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ODD 2016 : KDD 2016 Workshop on Outlier Definition, Detection, and Description on Demand

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Link: http://outlier-analytics.org/odd16kdd/
 
When Aug 14, 2016 - Aug 14, 2016
Where San Francisco
Submission Deadline May 27, 2016
Notification Due Jun 16, 2016
Final Version Due Jul 1, 2016
Categories    data mining   machine learning   anomaly mining   outlier analysis
 

Call For Papers


CFP: ACM SIGKDD 2016 Workshop on
Outlier Definition, Detection, and Description on Demand

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CALL FOR RESEARCH PAPERS
ODD 4.0 @ KDD 2016
Workshop on Outlier Definition, Detection, and Description on Demand
will be held in conjunction with KDD 2016

August 14, 2016 in San Francisco, CA

http://outlier-analytics.org/odd16kdd/
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ODD 4.0 is a half-day workshop, organized in conjunction with ACM SIGKDD 2016.
We follow the successful series of three ODD Workshops that have been organized at ACM KDD 2015, KDD 2014, and KDD 2013.

The main goal of the ODD workshop is to bring together academics, industry and government researchers and practitioners to discuss and reflect on outlier mining challenges.

The ODD (2013) Workshop focused on outlier detection and description, with particular emphasis on descriptive methods that could help make sense of the detected outliers. Next, ODD^2 (2014) extended the focus areas to outlier detection and description under data diversity, with emphasis on challenges associated with mining outliers in heterogeneous data environments (graphs, text, streams, metadata, etc.). ODDx3 (2015) focused on the translation of real world applications to different outlier definitions, highlighting the challenges associated with the variety of outlier definitions defined in theoretic models and used in a multitude of application domains.

This year, thanks to the feedback of industrial attendees at last year’s ODD workshop, we broaden the scope to industrial challenges (e.g. known from Industry 4.0 initiatives) for on-demand computation, visualization, and verification of outliers in industrial settings. This includes open challenges for (1) online stream outlier mining, (2) real-time visualization of anomalies, and (3) interactive exploration of outlier instances. Overall, ODD 4.0 (2016) aims to increase awareness of the community to the following challenges of outlier mining:

What are the key outlier mining requirements in industry?
How can we define outliers in data streams?
How can online detection and description be supported?
How can applications (e.g. predictive maintenance) steer outlier search?
How can we compute real-time visualizations of outlier models?
How could interaction allow better and more intuitive outlier mining?


CONFIRMED KEYNOTE SPEAKER:
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Jeff Schneider
Engineering Lead at Uber ATC
Professor of Carnegie Mellon University
https://www.linkedin.com/in/jeff-schneider-1593b322

Dr. Jeff Schneider is currently the engineering lead at Uber ATC and an associate research professor at the Carnegie Mellon University's School of Computer Science. He received his PhD in Computer Science from the University of Rochester in 1995. He has over 15 years experience developing, publishing, and applying machine learning algorithms in government, science, and industry. He has dozens of publications and has given numerous invited talks and tutorials on the subject.

Dr. Schneider was the co-founder and CEO of Schenley Park Research,Inc. (SPR), a company dedicated to bringing new machine learning algorithms to industry. Later, he developed a new machine-learning based CNS drug discovery system and spent a two-year sabbatical as the Chief Informatics Officer of a biotech, Psychogenics, to set up and commercialize the system. Through his work at CMU and his commercial and consulting efforts, he has worked with several dozen companies and government agencies including six Fortune 500 companies, and groups from seven other countries.


TOPICS OF INTEREST
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Topics of interests for the proposed workshop include, but are not limited to:

 interleaved detection and description of outliers
 description models for given outliers
 pattern and local information based outlier description
 ensemble methods for outlier detection and description
 novel outlier models for complex anomalies
 outlier detection methods for complex anomalies in heterogeneous datasets
 multi-view outlier ensembles over multiple data sources
 comparative studies on outlier detection
 identification of outlier rules
 supervised and unsupervised outlier detection
 statistical and information-theoretic outlier detection and description
 distance-based models for outlier ranking
 density-based models for local outlier ranking
 subspace outlier mining in high dimensional data
 community outlier mining in graph data
 anytime outlier mining in stream data
 contrast mining and causality analysis
 visualizations for outlier mining results
 visual analytics for interactive detection and evaluation of outliers
 human-in-the-loop modeling and learning

Application areas of interest include, but are not limited to:

 Fraud detection, and data logs
 Health surveillance, and other sensor databases
 Video surveillance, and other streaming databases
 Customer analysis, and other transactional data sources
 Process logs, and other sequential or ordered data
 Social networks, and other graph databases

We encourage submissions describing innovative work in related fields that address the issue of data diversity in outlier mining.


SUBMISSION GUIDELINES
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We invite submission of unpublished original research papers that are not under review elsewhere. All papers will be peer reviewed. If accepted, at least one of the authors must attend the workshop to present their work. The submitted papers must be written in English and formatted according to the ACM Proceedings Template (Tighter Alternate style) available at:
http://www.acm.org/sigs/publications/proceedings-templates

The maximum length of papers is 10 pages in this format. We also invite vision papers and descriptions of work-in-progress or case studies on benchmark data as short paper submissions of up to 4 pages.

The papers should be in PDF format and submitted via EasyChair submission site
https://easychair.org/conferences/?conf=odd2016

Note that this year the workshop papers will NOT be archived in the ACM Digital Library.

If you are considering submitting to the workshop and have questions regarding the workshop scope or need further information, please do not hesitate to contact the organizers at odd16kdd (at) outlier-analytics.org.


IMPORTANT DATES
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Submission deadline: May 27, 2016, 23:59 PST
Acceptance notification: Jun 16, 2016, 23:59 PST
Camera-ready deadline: Jul 1, 2016, 23:59 PST
Workshop day: Aug 14, 2016


PROGRAM COMMITTEE (in progress)
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Fabrizio Angiulli (University of Calabria)
James Bailey (University of Melbourne)
Arindam Banerjee (University of Minnesota)
Albert Bifet (Télécom ParisTech)
Petko Bogdanov (SUNY Albany)
Christian Böhm (Ludwig-Maximilians-Universität)
Rajmonda Caceres (MIT)
Varun Chandola (SUNY Buffalo)
Sanjay Chawla (University of Syndey)
Feng Chen (SUNY Albany)
Thomas Dietterich (Oregon State University)
Shobeir Fakhraei (University of Maryland)
Jaakko Hollmén (Aalto University)
Daniel Keim (University of Konstanz)
Arun Maiya (Institute for Defense Analyses)
Julian McAuley (UC San Diego)
Raymond Ng (University of British Columbia)
Lionel Ott (University of Sydney)
Spiros Papadimitriou (Rutgers)
Ambuj Singh (UC Santa Barbara)
Hanghang Tong (Arizona State)
Matthijs van Leeuwen (Universiteit Leiden)
Weng-Keen Wong (Oregon State University)


ORGANIZERS
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Leman Akoglu (Stony Brook University)
Franziska Bell (Uber)
Emmanuel Muller (Hasso-Plattner Institute)
Ted E. Senator


Contact us at:
odd16kdd (at) outlier-analytics.org

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