SHARD: a framework for sequential, hierarchical anomaly ranking and detection

Authors:
Jason Robinson;Margaret Lonergan;Lisa Singh;Allison Candido;Mehmet Sayal
Affiliations:
Georgetown University, Washington, DC;Georgetown University, Washington, DC;Georgetown University, Washington, DC;Georgetown University, Washington, DC;Hewlett Packard, Palo Alto, CA
Venue:
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Year:
2012

Citing 10
Cited 0

LOF: identifying density-based local outliers

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Finding surprising patterns in a time series database in linear time and space

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
SpaceTree: Supporting Exploration in Large Node Link Tree, Design Evolution and Empirical Evaluation

INFOVIS '02 Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02)
A Survey of Outlier Detection Methodologies

Artificial Intelligence Review
An overview of anomaly detection techniques: Existing solutions and latest technological trends

Computer Networks: The International Journal of Computer and Telecommunications Networking
Mining approximate top-k subspace anomalies in multi-dimensional time-series data

VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Anomaly detection: A survey

ACM Computing Surveys (CSUR)
Anomaly detection for symbolic sequences and time series data

Anomaly detection for symbolic sequences and time series data
An optimization model for outlier detection in categorical data

ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Order metrics for semantic knowledge systems

HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

This work explores unsupervised anomaly detection within sequential, hierarchical data. We present a flexible framework for detecting, ranking and analyzing anomalies. The framework 1) allows users to incorporate complex, multidimensional, hierarchical data into the anomaly detection process; 2) uses an ensemble method that can incorporate multiple unsupervised anomaly detection algorithms and configurations; 3) identifies anomalies from combinations of categorical, numeric and temporal data at different conceptual resolutions of hierarchical data; 4) supports a set of anomaly ranking schemes; and 5) uses an interactive tree hierarchy visualization to highlight anomalous regions and relationships. Using both synthetic and real world data, we show that standard anomaly detection algorithms, when plugged into our framework, maintain a high anomaly detection accuracy and identify both micro-level, detailed anomalies and macro-level global anomalies in the data.