Trust Assessment on Streaming Data: a Real Time Predictive Approach

International audienceIoT data, that most often carry a temporal dimension, can be exploited from an analysis perspective or from a forecasting one. In this paper, we propose a predictive approach to address the problem of data trustworthiness in a data stream generated by a Smart Home application. We describe an online (Ensemble Regression) model that perform prediction in assigning a trust score to a target temporal value in real-time. Experiments conducted with real data demonstrate the performance of the model, while assessing data accuracy

Effects of soil water table regime on tree community species richness and structure of alluvial forest fragments in Southeast Brazil

In order to determine the influence of soil water table fluctuation on tree species richness and structure of alluvial forest fragments, 24 plots were allocated in a point bar forest and 30 plots in five forest fragments located in a floodplain, in the municipality of São Sebastião da Bela Vista, Southeast Brazil, totalizing 54, 10 X 20 m, plots. The information recorded in each plot were the soil water table level, diameter at breast height (dbh), total height and botanical identity off all trees with dbh > 5 cm. The water table fluctuation was assessed through 1 m deep observation wells in each plot. Correlations analysis indicated that sites with shallower water table in the flooding plains had a low number of tree species and high tree density. Although the water table in the point bar remained below the wells during the study period, low tree species richness was observed. There are other events taking place within the point bar forest that assume a high ecological importance, such as the intensive water velocity during flooding and sedimentation processes