Workshop Program [Slides]
9:30 | Welcome & Remarks |
9:45 | Keynote Presentation
(abstract)
'Graph OLAP, Anomaly and Query-based Outlier Detection' by Xifeng Yan |
10:30 | Coffee |
11:00 |
Research talks (15+5 minutes each)
|
12:30 | Lunch (on your own) |
2:05 | Re-welcome |
2:15 | Keynote Presentation
(abstract) (slides)
'Identifying Rare Class in Absence of True Labels: Application to Monitoring Forest Fires from Satellite data' by Vipin Kumar |
3:00 | Coffee |
3:30 | Panel
'What is an Anomaly?' by Tiberio Caetano, Tina Eliassi-Rad, Vipin Kumar, Ted Senator, Jimeng Sun Here is the list of discussion questions. |
4:45 |
Research talks (15+5 minutes each)
|
5:45 | Discussion & Closing |
ODDx3 is a full-day workshop, organized in conjunction with ACM SIGKDD 2015.
Outlier Definition, Detection, and Description
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. Specifically,
the 1st ODD
workshop (2013) focused on
outlier detection and description, with particular emphasis on
descriptive methods that could help make sense of the detected
outliers. The 2nd
ODD^2 workshop (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.).
This year, we
broaden the scope to also include the translation of real world
applications to different
outlier definitions. Our goal is to highlight challenges associated
with (1) outlier mining by
new theoretic models and efficient algorithms, (2) translating real
world problems to one/multiple
of these definitions, and (3) comparing these definitions in their
detection quality for unknown
outlier instances. In all, the 3rd ODDx3 aims to increase awareness of
the community to the following
challenges of outlier mining:
- What is an outlier/anomaly?
- How can we define an anomaly in heterogeneous data environments?
- How do different definitions translate to real world applications (spam, fraud, etc.)?
- How can real world scenarios help shape new anomaly definitions?
- How can we build descriptive detection methods?
- How could data visualization aid anomaly mining?
Invited Keynote Speakers
We are proud to have Vipin Kumar and Xifeng Yan as our keynote speakers.
Each keynote will be 45 minutes long, including questions.
ODDx3 Panel: "What is an anomaly?"
A panel consisting of researchers from both academia and industry with expertise/experience in outlier mining and fraud detection (60 minutes, including 5 minute presentation by each panelist followed by Q&A and discussions)Panelists
- Tiberio Caetano (Ambiata, Chief Scientist)
- Tina Eliassi-Rad (Rutgers, Professor) (malware & fraud detection)
- Vipin Kumar (University of Minnesota)
- Ted E. Senator (Leidos, Technical Fellow) (insider threat detection)
- Jimeng Sun (Georgia Tech., Professor) (outliers in medical data)
Important Dates
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Camera-ready Deadline | July 19, 2015, 23:59 PST |
Workshop day | August 10, 2015 |
Call for Papers
Topics of interests for the 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
- 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
Submission Guidelines
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 the work. The submitted papers must be written in English and formatted according to the ACM Proceedings Template (Tighter Alternate style).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 the following EasyChair submission site.
Accepted papers will be included in the KDD 2015 Digital Proceedings, and made available in the ACM Digital Library.
Program Committee
- Fabrizio Angiulli, University of Calabria
- James Bailey, University of Melbourne
- Albert Bifet, University of Waikato
- Petko Bogdanov, SUNY Albany
- Christian Böhm, LMU
- Rajmonda Caceres, MIT Lincoln Laboratory
- Sanjay Chawla, University of Syndey
- Feng Chen, SUNY Albany
- Tina Eliassi-Rad, Rutgers University
- Christos Faloutsos, Carnegie Mellon
- Jing Gao, University of Buffalo
- Arun Maiya, Institute for Defense Analyses
- Daniel B. Neill, Carnegie Mellon University
- Raymond Ng, University of British Columbia
- Spiros Papadimitriou, Rutgers University
- Mykola Pechenizkiy, Eindhoven U. of Tech.
- Naren Ramakrishnan, Virginia Tech
- Fabio Ramos, University of Sydney
- Joerg Sander, University of Alberta
- Oliver Schulte, Simon Fraser University
- Ambuj Singh, UC Santa Barbara
Organizers
- Leman Akoglu (Stony Brook University)
- Sanjay Chawla (University of Sydney)
- Emmanuel Müller (Karlsruhe Institute of Technology)
- Ted E. Senator (Leidos--previously SAIC)
odd15kdd (at) outlier-analytics.org