|13:15||Welcome & Remarks|
Moving from Anomalies to Known Phenomena
by Jeff Schneider
Research talks (15+5 minutes each)
Research talks (15+5 minutes each)
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?
We are proud to have Dr. Jeff Schneider as our keynote speaker.
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.
The keynote will be 55 minutes long, including questions.
|Notification to Authors||
|Workshop day||August 14, 2016|
- Interleaved detection and description of outliers
- Description models for given outliers
- Pattern and local information based outlier description
- Subspace outliers, feature selection, and space transformations
- Ensemble methods for anomaly detection and description
- Descriptive local outlier ranking
- Identification of outlier rules
- Finding intensional knowledge
- Contextual and community outliers
- Human-in-the-loop modeling and learning
- Visualization techniques for interactive exploration of outliers
- Comparative studies on outlier description
- Related research fields
- Formal outlier mining models
- Supervised, semi-supervised, and unsupervised models
- Statistical models
- Distance-based models
- Density-based models
- Spectral models
- Constraint-based models
- Ensemble models
- Outlier mining for complex databases
- Graph data (e.g. community outliers)
- Spatio-temporal data
- Time series and sequential data
- Online processing of stream data
- Scalability to high dimensional data
- Applications of outlier detection and description
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.
Please submit your papers at the EasyChair Submission Link.
Submission is closed.
- 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 (University of Munich)
- 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)
odd16kdd (at) outlier-analytics.org