorithm proposed by Keogh et al.  was used due to its excellent performance . Oliver Amft.

  • (SWAB) was first proposed by Keogh.. we evaluate its benefits in the context of our BSearch algorithm.. (it allows) query processing that is 10X to 100X faster.  Arvind Thiagarajan MIT 2007

  • Keoghs ICDM paper  provides a good overview of the process of time series segmentation. . Mark Moss

  • To best serve our purpose without any modification, the bottom-up algorithm or SWAB is appropriate.  Jai-Jin Lim and Kang G. Shin

  • The SWAB algorithm given by Keogh et al using sliding window, is particularly popular with the medical community, since patient monitoring is inherently an online task...with the help of SWAB , we separate the data stream..  Yu Fan et al

  • especially Keogh, which presents a good survey of segmentation methods appearing in the data mining literature  Kehagias et al.

  • For the gesture recognition of the parking problem, we used the SWAB  Bannach et al. Embedded Systems Lab, University of Passau

  • SumTime-Mousam determines content using linear segmentation using Keoghs algorithm  Reiter et. al.

  • We used the Sliding-Window And Bottom-up (SWAB) algorithm to detect and classify of normal swallowing in humans.. . Oliver Amft and Gerhard Troster 2006

  •  to capture the evolutionary history of the webs structure and content we use (Keoghs) close-to-optimal rank synopsis... Furthermore, (using SWAB) the close-to-optimal rank synopses can be maintained incrementally as new observations of the evolving Web graph become available.  Berberich, Bedathur and Weikum, SIGIR 2007.

  • we use (SWAB) for generating textual summary of Neonatal intensive care data  Portet, Reiter, Hunter and Sripada AIME07

  • We experimented with the online algorithm of Keogh et al.. to use them to detect epidemics.  Heino and Toivonen 2003.

  • To begin with, dealing with large amounts of data requires a representation that would allow efficient computation. As a robust and efficient method for representing time series, Keogh's Piece-Wise Linear Segmentation (PWLS) was found to be a suitable method.. for a 600 day mission to Mars. Aydogan, Pekny and Orcun 2007

  • For intensive care monitoring .. in the first step of the procedure, trend detection was performed on separate periods relying on a piecewise linear segmentation of the time series which was carried out by SWAB. Verduijn, Sacchi, Peek, Bellazzi, de Jonge, de Mol  Artificial Intelligence in Medicine 2007.

  • We adapt (SWAB).. to develop a linear-time algorithm that generates nearly-optimal temporal coalescing for the given error threshold. Berberich, Bedathur, Neumann and Weikum VLDB 2007

  • The algorithm utilized for (recognizing gestures) is Sliding window and bottom-up (SWAB). Bannach, Amft, Kunze, Heinz, Troster and Lukowicz. CIG07

  • Accordingly we have used a well-known segmentation algorithm from the KDD community known as SWAB (Keogh et al 2002).. Sripada, Reiter, Hunter and Yu. Proc. Corpus Linguistics 2003

  • Using a user-defined error tolerance for approximation and an online-algorithm SWAB. the curve is represented by piecewise linear segments. This approximation significantly reduces the number of extracted features search.. Chen, Yau, Hansen, and Estrin 2007.

  • (to create) textual summaries of spatio-temporal data .. We use the linear segmentation algorithm (SWAB) developed by Keogh2001 and successfully applied in the SumTime system. Turner et al. 2007

  • To adapt the characteristics of streaming data, we need an online piecewise linear approximation algorithm. The proposed SWAB algorithm not only takes the advantage of linear time and online fashion of sliding window method, but also.. Yeh, Dai, and Chen, TKDE 2008.

  • We use a online segmentation-based algorithm (SWAB) ..  for processing continuous queries over models of continuous-time data.  Ahmad, Papaemmanouil, Cetintemel, Rogers ICDE08

  • ..since all financial time series contain high levels of noise, we need to smoothen the time series by using SWAB.. Wu et al. APWeb 2008

  •  The gestures potting procedure uses an explicit time series segmentation algorithm (SWAB) for activity recognition.  Bannach, D. Lukowicz, P. Amft, O.  Pervasive Computing, IEEE

  • The (SWAB) segmentation algorithm which was developed by Keogh et al. was used to segment the time series signals for fatigue analysis.. Nopiah et, a. SIP-08

Keogh, E. & Pazzani, M. (1999).  Scaling up dynamic time warping to massive datasets. In Proceedings of the 3rd European Conference on Principles and Practice of Knowledge Discovery in Databases. pp 1-11. [ps, pdf].

Keogh, E. & Pazzani,M (1999). Relevance feedback retrieval of time series data . In Proceedings of the 22th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval. pp 183-190. [ps, pdf]

Keogh, E. & Pazzani, M. (2000). A simple dimensionality reduction technique for fast similarity search in large time series databases.  In Proceedings of Pacific- Asia Conf. on Knowledge Discovery and Data Mining, pp 122-133. [ps, pdf]

Keogh, E.,  Chakrabarti, K., Pazzani, M. & Mehrotra, S. (2001). Locally adaptive dimensionality reduction for indexing large time series databases. In proceedings of ACM SIGMOD Conference on Management of Data, May. pp 151-162 Best paper award. [PDF] According to iFuice (Prof. Dr. Erhard Rahm) This is one of the top 5 most referenced papers of SIGMOD 2001. A journal version appears in TODS

  • In our work, we use the APCA approximation with the distance measures proposed by Keogh et al. Our results indicated APCA it increases the precision of the index without any adverse affect on the search performance… which translates to fewer disk I/Os.  Kadiyala and Shiri, KAIS 2007

  • coupled with the state of the art dimensionality reduction techniques such as Adaptive Piecewise Constant Approximation (APCA) and improve its performance by up to a factor of 3. Li, Lopez and Moon TKDE 2004

  • Our (indexing) technique is based on the segmentation of each sequence into an Adaptive Piecewise Constant Approximation (APCA). SHOU et al, ML Journal 2005

Keogh, E.,  Chakrabarti, K., Pazzani, M. & Mehrotra, S. (2000). Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. Knowledge and Information Systems 3(3): 263-286. [ps, pdf]

  •  More specifically, to be able to support all types of search queries listed above, we use the Piecewise Aggregate Approximation (PAA) . A.H.R. Albers et al
  •  Furthermore, we propose an efficient indexing method to retrieve similar trajectories for a query by combining a spatial indexing technique and PAA.  Yanagisawa et al
  •  (our systems uses) the Piecewise Aggregate Approximation (PAA). This results in a more compact description of the trajectory and enables fast search-queries.  Jaspers  et. al (Bosch Security Systems)
  •  By taking advantage of Piecewise Aggregate Approximation (PAA) we significantly speed up our algorithm both in the preprocessing and segmentation stage  Zifan et al. 2007
  •   PAA results in a more compact description of the trajectory...   E. Jaspers
  •  Using PAA based approach the search time is reduced by approx an order of magnitude.  Austin (2006) AURA technology project.
  •  Motivated by Keogh and Pazzanis PAA we have present a Karaoke music retrieval system that allows users to locate their desired music by singing to the system  Hung-Ming Yu et al
  •  (we use) LB_Keogh with PAA for efficient similarity range query processing in music databases  Ruxanda & Jensen 2006
  •  We propose to use the piecewise aggregate approximation (PAA) to solve this problem.  Ting Wang 2006:
  •  PAA is a powerful compression tool  Wilson, Birkin, and Aickelin 2007
  •  many linear transformations have been tested but PAA has been shown to be the best dimensionality reduction technique DTW queries. Thus we use PAA to..   Assent, Krieger, Afschari and Seidl EDBT 2008
  • Keoghs PAA method was used in this study (on) a telemedicine application using an internet ECG database for risk stratification of patients with various cardiovascular disease state. Khoor et al. 2007
  • (to support our system for Target Classification) each reference signal is compressed to the length of input signal using the PAA technique. Kim, Kim, Kim, Sung, and Yoo 2007.
  • …telemedicine application using an internet ECG database for risk stratification of patients with various cardiovascular disease state.. the PAA method was used in this study... Khoor et al. 2008

Chu, S., Keogh, E., Hart, D. & Pazzani, M.  (2002). Iterative Deepening Dynamic Time Warping.  In Second SIAM International Conference on Data Mining. [ps, pdf]

 ... can drastically reduce the computation time for classification and clustering of massive data sets . Arno Zinke 2006

Keogh, E. & Pazzani, M. (2001). Dynamic Time Warping with Higher Order Features. In First SIAM International Conference on Data Mining (SDM'2001), Chicago, USA. [pdf]

  • Using Keoghs  DDTW on Electrocardiogram segmentation is robust enough to give measures comparable to those given by experts.  Zifan et al.

  • the results obtained by the DDTW measure are very promising when compared with the classical feature extraction.. Miroslav Bursa and Lenka Lhotska 2007

  • The results on DDTW, obtained by using only one sensor channel on the shin showed an average recognition score of 95%, higher than the values obtained with DTW. Rossana Muscillo, IEEE EMBS 2007.

  • we have extended Keogh and Pazzani's (derivative dynamic time warping) measure.  Marek Kulbacki

  • we use Keogh's DDTW  for predicting the quality of a batch process using an inferential sensor... G. Gins, I.Y. Smets and J.F. Van Impe

  •  We use the Derivative Dynamic Time Warping (DDTW) method of Keogh for matching pairs of points between two subsequences..  Walter P. Schiefele 

  •  To help make training series more consistent and to ultimately merge these training series into one (generalize), derivative dynamic time warping (DDTW) is employed..  Sophia Antipolis

  •  The DTW cost function was based on derivative spectral features, as advocated generally by Keogh   Simon Dixon.

  •  {Translated from Polish} with Derivative Dynamic Time Warping we got the best accuracy  Khalid Saeed.

  •  This approach especially requires to define a similarity measure appropriate to the comparison of multivariate time-series of different lengths, so that events and states are properly identified and clustered. We use the Derivative Dynamic Time Warping (DDTW) method . Alberto Avanzi et al.

  •  In this work, characteristic voltage has been used to apply the DDTW algorithm in order to find similarity criteria among of a set of sag registers....  Llanos et al.

  •  One of them, derivative dynamic time warping, is used in our program which verificates signatures by computing distance between curves or shapes of signatures.  Algirdas Bastys

  •  we use the DDTW to establish the correspondence of crucial points.  Quan and Ji.

  •   We use the Derivative Dynamic Time Warping (DDTW) method for.. ...decision-making in surveillance systems  Monique Thonnat.

  •  For Multi-Dimensional Dynamic TimeWarping for Gesture Recognition Derivative DTW gave the better performance.  G.A. ten Holt , M.J.T. Reinders , E.A. Hendriks (2007)

  • Derivative Dynamic Time Warping (DDTW) has been proved to be useful in time series alignment to avoid singularity points and reduce the bias of alignment results...  Zhang, Yang and Edgar, Thomas 2008

 

Keogh, E. & Pazzani,M.(2000). Scaling Up Dynamic Time Warping for Data Mining Applications. In proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, August 20-23. Boston, MA, USA. pp 285-289. [pdf]

Keogh, E. and Pazzani, M. (1998). An enhanced representation of time series which allows fast and accurate classification clustering and relevance feedback. In 4th International Conference on Knowledge Discovery and Data Mining. New York, NY, Aug 27-31. pp 239-243. [ps, pdf]

Keogh, E. & Smyth, P. (1997). A probabilistic approach to fast pattern matching in time series databases. In Proceedings of the 3rd International Conference of Knowledge Discovery and Data Mining. pp 24-20. Runner up, best paper award.  Also appears as: Clustering and Mode Classification of Engineering Time Series Data. JPL Technical report 960621. [ps]

Keogh, E. An Overview of the Science of Fingerprints. Anil Aggrawal's Journal of Forensic Medicine and Toxicology, 2001; Vol. 2, No. 1 (January-June 2001): Published January 8, 2001

  1. Birke Reinhard (2002) Die verschiedenen technischen Möglichkeiten zur Erfassung von Fingerabdrücken 
  2. Florian Bauer (2002).  Fingerabdrücke von Zwillingen ( Twin Test ) 
  3. Fred Chris Smith and Rebecca G. Bace (2002). The Information Technology Expert. Published by Addison Wesley Longman Publishing.
  4. Dr Simon W. Lewis, Deakin University (2000). http://www.deakin.edu.au/forensic/Chemical%20Detective/fp_links.htm
  5. Murdoch University (2001). http://wwwscience.murdoch.edu.au/teaching/m235/forensictech.htm
  6. James Betts (2002). Lincoln Nebraska Police Department Training Materials.
  7. Mboneli Ndlangisa (2001). Biometric Authentication using fingerprints and evaluating fingerprint readers. Rhodes University, Masters thesis.
  8. Steven Flaherty, Ryan McGrath, Chih-Liang Cheng , Chih-Hung Cheng ,Jong-Yun Ahn (2002). Biometrics, Counterterrorism And Personal Privacy. University of Colorado Denver.
  9. Australian Broadcasting Corporation (2002). Used as background material for: FOUR CORNERS program  Finger of Suspicion .
  10. Xiaoyi Jiang (2003). Biometrik: Fingerabdruckerkennung, Ein Ausarbeitung innerhalb des Seminars Biometrics
  11. Translated into Polish by Dr. Andrzej Pacut. Biulety Nask, ISSN 1509-3603. Styczen- Luty 2003.