Information Visualization and Visual Data Mining
IEEE Transactions on Visualization and Computer Graphics
Towards Sensor Database Systems
MDM '01 Proceedings of the Second International Conference on Mobile Data Management
The design of an acquisitional query processor for sensor networks
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Providing architectural support for building context-aware applications
Providing architectural support for building context-aware applications
Cleaning and querying noisy sensors
WSNA '03 Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications
A robust architecture for distributed inference in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Analysis Guided Visual Exploration of Multivariate Data
VAST '07 Proceedings of the 2007 IEEE Symposium on Visual Analytics Science and Technology
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Researchers have seen visualization as a tool in presenting data based on available datasets. Its usage is however undermined by its inability to acknowledge the associated uncertainties in real world measurements. Visualization results are said to be "too generous", providing us with visual assumptions that though, may not be too far from reality, but the associated inaccuracies could become significant when dealing with life dependant datasets. Uncertainty reality is now becoming a significant research interest. In most cases accuracy is a neglected issue. Two wrong assumptions are believed; the first is that the data visualized is accurate, and the second is that the visualization process is exempt from errors. The objectives of this paper are to present the implications of inaccuracies and propose a treatment algorithm for the visualizations of pipeline sensors' datasets. The paper also features attributes that gives a user an idea of sensors' datasets inaccuracies.