SIGKDD Workshop on Mining and Learning from Time Series (MiLeTS)

Time series data are ubiquitous. The explosion of new sensing technologies (wearable sensors, satellites, mobile phones, etc.), combined with increasingly cheap and effective storage, is generating an unprecedented and growing amount of time series data in a variety of domains. The volume and complexity of these data present new and significant challenges to existing and even state-of-the-art methods. The focus of MiLeTS workshop is to synergize the research in this area and discuss both new and open problems in time series analysis and mining. The solutions to these problems may be algorithmic, theoretical, statistical, or systems-based in nature. Further, MiLeTS emphasizes applications to high impact or relatively new domains, including but not limited to biology, health and medicine, climate and weather, road traffic, astronomy, and energy.


1st International Workshop on Mining and Learning from Time Series (MiLeTS)
Level 4, Room 5
August 10, 2015
Sydney, Australia

1:30-2:30 PMKEYNOTE: Leveraging Open Spatio-temporal Data for Business Analytics SolutionsChid Apte, IBM Research
2:30-3:10 PMTechnical Session 1: Time-evolving Networks and Graphs
3:10-3:40 PMCoffee Break
3:40-4:40 PMKEYNOTE: Scaling Log-linear Analysis to Datasets with Thousands of VariablesGeoff Webb, Monash University
4:40-5:40 PMTechnical Session 2: Uncertain and Intermittent Time Series
5:40-6:00 PMRoundtable Discussion on Future of Time Series Research

The inaugural MiLeTS workshop will discuss a broad variety of topics related to time series, including:

Keynote Speakers

Chid Apte, Ph.D., Director Mathematical Sciences & Analytics

IBM T.J. Watson Research Center
Yorktown Heights, NY, USA

Leveraging open spatio-temporal data for business analytics solutions
The confluence and availability of large-scale remotely-sensed and locally-instrumented spatio-temporal data will significantly enhance our ability to drive insights for business decision optimization. The technical challenges that need to be addressed for handling this class of data represents new opportunities for time-series mining and learning techniques. I will discuss these challenges, examples of industry applications that will benefit, and the role for underlying big data and high performance computing methods.

Chid Apte is Director of Mathematical Sciences and Analytics in the IBM Research Division, at the Thomas J. Watson Research Center in Yorktown Heights, New York. Chid has over twenty five years of technical experience as a research scientist and leader in the data sciences area. He has worked on several projects in predictive analytics solutions for a wide cross-section of industries and led several projects to develop capabilities for IBM's Information & Analytics product portfolio and advanced analytics solutions for IBM's clients. He received his Ph.D. in Computer Science from Rutgers University, and B. Tech. in Electrical Engineering from the Indian Institute of Technology (Bombay).

Geoff Webb, Ph.D., Research Professor, Faculty of Information Technology

Monash University
Victoria, Australia

Scaling Log-linear Analysis to Datasets with Thousands of Variables
Association discovery is a fundamental data mining task. The primary statistical approach to association discovery between variables is log-linear analysis. Classical approaches to log-linear analysis do not scale beyond about ten variables. By melding the state-of-the-art in statistics, graphical modeling, and data mining research, we have developed efficient and effective algorithms for log-linear analysis, performing in seconds log-linear analysis of datasets with thousands of variables and providing a powerful statistically-sound method for creating compact models of complex high-dimensional multivariate distributions.

Geoff Webb is a leading data scientist. He was editor in chief of the premier data mining journal, Data Mining and Knowledge Discovery from 2005 to 2014. He has been Program Committee Chair of the two top data mining conferences, ACM SIGKDD and IEEE ICDM, as well as General Chair of ICDM. He is the Director of the Monash University Center for Data Science. He is a Technical Advisor to BigML Inc, who are incorporating his best of class association discovery software, Magnum Opus, into their cloud based Machine Learning service. He developed many of the key mechanisms of support-confidence association discovery in the late 1980s. His OPUS search algorithm remains the state-of-the-art in rule search. He pioneered multiple research areas as diverse as black-box user modelling, interactive data analytics and statistically-sound pattern discovery. He has developed many useful machine learning algorithms that are widely deployed. He received the 2013 IEEE Outstanding Service Award, a 2014 Australian Research Council Discovery Outstanding Researcher Award and was elevated to IEEE Fellow earlier this year.

Accepted Papers

Technical Session 1: Time-evolving Networks and Graphs

Xiaodong Liu, Xiangke Liao, Shanshan Li, Jingying Zhang, Lisong Shao, Chenlin Huang and Liquan Xiao
Ayan Acharya, Avijit Saha, Mingyuan Zhou, Joydeep Ghosh and Dean Teffer

Technical Session 2: Uncertain and Intermittent Time Series

Duncan Barrack, James Goulding, Keith Hopcraft, Simon Preston and Gavin Smith
Souhaib Ben Taieb, Raphael Huser, Rob J. Hyndman and Marc G. Genton


Eamonn KeoghUniversity of California Riverside
Yan LiuUniversity of Southern California
Abdullah MueenUniversity of New Mexico
David KaleUniversity of Southern California

Program Committee
Jessica LinGeorge Mason University
Anthony BagnallUniversity of East Anglia
Josif GrabockaUniversity of Hildesheim
Gustavo BatistaUniversity of São Paulo
Mohammad Taha BahadoriUniversity of Southern California
Spiros PapadimitriouRutgers University
Zhenhui LiPenn State University
Gautam DasUniversity of Texas at Arlington
Zeeshan SyedUniversity of Michigan
Francois PetitjeanMonash University


Call for Papers

Key Dates
Submission Deadline:June 11, 2015, 11:59 PM PST
Notification:June 30, 2015, 11:59 PM PST
Camera Ready Deadline:July 10, 2015, 11:59 PM PST
Workshop:August 10, 2015

Submissions should follow the SIGKDD formatting requirements and will be evaluated using the SIGKDD Research Track evaluation criteria. Preference will be given to papers that are reproducible, and authors are encouraged to share their data and code publicly whenever possible.

Note on open problem submissions: In order to promote new and innovative research on time series, we plan to accept a small number of high quality manuscripts describing open problems in time series analysis and mining. Such papers should provide a clear, detailed description and analysis of a new or open problem that poses a significant challenge to existing techniques, as well as a thorough empirical investigation demonstrating that current methods are insufficient.

Submissions will be managed via the MiLeTS 2015 EasyChair website.