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Sanjay Purushotham <[log in to unmask]>
Sun, 19 Apr 2020 17:25:41 -0400
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CFP Webpage: https://kdd-milets.github.io/milets2020/#call

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KDD Mining and Learning from Time Series (MiLeTS) 2020

Aug 24th, 2020 - San Diego, California, USA

https://kdd-milets.github.io/milets2020/

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KEY DATES

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Paper Submission Deadline: May 20th, 2020, 11:59PM Alofi Time

Author Notification: June 15th, 2020

Camera Ready Version: July 2nd, 2020

Workshop: August 24th, 2020

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MiLETS is the premier KDD workshop on Mining and Learning from Time Series.

Time series data are ubiquitous. In domains as diverse as finance,
entertainment, transportation, and health care, we observe a fundamental
shift away from parsimonious, infrequent measurement to nearly continuous
monitoring and recording. Rapid advances in diverse sensing technologies,
ranging from remote sensors to wearables and social sensing, are generating
rapid growth in the size and complexity of time series archives. Thus,
although time series analysis has been studied extensively, its importance
only continues to grow. What is more, modern time series data pose
significant challenges to existing techniques (e.g., irregular sampling in
hospital records and spatiotemporal structure in climate data). Finally,
time series mining research is challenging and rewarding because it bridges
a variety of disciplines and demands interdisciplinary solutions. Now is
the time to discuss the next generation of temporal mining algorithms. 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.

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

·       Time series pattern mining and detection, representation, searching
and indexing, classification, clustering, prediction, forecasting, and rule
mining.

·       BIG time series data.

·       Hardware acceleration techniques using GPUs, FPGAs and special
processors.

·       Online, high-speed learning and mining from streaming time series.

·       Uncertain time series mining.

·       Privacy preserving time series mining and learning.

·       Time series that are multivariate, high-dimensional, heterogeneous,
etc., or that possess other atypical properties.

·       Time series with special structure: spatiotemporal (e.g., wind
patterns at different locations), relational (e.g., patients with similar
diseases), hierarchical, etc.

·       Time series with sparse or irregular sampling, non-random missing
values, and special types of measurement noise or bias.

·       Time series analysis using less traditional approaches, such as
deep learning and subspace clustering.

·       Applications to high impact or relatively new time series domains,
such as health and medicine, road traffic, and air quality.

·       New, open, or unsolved problems in time series analysis and mining.

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Submission Guidelines

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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. Submissions are
strongly recommended to be no more than 4 pages, excluding references or
supplementary materials (all in a single pdf). The appropriateness of using
additional pages over the recommended length will be judged by reviewers.
All submissions must be in pdf format using the workshop template (latex,
word). Submissions will be managed via the MiLeTS 2020 EasyChair website.

*Note on open problem submissions:* 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.

*COVID-19 Time Series Analysis Special Track:* The COVID-19 pandemic is
impacting almost everyone worldwide and is expected to have life-altering
short and long-term effects. There are many potential applications of time
series analysis and mining that can contribute to the understanding of this
pandemic. We encourage the submission of high-quality manuscripts
describing original problems, time-series datasets, and novel solutions for
time series analysis and forecasting of COVID-19.

The review process is single-round and double-blind (submission files have
to be anonymized). Concurrent submissions to other journals and conferences
are acceptable. Accepted papers will be presented as posters during the
workshop and list on the website. Besides, a small number of accepted
papers will be selected to be presented as contributed talks.

Any questions may be directed to the workshop e-mail address:
[log in to unmask]

-----------------

KEY DATES

-----------------

Paper Submission Deadline: May 20th, 2020, 11:59PM Alofi Time

Author Notification: June 15th, 2020

Camera Ready Version: July 2nd, 2020

Workshop: August 24th, 2020

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Organizing Committee

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Sanjay Purushotham

University of Maryland, Baltimore County



Yaguang Li

Google



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Steering Committee

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Eamonn Keogh

University of California Riverside



Yan Liu

University of Southern California



Abdullah Mueen

University of New Mexico



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Contact:

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Any questions may be directed to the workshop e-mail address:
[log in to unmask]


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