Jessica Lin
Full Publication List By Year

2012

  1. Yuan Li, Jessica Lin, and Tim Oates. 2012. Visualizing variable-length time series motifs. In Proceedings of the 2012 SIAM International Conference on Data Mining. Anaheim, CA. Apr 26-28. Pages 895-906.
  2. Jessica Lin, Rohan Khade, and Yuan Li. 2012. Rotation-invariant similarity in time series using Bag-of-Patterns representation. Journal of Intelligent Information Systems. Vol 39, Issue 2. Pages 287-315.

  3. Chun-Kit Ngan, Alexander Brodsky, and Jessica Lin. 2012. An event-based service framework for learning, querying and monitoring multivariate time series. Lecture Notes in Business Information Processing Series, Vol 0102. To Appear.
  4. Chun-Kit Ngan, Alexander Brodsky, and Jessica Lin. 2012. Multi-event decision making over multivariate time series. International Journal of Information and Decision Sciences. To Appear.
  5. Chun-Kit Ngan, Alexander Brodsky, and Jessica Lin. 2012. R-Checkpoint algorithm for multi-event decision making over multivariate time series. In Proceedings of the 16th International Conference on Decision Support Systems. To Appear

  6. Jessica Lin, Sheri Williamson, Kirk Borne, and David DeBarr. 2012. Pattern recognition in time series. Advances in Machine Learning and Data Mining for Astronomy. Eds. Kamal, A., Srivastava, A., Way, M., and Scargle, J. Chapman & Hall. To Appear.

  7. Guido Cervone, Jessica Lin, and Nigel Waters (Eds.). 2012. Spatio-Temporal Data Mining for Geoinformatics: Methods and Applications. Springer-Verlag. To be released in 2012.

2011

  1. Chun-Kit Ngan, Alexander Brodsky, and Jessica Lin. 2011. A service framework for learning, querying, and monitoring multivariate time series. In Proceedings of the 13th International Conference on Enterprise Information Systems. Pages 92-101.
    • Best Student Paper Award

  2. Chun-Kit Ngan, Alexander Brodsky, and Jessica Lin. 2011. Multi-event decision making over multivariate time series. In Proceedings of EWG-DSS London 2011 Workshop on Decision Systems. Birbeck, UK. June 23-34.

  3. Guido Cervone, Jessica Lin, Pasquale Franzese. 2011. Addressing wind direction uncertainty in source estimation through dynamic time warping, In Proceedings of the 91st American Meteorological Society Annual Meeting, Computational Intelligence Methods and Their Applications to Environmental Science, Seattle, WA, January 2011.

  4. Jessica Lin, Guido Cervone, and Nigel Waters. 2011. DMGI 2010 workshop report: The First ACM SIGSPATIAL International Workshop on Data Mining for Geoinformatics. SIGSPATIAL Special 3(1): 6-7.

  5. Chun-Kit Ngan, Alexander Brodsky, and Jessica Lin. 2011. An event-based service framework for learning, querying, and monitoring multivariate time series. Lecture Notes in Business Information Processing. Springer-Verlag. To Appear.

2010

  1. Jessica Lin, Guido Cervone, and Nigel Waters (Eds.). 2010. Proceedings of the 1st ACM SIGSPATIAL International Workshop on Data Mining for Geoinformatics. ACM, New York, NY, USA.

  2. Jessica Lin and Yuan Li. 2010. Finding approximate frequent patterns in streaming medical data. In Proceedings of the 23rd IEEE International Symposium on Computer-Based Medical Systems. IEEE Computer Society, Washington DC, USA.

  3. Chun-Kit Ngan, Alexander Brodsky, and Jessica Lin. 2010. Decisions on multivariate time series: combining domain knowledge with utility maximization. In Supplemental Proceedings of the 15th International Conference on Decision Support Systems. July 7-10. Lisbon, Portugal.

  4. Jessica Lin, Guido Cervone, and Pasquale Franzese. 2010. Assessment in error in air quality models using dynamic time warping. In Proceedings of the 1st International Workshop on Data Mining for Geoinformatics, in conjunction with SIGSPATIAL GIS 2010. San Jose, CA. Nov 2, 2010. Pages 38-44.

  5. Yuan Li and Jessica Lin. 2010. Approximate variable-length time series motif discovery using grammar inference. In Proceedings of the 10th International Workshop on Multimedia Data Mining, in conjunction with SIGKDD 2010. ACM, New York, NY, USA. Pages 1-9.

  6. Chotirat Ann Ratanamahatana, Jessica Lin, Dimitrios Gunopulos, Eamonn Keogh, Michail Vlachos, and Gautam Das. 2010. Mining time series data. Data Mining and Knowledge Discovery Handbook 2010, 2nd Edition. Eds. Oded Maimon, Lior Rokach. Springer. Pages 1049-1077

2009

  1. Jessica Lin and Yuan Li. 2009. Finding structural similarity in time series data using Bag-of-Patterns representation. In Proceedings of the 21st International Conference on Scientific and Statistical Database Management (SSDBM 2009), Marianne Winslett (Ed.). Springer-Verlag, Berlin, Heidelberg, Pages 461-477.

  2. Jessica Lin and Yuan Li. 2009. Finding structurally different medical data. In Proceedings of the 22nd IEEE International Symposium on Computer-Based Medical Systems. IEEE Computer Society, Washington DC, USA. Pages 1-8.

2008

  1. Jessica Lin , David Etter and Dave DeBarr. 2008. Exact and approximate reverse nearest neighbor search in multimedia data. In Proceedings of the SIAM International Conference on Data Mining. Pages 656-667.

  2. Eiman Al-Shammari and Jessica Lin. 2008. A new Arabic stemming algorithm. In Proceedings of the 2008 ISCA Workshop on Experimental Linguistics. Athens, Greece. Aug 25-27.

  3. Eiman Al-Shammari, Jessica Lin. 2008. A novel Arabic lemmatization algorithm. In Proceedings of the 2nd SIGIR Workshop on Analytics for Noisy Unstructured Text Data. Singapore, July 24-27, 2008. p. 113-118.

  4. Eiman Al-Shammari and Jessica Lin. 2008. Towards an error-free Arabic stemming. In Proceeding of the 2nd ACM workshop on Improving non English web searching (INEWS). ACM, New York, NY, USA. Pages 9-16.

  5. Eiman Al-Shammari and Jessica Lin. 2008. Automated Corpora Creation Using a Novel Arabic Stemming Algorithm. In Proceedings of the 2008 International Symposium on Using Corpora in Contrastive and Translation Studies (UCCTS). Hanzhou, China. Sept 25-27.

2007

  1. Jessica Lin, Eamonn Keogh, Li Wei, and Stefano Lonardi. 2007. Experiencing SAX: a novel symbolic representation of time series. Data Mining and Knowledge Discovery, 15(2): 107-144.
    [Citations]

  2. Stefano Lonardi, Jessica Lin, Eamonn Keogh, and Bill Chiu. 2007. Efficient discovery of unusual patterns in time series. New Generation Computing, 25(1): 61-93.

  3. David DeBarr and Jessica Lin. 2007. Time series classification challenge experiments. In Proceedings of the Workshop and Challenge on Time Series Classification, at the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Jose, CA. Aug 12-15.

  4. Jessica Lin, Michail Vlachos, Eamonn Keogh, and Dimitrios Gunopulos. 2007. Multi-resolution time series clustering and application to images. Multimedia Data Mining and Knowledge Discovery, Eds. Valery Al Petrushin and Latifur Khan. Springer. Pages 58-79.

2006

  1. Eamonn Keogh, Jessica Lin, Sang-Hee Lee, and Helga Van Herle. 2006. Finding the most unusual time series subsequence: algorithms and applications. Knowledge and Information Systems, 11(1): 1-27.
    [Citations]

  2. Eamonn Keogh, Jessica Lin, Ada Fu & Helga Van Herie. 2006. Finding unusual medical time series subsequences: algorithms and applications. IEEE Transactions on Information Technology in Biomedicine, 10(3): 429-439.

  3. Jessica Lin and Eamonn Keogh. 2006. Group SAX: Extending the notion of contrast sets to time series and multimedia data. In Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases. Berlin, Germany. Sept 18-22. Pages 284-296. Lecture Notes in Computer Science, Springer.

  4. Ada Fu, Oscar Leung, Eamonn Keogh, and Jessica Lin. 2006. Finding time series discords based on Haar transform. In Proceedings of the 2nd International Conference on Advanced Data Mining and Applications. Xi’an, China. Aug 14-18. Pages 31-41.
    [Citations]

2005

  1. Jessica Lin, Eamonn Keogh, and Stefano Lonardi. 2005. Visualizing and discovering non-trivial patterns in large time series databases. Information Visualization, 4(2): 61-82.
    [Citations]

  2. Eamonn Keogh and Jessica Lin. 2005. Clustering of time series subsequences is meaningless: implications for previous and future research. Knowledge and Information Systems, 8(2): 154-177.
    [Citations]

  3. Eamonn Keogh, Jessica Lin, and Ada Fu. 2005. HOT SAX: Efficiently finding the most unusual time series subsequence. In Proceedings of the 5th IEEE International Conference on Data Mining (ICDM). Nov 27-30. Houston, TX. Pages 226-233. IEEE Computer Society.
    [Citations]

  4. Jessica Lin, Eamonn Keogh, Ada Fu, and Helga Van Herie. 2005. Approximations to magic: finding unusual medical time series. In Proceedings of the 18th International Symposium on Computer-Based Medical Systems. IEEE Computer Society, Washington DC, USA. Pages 329-334.
    [Citations]

  5. Jessica Lin, Michail Vlachos, Eamonn Keogh, Dimitrios Gunopulos, Jianwei Liu, Shoujian Yu, and Jiajin Le. 2005. A MPAA-based iterative clustering algorithm augmented by nearest neighbors search for time-series data streams. In Proceedings of the 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-05). Lecture Notes in Computer Science, Springer. Pages 333-342.

  6. Chotirat Ann Ratanamahatana, Jessica Lin, Dimitrios Gunopulos, Eamonn Keogh, Michail Vlachos, and Gautam Das. 2005. Mining time series data. Data Mining and Knowledge Discovery Handbook 2005. Eds. Oded Maimon, Lior Rokach. Springer. Pages 1069-1103.

2004

  1. Jessica Lin, Eamonn Keogh, 2004, Finding or not finding rules in time series. Applications of Artificial Intelligence in Finance and Economics (Advances in Econometrics, Volume 19), pp.175-201.

  2. Jessica Lin, Eamonn Keogh, Stefano Lonardi, Jeffrey P. Lankford, and Donna M. Nystrom. 2004. Visually mining and monitoring massive time series. In Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '04). ACM, New York, NY, USA, 460-469.
    [Citations]

  3. Jessica Lin, Eamonn Keogh, Stefano Lonardi, Jeffrey P. Lankford, and Daonna M. Nystrom. 2004. VizTree: a tool for visually mining and monitoring massive time series databases. In Proceedings of the 30th International Conference on Very large Data Bases - Volume 30 (VLDB '04). VLDB Endowment. Pages 1269-1272.

  4. Jessica Lin, Michail Vlachos, Eamonn Keogh, and Dimitrios Gunopulos. 2004. Iterative incremental clustering of time series. In Proceedings of the IX Conference on Extending Database Technology (EDBT). Lecture Notes in Computer Science, Springer. Pages 106-122.
    [Citations]

  5. Eamonn Keogh, Jessica Lin, Stefano Lonardi, and Bill Chiu. 2004. We have seen the future, and it is symbolic. In Proceedings of the Second Workshop on Australasian Information Security, Data Mining and Web Intelligence, and Software Internationalisation - Volume 32 (ACSW Frontiers '04), J. Hogan, P. Montague, M. Purvis, and C. Steketee (Eds.), Vol. 32. Australian Computer Society, Inc., Darlinghurst, Australia, Australia, Page 83.

2003

  1. Eamonn Keogh, Jessica Lin, and Wagner 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 (ICDM). IEEE Computer Society. Pages 115-122.
    [Citations]

  2. Jessica Lin, Eamonn Keogh, and Wagner Truppel. 2003. (Not) Finding rules in time series: a surprising result with implications for previous and future research. In Proceedings of the 2003 International Conference on Artificial Intelligence. Las Vegas, NV. June 23-26. Pages 55-61.

  3. Jessica Lin, Vlachos, M, Keogh, E., & Gunopulos, D. 2003. Multi-resolution k-means clustering of time series and application to images. In Proceedings of the 4th SIGKDD Workshop on Multimedia Data Mining, in conjunction with SIGKDD 2003. 10 pages.

  4. Jessica Lin, Eamonn Keogh, Stefano Lonardi, and Bill Chiu. 2003. A symbolic representation of time series, with implication for streaming algorithms. In Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD '03). ACM, New York, NY, USA. Pages 2-11.
    [Citations]

  5. Jessica Lin, Eamonn Keogh, and Wagner Truppel. 2003. Clustering of streaming time series is meaningless. In Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD '03). ACM, New York, NY, USA. Pages 2-11.
    [Citations]

  6. Jessica Lin, Michail Vlachos, Eamonn Keogh, and Dimitrios Gunopulos. 2003. A wavelet-based anytime algorithm for k-means clustering of time series. In Proceedings of the Workshop on Clustering High Dimensional Data and Its Applications, at the 3rd SIAM International Conference on Data Mining. San Francisco, CA. May 3, 2003. 10 pages.
    [Citations]

  7. Jessica Lin and Gunopulos, D. 2003. Dimensionality reduction by random projection and latent semantic indexing. In Proceedings of the Text Mining Workshop, at the 3rd SIAM International Conference on Data Mining. San Francisco, CA. May 3, 2003. 10 pages.
    [Citations]

2002

  1. Pranav Patel, Eamonn Keogh, Jessica Lin, and Stefano Lonardi. 2002. Mining motifs in massive time series databases. In Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM '02). IEEE Computer Society, Washington, DC, USA, 370-377.
    [Citations]

  2. Jessica Lin, Eamonn Keogh, Pranav Patel, and Stefano Lonardi. 2002. Finding motifs in time series. In Proceedings of the 2nd Workshop on Temporal Data Mining, at the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Edmonton, Alberta, Canada. July 23-26, 2002. 11 pages.
    [Citations]