MobiVis: A Visualization System for Exploring Mobile Data

In Proceedings of IEEE Pacific Visualization Symposium, IEEE VGTC, March, 2008, pp.175-182
MobiVis: A Visualization System for Exploring Mobile Data
Zeqian Shen, Kwan-Liu Ma, Zeqian Shen, Kwan-Liu Ma

The widespread use of mobile devices brings opportunities to capture large-scale, continuous information about human behavior. Mobile data has tremendous value, leading to business opportunities, market strategies, security concerns, etc.

Visual analytics systems that support interactive exploration and discovery are needed to extracting insight from the data. However, visual analysis of complex social-spatial-temporal mobile data presents several challenges.

We have created MobiVis, a visual analytics tool, which incorporates the idea of presenting social and spatial information in one heterogeneous network. The system supports temporal and semantic filtering through an interactive time chart and ontology graph, respectively, such that data subsets of interest can be isolated for close-up investigation.

"Behavior rings," a compact radial representation of individual and group behaviors, is introduced to allow easy comparison of behavior patterns. We demonstrate the capability of MobiVis with the results obtained from analyzing the MIT Reality Mining dataset.

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Drawing Sound Conclusions from Noisy Judgments

David Goldberg, Andrew Trotman, Xiao Wang, Wei Min, Zongru Wan

The quality of a search engine is typically evaluated using hand-labeled data sets, where the labels indicate the relevance of documents to queries. Often the number of labels needed is too large to be created by the best annotators, and so less accurate labels (e.g. from crowdsourcing) must be used. This introduces errors in the labels, and thus errors in standard precision metrics (such as P@k and DCG); the lower the quality of the judge, the more errorful the labels, consequently the more inaccurate the metric. We introduce equations and algorithms that can adjust the metrics to the values they would have had if there were no annotation errors.

This is especially important when two search engines are compared by comparing their metrics. We give examples where one engine appeared to be statistically significantly better than the other, but the effect disappeared after the metrics were corrected for annotation error. In other words the evidence supporting a statistical difference was illusory, and caused by a failure to account for annotation error.