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Eamonn J. Keogh

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2008
109EEShashwati Kasetty, Candice Stafford, Gregory P. Walker, Xiaoyue Wang, Eamonn J. Keogh: Real-Time Classification of Streaming Sensor Data. ICTAI (1) 2008: 149-156
108EEXiaoyue Wang, Lexiang Ye, Eamonn J. Keogh, Christian R. Shelton: Annotating historical archives of images. JCDL 2008: 341-350
107EEJin Shieh, Eamonn J. Keogh: iSAX: indexing and mining terabyte sized time series. KDD 2008: 623-631
106EELexiang Ye, Xiaoyue Wang, Dragomir Yankov, Eamonn J. Keogh: The Asymmetric Approximate Anytime Join: A New Primitive with Applications to Data Mining. SDM 2008: 363-374
105EEEamonn J. Keogh: Indexing and Mining Time Series Data. Encyclopedia of GIS 2008: 493-497
104EELi Wei, Eamonn J. Keogh, Xiaopeng Xi, Melissa Yoder: Efficiently finding unusual shapes in large image databases. Data Min. Knowl. Discov. 17(3): 343-376 (2008)
103EEThemis Palpanas, Michail Vlachos, Eamonn J. Keogh, Dimitrios Gunopulos: Streaming Time Series Summarization Using User-Defined Amnesic Functions. IEEE Trans. Knowl. Data Eng. 20(7): 992-1006 (2008)
102EEDragomir Yankov, Eamonn J. Keogh, Li Wei, Xiaopeng Xi, Wendy L. Hodges: Fast Best-Match Shape Searching in Rotation-Invariant Metric Spaces. IEEE Transactions on Multimedia 10(2): 230-239 (2008)
101EEDragomir Yankov, Eamonn J. Keogh, Umaa Rebbapragada: Disk aware discord discovery: finding unusual time series in terabyte sized datasets. Knowl. Inf. Syst. 17(2): 241-262 (2008)
100EEHui Ding, Goce Trajcevski, Peter Scheuermann, Xiaoyue Wang, Eamonn J. Keogh: Querying and mining of time series data: experimental comparison of representations and distance measures. PVLDB 1(2): 1542-1552 (2008)
99EEXiaopeng Xi, Ken Ueno, Eamonn J. Keogh, Dah-Jye Lee: Converting non-parametric distance-based classification to anytime algorithms. Pattern Anal. Appl. 11(3-4): 321-336 (2008)
98EEAda Wai-Chee Fu, Eamonn J. Keogh, Leo Yung Hang Lau, Chotirat (Ann) Ratanamahatana, Raymond Chi-Wing Wong: Scaling and time warping in time series querying. VLDB J. 17(4): 899-921 (2008)
2007
97EEPetko Bakalov, Eamonn J. Keogh, Vassilis J. Tsotras: TS2-tree - an efficient similarity based organization for trajectory data. GIS 2007: 58
96EEDragomir Yankov, Eamonn J. Keogh, Umaa Rebbapragada: Disk Aware Discord Discovery: Finding Unusual Time Series in Terabyte Sized Datasets. ICDM 2007: 381-390
95EEDragomir Yankov, Eamonn J. Keogh, Kin Fai Kan: Locally Constrained Support Vector Clustering. ICDM 2007: 715-720
94EEDragomir Yankov, Eamonn J. Keogh, Jose Medina, Bill Yuan-chi Chiu, Victor B. Zordan: Detecting time series motifs under uniform scaling. KDD 2007: 844-853
93EEMichail Vlachos, Bahar Taneri, Eamonn J. Keogh, Philip S. Yu: Visual Exploration of Genomic Data. PKDD 2007: 613-620
92EEDragomir Yankov, Eamonn J. Keogh, Li Wei, Xiaopeng Xi, Wendy L. Hodges: Fast Best-Match Shape Searching in Rotation Invariant Metric Spaces. SDM 2007
91EEXiaopeng Xi, Eamonn J. Keogh, Li Wei, Agenor Mafra-Neto: Finding Motifs in a Database of Shapes. SDM 2007
90EEYingyi Bu, Oscar Tat-Wing Leung, Ada Wai-Chee Fu, Eamonn J. Keogh, Jian Pei, Sam Meshkin: WAT: Finding Top-K Discords in Time Series Database. SDM 2007
89EEEamonn J. Keogh, Stefano Lonardi, Chotirat Ann Ratanamahatana, Li Wei, Sang-Hee Lee, John Handley: Compression-based data mining of sequential data. Data Min. Knowl. Discov. 14(1): 99-129 (2007)
88EEJessica Lin, Eamonn J. Keogh, Li Wei, Stefano Lonardi: Experiencing SAX: a novel symbolic representation of time series. Data Min. Knowl. Discov. 15(2): 107-144 (2007)
87EELongbing Cao, Chengqi Zhang, Qiang Yang, David Bell, Michail Vlachos, Bahar Taneri, Eamonn J. Keogh, Philip S. Yu, Ning Zhong, Mafruz Zaman Ashrafi, David Taniar, Eugene Dubossarsky, Warwick Graco: Domain-Driven, Actionable Knowledge Discovery. IEEE Intelligent Systems 22(4): 78-88 (2007)
86EEEamonn J. Keogh, Jessica Lin, Sang-Hee Lee, Helga Van Herle: Finding the most unusual time series subsequence: algorithms and applications. Knowl. Inf. Syst. 11(1): 1-27 (2007)
85EELi Wei, Eamonn J. Keogh, Helga Van Herle, Agenor Mafra-Neto, Russell J. Abbott: Efficient query filtering for streaming time series with applications to semisupervised learning of time series classifiers. Knowl. Inf. Syst. 11(3): 313-344 (2007)
2006
84EEEamonn J. Keogh: Data mining and information retrieval in time series/multimedia databases. ACM Multimedia 2006: 10
83EEAda Wai-Chee Fu, Oscar Tat-Wing Leung, Eamonn J. Keogh, Jessica Lin: Finding Time Series Discords Based on Haar Transform. ADMA 2006: 31-41
82EEPablo Viana, Ann Gordon-Ross, Eamonn J. Keogh, Edna Barros, Frank Vahid: Configurable cache subsetting for fast cache tuning. DAC 2006: 695-700
81EEDragomir Yankov, Dennis DeCoste, Eamonn J. Keogh: Ensembles of Nearest Neighbor Forecasts. ECML 2006: 545-556
80EEDragomir Yankov, Eamonn J. Keogh: Manifold Clustering of Shapes. ICDM 2006: 1167-1171
79EEKen Ueno, Xiaopeng Xi, Eamonn J. Keogh, Dah-Jye Lee: Anytime Classification Using the Nearest Neighbor Algorithm with Applications to Stream Mining. ICDM 2006: 623-632
78EELi Wei, Eamonn J. Keogh, Xiaopeng Xi: SAXually Explicit Images: Finding Unusual Shapes. ICDM 2006: 711-720
77EEEamonn J. Keogh, Li Wei, Xiaopeng Xi, Stefano Lonardi, Jin Shieh, Scott Sirowy: Intelligent Icons: Integrating Lite-Weight Data Mining and Visualization into GUI Operating Systems. ICDM 2006: 912-916
76EELi Wei, John Handley, Nathaniel Martin, Tong Sun, Eamonn J. Keogh: Clustering Workflow Requirements Using Compression Dissimilarity Measure. ICDM Workshops 2006: 50-54
75EEXiaopeng Xi, Eamonn J. Keogh, Christian R. Shelton, Li Wei, Chotirat Ann Ratanamahatana: Fast time series classification using numerosity reduction. ICML 2006: 1033-1040
74EEAris Anagnostopoulos, Michail Vlachos, Marios Hadjieleftheriou, Eamonn J. Keogh, Philip S. Yu: Global distance-based segmentation of trajectories. KDD 2006: 34-43
73EELi Wei, Eamonn J. Keogh: Semi-supervised time series classification. KDD 2006: 748-753
72EEJessica Lin, Eamonn J. Keogh: Group SAX: Extending the Notion of Contrast Sets to Time Series and Multimedia Data. PKDD 2006: 284-296
71EEEamonn J. Keogh: A Decade of Progress in Indexing and Mining Large Time Series Databases. VLDB 2006: 1268
70EEEamonn J. Keogh, Li Wei, Xiaopeng Xi, Sang-Hee Lee, Michail Vlachos: LB_Keogh Supports Exact Indexing of Shapes under Rotation Invariance with Arbitrary Representations and Distance Measures. VLDB 2006: 882-893
69EEAnthony J. Bagnall, Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh, Stefano Lonardi, Gareth J. Janacek: A Bit Level Representation for Time Series Data Mining with Shape Based Similarity. Data Min. Knowl. Discov. 13(1): 11-40 (2006)
68EEEamonn J. Keogh, Jessica Lin, Ada Wai-Chee Fu, Helga Van Herle: Finding Unusual Medical Time-Series Subsequences: Algorithms and Applications. IEEE Transactions on Information Technology in Biomedicine 10(3): 429-439 (2006)
67EEStefano Lonardi, Jessica Lin, Eamonn J. Keogh, Bill Yuan-chi Chiu: Efficient Discovery of Unusual Patterns in Time Series. New Generation Comput. 25(1): 61-93 (2006)
66EEMichail Vlachos, Marios Hadjieleftheriou, Dimitrios Gunopulos, Eamonn J. Keogh: Indexing Multidimensional Time-Series. VLDB J. 15(1): 1-20 (2006)
2005
65EEJessica Lin, Eamonn J. Keogh, Ada Wai-Chee Fu, Helga Van Herle: Approximations to Magic: Finding Unusual Medical Time Series. CBMS 2005: 329-334
64EELi Wei, Nitin Kumar, Venkata Nishanth Lolla, Eamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) Ratanamahatana, Helga Van Herle: A Practical Tool for Visualizing and Data Mining Medical Time Series. CBMS 2005: 341-346
63EEChotirat (Ann) Ratanamahatana, Eamonn J. Keogh: Multimedia Retrieval Using Time Series Representation and Relevance Feedback. ICADL 2005: 400-405
62EEEamonn J. Keogh, Jessica Lin, Ada Wai-Chee Fu: HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence. ICDM 2005: 226-233
61EELi Wei, Eamonn J. Keogh, Helga Van Herle, Agenor Mafra-Neto: Atomic Wedgie: Efficient Query Filtering for Streaming Times Series. ICDM 2005: 490-497
60EELongin Jan Latecki, Vasileios Megalooikonomou, Qiang Wang, Rolf Lakämper, Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh: Partial Elastic Matching of Time Series. ICDM 2005: 701-704
59EEDragomir Yankov, Eamonn J. Keogh, Stefano Lonardi, Ada Wai-Chee Fu: Dot Plots for Time Series Analysis. ICTAI 2005: 159-168
58EEEamonn J. Keogh: Visualization and Mining of Temporal Data. IEEE Visualization 2005: 126
57EEChotirat (Ann) Ratanamahatana, Eamonn J. Keogh: Using Relevance Feedback to Learn Both the Distance Measure and the Query in Multimedia Databases. KES (2) 2005: 16-23
56EEPetko Bakalov, Marios Hadjieleftheriou, Eamonn J. Keogh, Vassilis J. Tsotras: Efficient trajectory joins using symbolic representations. Mobile Data Management 2005: 86-93
55EEJessica Lin, Michail Vlachos, Eamonn J. Keogh, Dimitrios Gunopulos, Jian-Wei Liu, Shou-Jian Yu, Jia-Jin Le: A MPAA-Based Iterative Clustering Algorithm Augmented by Nearest Neighbors Search for Time-Series Data Streams. PAKDD 2005: 333-342
54EEChotirat (Ann) Ratanamahatana, Eamonn J. Keogh, Anthony J. Bagnall, Stefano Lonardi: A Novel Bit Level Time Series Representation with Implication of Similarity Search and Clustering. PAKDD 2005: 771-777
53EELongin Jan Latecki, Vasilis Megalooikonomou, Qiang Wang, Rolf Lakämper, Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh: Elastic Partial Matching of Time Series. PKDD 2005: 577-584
52EEEamonn J. Keogh: Recent Advances in Mining Time Series Data. PKDD 2005: 6
51 Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh: Three Myths about Dynamic Time Warping Data Mining. SDM 2005
50 Nitin Kumar, Venkata Nishanth Lolla, Eamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) Ratanamahatana: Time-series Bitmaps: a Practical Visualization Tool for Working with Large Time Series Databases. SDM 2005
49 Li Wei, Nitin Kumar, Venkata Nishanth Lolla, Eamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) Ratanamahatana: Assumption-Free Anomaly Detection in Time Series. SSDBM 2005: 237-240
48EEAda Wai-Chee Fu, Eamonn J. Keogh, Leo Yung Hang Lau, Chotirat (Ann) Ratanamahatana: Scaling and Time Warping in Time Series Querying. VLDB 2005: 649-660
47 Michail Vlachos, Marios Hadjieleftheriou, Eamonn J. Keogh, Dimitrios Gunopulos: Indexing Multi-Dimensional Trajectories for Similarity Queries. Spatial Databases 2005: 107-128
46 Chotirat (Ann) Ratanamahatana, Jessica Lin, Dimitrios Gunopulos, Eamonn J. Keogh, Michail Vlachos, Gautam Das: Mining Time Series Data. The Data Mining and Knowledge Discovery Handbook 2005: 1069-1103
45EEJessica Lin, Eamonn J. Keogh, Stefano Lonardi: Visualizing and discovering non-trivial patterns in large time series databases. Information Visualization 4(2): 61-82 (2005)
44EELi Wei, Eamonn J. Keogh, Xiaopeng Xi, Stefano Lonardi: Integrating Lite-Weight but Ubiquitous Data Mining into GUI Operating Systems. J. UCS 11(11): 1820-1834 (2005)
43EEEamonn J. Keogh, Chotirat (Ann) Ratanamahatana: Exact indexing of dynamic time warping. Knowl. Inf. Syst. 7(3): 358-386 (2005)
42EEEamonn J. Keogh, Jessica Lin: Clustering of time-series subsequences is meaningless: implications for previous and future research. Knowl. Inf. Syst. 8(2): 154-177 (2005)
41EEEamonn J. Keogh: Guest Editorial. Machine Learning 58(2-3): 103-105 (2005)
2004
40EEEamonn J. Keogh, Jessica Lin, Stefano Lonardi, Bill Yuan-chi Chiu: We Have Seen the Future, and It Is Symbolic. ACSW Frontiers 2004: 83
39EEJessica Lin, Michail Vlachos, Eamonn J. Keogh, Dimitrios Gunopulos: Iterative Incremental Clustering of Time Series. EDBT 2004: 106-122
38EEThemistoklis Palpanas, Michail Vlachos, Eamonn J. Keogh, Dimitrios Gunopulos, Wagner Truppel: Online Amnesic Approximation of Streaming Time Series. ICDE 2004: 338-349
37EEEamonn J. Keogh, Stefano Lonardi, Chotirat (Ann) Ratanamahatana: Towards parameter-free data mining. KDD 2004: 206-215
36EEJessica Lin, Eamonn J. Keogh, Stefano Lonardi, Jeffrey P. Lankford, Donna M. Nystrom: Visually mining and monitoring massive time series. KDD 2004: 460-469
35EEChotirat (Ann) Ratanamahatana, Eamonn J. Keogh: Making Time-Series Classification More Accurate Using Learned Constraints. SDM 2004
34EEJessica Lin, Eamonn J. Keogh, Stefano Lonardi, Jeffrey P. Lankford, Donna M. Nystrom: VizTree: a Tool for Visually Mining and Monitoring Massive Time Series Databases. VLDB 2004: 1269-1272
33EEEamonn J. Keogh, Themis Palpanas, Victor B. Zordan, Dimitrios Gunopulos, Marc Cardle: Indexing Large Human-Motion Databases. VLDB 2004: 780-791
32EEJiyuan An, Yi-Ping Phoebe Chen, Eamonn J. Keogh: A Grid-Based Index Method for Time Warping Distance. WAIM 2004: 65-75
2003
31EEJessica Lin, Eamonn J. Keogh, Stefano Lonardi, Bill Yuan-chi Chiu: A symbolic representation of time series, with implications for streaming algorithms. DMKD 2003: 2-11
30EEJessica Lin, Eamonn J. Keogh, Wagner Truppel: Clustering of streaming time series is meaningless. DMKD 2003: 56-65
29 Jessica Lin, Eamonn J. Keogh, Wagner Truppel: (Not) Finding Rules in Time Series: A Surprising Result with Implications for Previous and Future Research. IC-AI 2003: 55-61
28EEEamonn J. Keogh, Jessica Lin, Wagner Truppel: Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research. ICDM 2003: 115-122
27EEJiyuan An, Hanxiong Chen, Kazutaka Furuse, Nobuo Ohbo, Eamonn J. Keogh: Grid-Based Indexing for Large Time Series Databases. IDEAL 2003: 614-621
26EEMichail Vlachos, Marios Hadjieleftheriou, Dimitrios Gunopulos, Eamonn J. Keogh: Indexing multi-dimensional time-series with support for multiple distance measures. KDD 2003: 216-225
25EEBill Yuan-chi Chiu, Eamonn J. Keogh, Stefano Lonardi: Probabilistic discovery of time series motifs. KDD 2003: 493-498
24EEEamonn J. Keogh: Efficiently Finding Arbitrarily Scaled Patterns in Massive Time Series Databases. PKDD 2003: 253-265
23 Eamonn J. Keogh: A Gentle Introduction to Machine Learning and Data Mining for the Database Community. SBBD 2003: 2
22EEEamonn J. Keogh, Shruti Kasetty: On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. Data Min. Knowl. Discov. 7(4): 349-371 (2003)
2002
21EEEamonn J. Keogh, Harry Hochheiser, Ben Shneiderman: An Augmented Visual Query Mechanism for Finding Patterns in Time Series Data. FQAS 2002: 240-250
20EEPranav Patel, Eamonn J. Keogh, Jessica Lin, Stefano Lonardi: Mining Motifs in Massive Time Series Databases. ICDM 2002: 370-377
19EEEamonn J. Keogh, Shruti Kasetty: On the need for time series data mining benchmarks: a survey and empirical demonstration. KDD 2002: 102-111
18EEEamonn J. Keogh, Stefano Lonardi, Bill Yuan-chi Chiu: Finding surprising patterns in a time series database in linear time and space. KDD 2002: 550-556
17 Eamonn J. Keogh: Indexing and Mining Time Series. SBBD 2002: 9
16EESelina Chu, Eamonn J. Keogh, David Hart, Michael J. Pazzani: Iterative Deepening Dynamic Time Warping for Time Series. SDM 2002
15EEEamonn J. Keogh: Exact Indexing of Dynamic Time Warping. VLDB 2002: 406-417
14EEKaushik Chakrabarti, Eamonn J. Keogh, Sharad Mehrotra, Michael J. Pazzani: Locally adaptive dimensionality reduction for indexing large time series databases. ACM Trans. Database Syst. 27(2): 188-228 (2002)
13EEEamonn J. Keogh, Michael J. Pazzani: Learning the Structure of Augmented Bayesian Classifiers. International Journal on Artificial Intelligence Tools 11(4): 587-601 (2002)
2001
12EEEamonn J. Keogh, Selina Chu, David Hart, Michael J. Pazzani: An Online Algorithm for Segmenting Time Series. ICDM 2001: 289-296
11EEEamonn J. Keogh, Selina Chu, Michael J. Pazzani: Ensemble-index: a new approach to indexing large databases. KDD 2001: 117-125
10EEEamonn J. Keogh, Kaushik Chakrabarti, Sharad Mehrotra, Michael J. Pazzani: Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases. SIGMOD Conference 2001: 151-162
9EEEamonn J. Keogh, Kaushik Chakrabarti, Michael J. Pazzani, Sharad Mehrotra: Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. Knowl. Inf. Syst. 3(3): 263-286 (2001)
2000
8EEEamonn J. Keogh, Michael J. Pazzani: Scaling up dynamic time warping for datamining applications. KDD 2000: 285-289
7 Eamonn J. Keogh, Michael J. Pazzani: A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases. PAKDD 2000: 122-133
1999
6 Eamonn J. Keogh, Michael J. Pazzani: Scaling up Dynamic Time Warping to Massive Dataset. PKDD 1999: 1-11
5EEEamonn J. Keogh, Michael J. Pazzani: Relevance Feedback Retrieval of Time Series Data. SIGIR 1999: 183-190
4EEEamonn J. Keogh, Michael J. Pazzani: An Indexing Scheme for Fast Similarity Search in Large Time Series Databases. SSDBM 1999: 56-67
1998
3 Eamonn J. Keogh, Michael J. Pazzani: An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback. KDD 1998: 239-243
1997
2EEEamonn J. Keogh: Fast Similarity Search in the Presence of Longitudinal Scaling in Time Series Databases. ICTAI 1997: 578-584
1 Eamonn J. Keogh, Padhraic Smyth: A Probabilistic Approach to Fast Pattern Matching in Time Series Databases. KDD 1997: 24-30

Coauthor Index

1Russ Abbott (Russell J. Abbott) [85]
2Jiyuan An [27] [32]
3Aris Anagnostopoulos [74]
4Mafruz Zaman Ashrafi [87]
5Anthony J. Bagnall [54] [69]
6Petko Bakalov [56] [97]
7Edna Barros [82]
8David Bell [87]
9Yingyi Bu [90]
10Longbing Cao [87]
11Marc Cardle [33]
12Kaushik Chakrabarti [9] [10] [14]
13Hanxiong Chen [27]
14Yi-Ping Phoebe Chen [32]
15Bill Yuan-chi Chiu [18] [25] [31] [40] [67] [94]
16Selina Chu [11] [12] [16]
17Gautam Das [46]
18Dennis DeCoste [81]
19Hui Ding [100]
20Eugene Dubossarsky [87]
21Ada Wai-Chee Fu [48] [59] [62] [65] [68] [83] [90] [98]
22Kazutaka Furuse [27]
23Ann Gordon-Ross [82]
24Warwick Graco [87]
25Dimitrios Gunopulos [26] [33] [38] [39] [46] [47] [55] [66] [103]
26Marios Hadjieleftheriou [26] [47] [56] [66] [74]
27John Handley [76] [89]
28David Hart [12] [16]
29Helga Van Herle [61] [64] [65] [68] [85] [86]
30Harry Hochheiser [21]
31Wendy L. Hodges [92] [102]
32Gareth J. Janacek [69]
33Kin Fai Kan [95]
34Shashwati Kasetty [109]
35Shruti Kasetty [19] [22]
36Nitin Kumar [49] [50] [64]
37Rolf Lakämper [53] [60]
38Jeffrey P. Lankford [34] [36]
39Longin Jan Latecki [53] [60]
40Leo Yung Hang Lau [48] [98]
41Jia-Jin Le [55]
42Dah-Jye Lee [79] [99]
43Sang-Hee Lee [70] [86] [89]
44Oscar Tat-Wing Leung [83] [90]
45Jessica Lin [20] [28] [29] [30] [31] [34] [36] [39] [40] [42] [45] [46] [55] [62] [65] [67] [68] [72] [83] [86] [88]
46Jian-Wei Liu [55]
47Venkata Nishanth Lolla [49] [50] [64]
48Stefano Lonardi [18] [20] [25] [31] [34] [36] [37] [40] [44] [45] [49] [50] [54] [59] [64] [67] [69] [77] [88] [89]
49Agenor Mafra-Neto [61] [85] [91]
50Nathaniel Martin [76]
51Jose Medina [94]
52Vasileios Megalooikonomou (Vasilis Megalooikonomou) [53] [60]
53Sharad Mehrotra [9] [10] [14]
54Sam Meshkin [90]
55Donna M. Nystrom [34] [36]
56Nobuo Ohbo [27]
57Themis Palpanas (Themistoklis Palpanas) [33] [38] [103]
58Pranav Patel [20]
59Michael J. Pazzani [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [16]
60Jian Pei [90]
61Chotirat (Ann) Ratanamahatana (Chotirat Ann Ratanamahatana) [35] [37] [43] [46] [48] [49] [50] [51] [53] [54] [57] [60] [63] [64] [69] [75] [89] [98]
62Umaa Rebbapragada [96] [101]
63Peter Scheuermann [100]
64Christian R. Shelton [75] [108]
65Jin Shieh [77] [107]
66Ben Shneiderman [21]
67Scott Sirowy [77]
68Padhraic Smyth [1]
69Candice Stafford [109]
70Tong Sun [76]
71Bahar Taneri [87] [93]
72David Taniar [87]
73Goce Trajcevski [100]
74Wagner Truppel [28] [29] [30] [38]
75Vassilis J. Tsotras [56] [97]
76Ken Ueno [79] [99]
77Frank Vahid [82]
78Pablo Viana [82]
79Michail Vlachos [26] [38] [39] [46] [47] [55] [66] [70] [74] [87] [93] [103]
80Gregory P. Walker [109]
81Qiang Wang [53] [60]
82Xiaoyue Wang [100] [106] [108] [109]
83Li Wei [44] [49] [61] [64] [70] [73] [75] [76] [77] [78] [85] [88] [89] [91] [92] [102] [104]
84Raymond Chi-Wing Wong [98]
85Xiaopeng Xi [44] [70] [75] [77] [78] [79] [91] [92] [99] [102] [104]
86Qiang Yang [87]
87Dragomir Yankov [59] [80] [81] [92] [94] [95] [96] [101] [102] [106]
88Lexiang Ye [106] [108]
89Melissa Yoder [104]
90Philip S. Yu [74] [87] [93]
91Shou-Jian Yu [55]
92Chengqi Zhang [87]
93Ning Zhong [87]
94Victor B. Zordan [33] [94]

Colors in the list of coauthors

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