BibSLEIGH — Eamonn_J

Travelled to:

1 × Australia
1 × France
1 × Korea
1 × Norway
17 × USA
2 × Canada
2 × China

Collaborated with:

M.J.Pazzani S.Lonardi A.Mueen ∅ L.Wei B.Y.Chiu N.Begum L.Ye X.Wang M.Vlachos T.Rakthanmanon G.E.A.P.A.Batista Y.Chen B.Hu C.(.Ratanamahatana J.Shieh S.Kasetty P.Smyth J.Zakaria S.Lee C.R.Shelton M.Hadjieleftheriou D.Gunopulos L.Ulanova V.B.Zordan X.Xi J.Lin J.P.Lankford D.M.Nystrom Q.Zhu O.M.Tataw N.E.Young S.Chu B.J.L.Campana J.Wang M.Hasan V.J.Tsotras A.Mafra-Neto E.Rowton A.W.Fu L.Y.H.Lau H.Ding G.Trajcevski P.Scheuermann D.Yankov J.Medina P.Viana A.Gordon-Ross E.Barros F.Vahid C.A.Ratanamahatana A.Anagnostopoulos P.S.Yu T.Palpanas M.Cardle M.Shokoohi-Yekta T.Yan H.Chen G.Jiang K.Zhang Y.Hao M.B.Westover

Talks about:

time (31) seri (25) mine (11) index (8) data (7) databas (6) warp (5) discoveri (4) distanc (4) classif (4)

Person: Eamonn J. Keogh

DBLP: Keogh:Eamonn_J=

Contributed to:

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

2008

Wrote 39 papers:

KDD-2015-BegumUWK #clustering #novel: Accelerating Dynamic Time Warping Clustering with a Novel Admissible Pruning Strategy (NB, LU, JW, EJK), pp. 49–58.
KDD-2015-Shokoohi-Yekta0: Discovery of Meaningful Rules in Time Series (MSY, YC, BJLC, BH, JZ, EJK), pp. 1085–1094.
KDD-2015-UlanovaYCJKZ #performance #physics #profiling: Efficient Long-Term Degradation Profiling in Time Series for Complex Physical Systems (LU, TY, HC, GJ, EJK, KZ), pp. 2167–2176.
VLDB-2015-BegumK14 #bound: Rare Time Series Motif Discovery from Unbounded Streams (NB, EJK), pp. 149–160.
ICDAR-2013-TatawRK #clustering #using: Clustering of Symbols Using Minimal Description Length (OMT, TR, EJK), pp. 180–184.
KDD-2013-ChenHKB #learning #named: DTW-D: time series semi-supervised learning from a single example (YC, BH, EJK, GEAPAB), pp. 383–391.
KDD-2013-HaoCZ0RK #learning #towards: Towards never-ending learning from time series streams (YH, YC, JZ, BH, TR, EJK), pp. 874–882.
CIKM-2012-HasanMTK #query: Diversifying query results on semi-structured data (MH, AM, VJT, EJK), pp. 2099–2103.
KDD-2012-RakthanmanonCMBWZZK #mining #sequence: Searching and mining trillions of time series subsequences under dynamic time warping (TR, BJLC, AM, GEAPAB, MBW, QZ, JZ, EJK), pp. 262–270.
KDD-2011-BatistaKMR #data mining #mining: SIGKDD demo: sensors and software to allow computational entomology, an emerging application of data mining (GEAPAB, EJK, AMN, ER), pp. 761–764.
KDD-2011-MueenKY #classification #named: Logical-shapelets: an expressive primitive for time series classification (AM, EJK, NEY), pp. 1154–1162.
KDD-2010-MueenK #maintenance #online: Online discovery and maintenance of time series motifs (AM, EJK), pp. 1089–1098.
KDD-2009-YeK #data mining #mining: Time series shapelets: a new primitive for data mining (LY, EJK), pp. 947–956.
KDD-2009-ZhuWKL #mining: Augmenting the generalized hough transform to enable the mining of petroglyphs (QZ, XW, EJK, SHL), pp. 1057–1066.
KDD-2008-ShiehK #mining #named: iSAX: indexing and mining terabyte sized time series (JS, EJK), pp. 623–631.
VLDB-2008-DingTSWK #comparison #distance #metric #mining #query: Querying and mining of time series data: experimental comparison of representations and distance measures (HD, GT, PS, XW, EJK), pp. 1542–1552.
KDD-2007-YankovKMCZ #detection #scalability: Detecting time series motifs under uniform scaling (DY, EJK, JM, BYcC, VBZ), pp. 844–853.
DAC-2006-VianaGKBV #configuration management #performance: Configurable cache subsetting for fast cache tuning (PV, AGR, EJK, EB, FV), pp. 695–700.
ICML-2006-XiKSWR #classification #performance #reduction #using: Fast time series classification using numerosity reduction (XX, EJK, CRS, LW, CAR), pp. 1033–1040.
KDD-2006-AnagnostopoulosVHKY #segmentation: Global distance-based segmentation of trajectories (AA, MV, MH, EJK, PSY), pp. 34–43.
KDD-2006-WeiK #classification: Semi-supervised time series classification (LW, EJK), pp. 748–753.
VLDB-2006-Keogh #database #mining #scalability: A Decade of Progress in Indexing and Mining Large Time Series Databases (EJK), p. 1268.
VLDB-2006-KeoghWXLV #distance #metric: LB_Keogh Supports Exact Indexing of Shapes under Rotation Invariance with Arbitrary Representations and Distance Measures (EJK, LW, XX, SHL, MV), pp. 882–893.
VLDB-2005-FuKLR #query #scalability: Scaling and Time Warping in Time Series Querying (AWCF, EJK, LYHL, C(R), pp. 649–660.
KDD-2004-KeoghLR #data mining #mining #towards: Towards parameter-free data mining (EJK, SL, C(R), pp. 206–215.
KDD-2004-LinKLLN #mining #monitoring #visual notation: Visually mining and monitoring massive time series (JL, EJK, SL, JPL, DMN), pp. 460–469.
VLDB-2004-LinKLLN #database #mining #monitoring #named #visual notation: VizTree: a Tool for Visually Mining and Monitoring Massive Time Series Databases (JL, EJK, SL, JPL, DMN), pp. 1269–1272.
VLDB-2004-PalpanasCGKZ #database #scalability: Indexing Large Human-Motion Databases (EJK, TP, VBZ, DG, MC), pp. 780–791.
KDD-2003-ChiuKL #probability: Probabilistic discovery of time series motifs (BYcC, EJK, SL), pp. 493–498.
KDD-2003-VlachosHGK #distance #metric #multi: Indexing multi-dimensional time-series with support for multiple distance measures (MV, MH, DG, EJK), pp. 216–225.
KDD-2002-KeoghK #benchmark #data mining #empirical #metric #mining #on the #overview: On the need for time series data mining benchmarks: a survey and empirical demonstration (EJK, SK), pp. 102–111.
KDD-2002-KeoghLC #database #linear: Finding surprising patterns in a time series database in linear time and space (EJK, SL, BYcC), pp. 550–556.
VLDB-2002-Keogh: Exact Indexing of Dynamic Time Warping (EJK), pp. 406–417.
KDD-2001-KeoghCP #approach #database #named #scalability: Ensemble-index: a new approach to indexing large databases (EJK, SC, MJP), pp. 117–125.
KDD-2000-KeoghP #scalability: Scaling up dynamic time warping for datamining applications (EJK, MJP), pp. 285–289.
SIGIR-1999-KeoghP #feedback #retrieval: Relevance Feedback Retrieval of Time Series Data (EJK, MJP), pp. 183–190.
KDD-1998-KeoghP #classification #clustering #feedback #performance #representation: An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback (EJK, MJP), pp. 239–243.
KDD-1997-KeoghS #approach #database #pattern matching #performance #probability: A Probabilistic Approach to Fast Pattern Matching in Time Series Databases (EJK, PS), pp. 24–30.
JCDL-2008-WangYKS #image: Annotating historical archives of images (XW, LY, EJK, CRS), pp. 341–350.