Anomaly Detection in GPS Data Based on Visual Analytics Kyung Min Su - Zicheng Liao, Yizhou Yu, and Baoquan Chen, Anomaly Detection in GPS Data Based on Visual Analytics. IEEE Conference on Visual Analytics Science and Technology, 2010

Overview Data analysis on GPS traces of taxis  For traffic monitoring  To detect abnormal situations Visual analytics approach  collaboration between machines and human analysts

System architecture

Feature Set

Feature Extraction

Probabilistic Models Conditional Random Fields (CRF)

Hidden state sequence y Z(x): normalization item

CRF - Training Training: computes the model parameters (the weight vector) according to labeled training data pairs {y, x}

CRF - Inference Inference:  tries to find the most likely hidden state assignment y, the label sequence for the unlabeled input sequence x

Active Learning Active learning:  learner selectively chooses the examples  to reduced amount of training data  to improve the generalization performance on a fixed-size training set Criteria  Uncertainty  Representativeness  Diversity

Uncertainty High model uncertainty  Help enrich the classifier Confidence Uncertainty

Representativeness High representativeness  sample sequence is not similar to any other

Diversity Diversity:  To remove items that are redundant with respect to data items that are already in the training set from the previous iteration. Similarity score is not greater than the average pairwise similarity among all sequences currently in the training set.

Visualization and Interaction

Interaction Interface Basic mode  Raw GPS traces without any labels Monitoring mode  Anomaly tags are shown.  Show the internal CRF states of the tagged data items. Tagging mode  Active learning module is activated.  Highly uncertain labels from the CRF model are highlighted, requesting for user input.

Visualizing CRF Features CRF internal states visualization Features and their Weights  Red: +  Negative: -

Visualizing CRF Features

Summarization Anomaly detection system  Conditional Random Fields Active Learning Visualization and Interaction

References [1] Zicheng Liao, Yizhou Yu, and Baoquan Chen. Anomaly Detection in GPS Data Based on Visual Analytics. IEEE Conference on Visual Analytics Science and Technology (VAST 2010), [2] J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of the International Conference on Machine Learning (ICML- 2001), [3] C. T. Symons, N. F. Samatova, R. Krishnamurthy, B. H. Park, T. Umar, D. Buttler, T. Critchlow, and D. Hysom. Multi-criterion active learning in conditional random fields. In Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence, 2006.