posted by user: emueller || 4145 views || tracked by 5 users: [display]

ODD 2014 : KDD 2014 Workshop on Outlier Detection & Description under Data Diversity


When Aug 24, 2014 - Aug 24, 2014
Where New York City
Submission Deadline Jun 4, 2014
Notification Due Jul 8, 2014
Final Version Due Jul 15, 2014
Categories    anomaly detection   outlier analysis   data mining   knowledge discovery

Call For Papers

ODD^2 @ KDD 2014
Workshop on Outlier Detection & Description under Data Diversity
will be held in conjunction with KDD 2014
August 24th, 2014 in NYC, USA

ODD^2 is a workshop organized in conjunction with the ACM SIGKDD Conference on Knowledge, Discovery, and Data Mining (KDD 2014).

The goal of the workshop is to address outlier mining as a three-fold challenge: outlier detection, outlier description, and outlier modeling under data diversity. The focus of the workshop lies in the quantitative and qualitative analysis of anomalies in heterogeneous data, including temporal, spatial, sequence, graph, and multi-dimensional space anomalies. The topics of detection and description for diverse data are rarely considered in unison, and literature for these tasks is spread over different research communities. The main goal of ODD^2 is to bridge this gap and provide a venue for knowledge exchange between these different research areas for a corroborative union of quantitative and qualitative analyses for the study of outlier mining.

Traditionally, outlier mining and anomaly detection focused on the automatic detection of highly deviating objects. It has been studied for several decades in statistics, machine learning, data mining, and database systems, and led to a lot of insight as well as automated systems for the detection of outliers.

For today's outlier mining applications to be successful, three directions should be addressed: (i) how to create outlier detection models, (ii) how to serve the needs of certain applications with respect to interpretability, and (iii) how to create outlier models that can handle datasets consisting of diverse sources. We remark that those directions are not disjoint, in contrast they are quite intertwined: as the diversity and thus complexity of the data increases, outlier description not only becomes more challenging but also even more necessary.

With diverse set of applications using outlier analysis for data exploration and knowledge discovery, the demand for manual verification and interpretability of outliers is steadily increasing. Examples include applications such as health surveillance, customer segmentation, fraud analysis, or sensor monitoring, where one is particularly interested in why an object seems outlying. We elaborate below with a specific example.

For example consider social media platforms such as Twitter or Facebook. The diversity of data in such platforms is immense; ranging from relations among users (graphs), user demographics (high-dimensional features), user-generated content (text), temporal dynamics (data streams), heterogeneous relational data in the form of likes, shares, tags, and so on. Outliers in those applications often correspond to various types of anomalies ranging from fake celebrities, fake user accounts, (social) malware, page-like-as-a-service, and so on. As a result, it becomes critical to develop outlier models that can work with diverse datasets, provide high quality outlier detection, but also describe the reasons for outlier properties to the human user.

Ambuj Singh (University of California Santa Barbara)
Keynote: "Detection of significant network processes"

Srinivasan Parthasarathy (Ohio State University)
Keynote: "TBD"

Topics of interests for the workshop include, but are not limited to:

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

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

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

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


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:

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

Accepted papers will be included in the KDD 2014 Digital Proceedings, and made available 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.

Submission deadline: June 11, 2014 23:59 PST
Acceptance notification: July 15, 2014 23:59 PST
Camera-ready deadline: July 27, 2014 23:59 PST
Workshop day: August 24, 2014

Fabrizio Angiulli, University of Calabria
Ira Assent, Aarhus University
Albert Bifet, Yahoo! Labs Barcelona
Rajmonda Caceres, MIT Lincoln Laboratory
Varun Chandola, Oak Ridge Nat. Lab.
Polo Chau, Georgia Tech
Sanjay Chawla, University of Syndey
Christos Faloutsos, Carnegie Mellon University
Jing Gao, University of Buffalo
Manish Gupta, Microsoft, India
Arun Maiya, Institute for Defense Analyses
Daniel B. Neill, Carnegie Mellon University
Spiros Papadimitriou, Rutgers University
Joerg Sander, University of Alberta
Thomas Seidl, RWTH Aachen University
Koen Smets, University of Antwerp
Hanghang Tong, CUNY
Ye Wang, The Ohio State University
Osmar Zaiane, University of Alberta
Arthur Zimek, LMU Munich

Charu Aggarwal (IBM T. J. Watson Research Center)
Leman Akoglu (Stony Brook University)
Emmanuel Müller (Karlsruhe Institute of Technology)
Raymond Ng (University of British Columbia)

Contact us at:
odd14kdd (at)

Related Resources

KDD 2022   28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
MLDM 2022   18th International Conference on Machine Learning and Data Mining
IEEE COINS 2022   IEEE COINS 2022: Hybrid (3 days on-site | 2 days virtual)
ICDM 2022   22th Industrial Conference on Data Mining
JCRAI 2022-Ei Compendex & Scopus 2022   2022 International Joint Conference on Robotics and Artificial Intelligence (JCRAI 2022)
CoMSE 2022   2022 International Conference on Materials Science and Engineering (CoMSE 2022)
Intelligence for Engineering 2022   ASME JCISE Special Issue on Machine Intelligence for Engineering under Uncertainties
CiViE 2022   6th International Conference On Civil Engineering
DCADS 2022   Data Science and Analytics for Decision Support Workshop
IJANS 2022   International Journal on AdHoc Networking Systems