Data mining: concepts and techniques
Data mining: concepts and techniques
MobiMine: monitoring the stock market from a PDA
ACM SIGKDD Explorations Newsletter
Designing Pixel-Oriented Visualization Techniques: Theory and Applications
IEEE Transactions on Visualization and Computer Graphics
Information Visualization and Visual Data Mining
IEEE Transactions on Visualization and Computer Graphics
New Methods for the Visualization of Electric Power System Information
INFOVIS '00 Proceedings of the IEEE Symposium on Information Vizualization 2000
A system for analysis and prediction of electricity-load streams
Intelligent Data Analysis - Knowledge Discovery from Data Streams
Visual boosting in pixel-based visualizations
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
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Sensor data is usually represented by streaming time series. Current state-of-the-art systems for visualization include line plots and three-dimensional representations, which most of the time require screen resolutions that are not available in small transient mobile devices. Moreover, when data presents cyclic behaviors, such as in the electricity domain, predictive models may tend to give higher errors in certain recurrent points of time, but the human-eye is not trained to notice this cycles in a long stream. In these contexts, information is usually hard to extract from visualization. New visualization techniques may help to detect recurrent faulty predictions. In this paper we inspect visualization techniques in the scope of a real-world sensor network, quickly dwelling into future trends in visualization in transient mobile devices. We propose a simple dense pixel display visualization system, exploiting the benefits that it may represent on detecting and correcting recurrent faulty predictions. A case study is also presented, where a simple corrective strategy is studied in the context of global electrical load demand, exemplifying the utility of the new visualization method when compared with automatic detection of recurrent errors.