A Generic Framework for Handling Uncertain Data with Local Correlations
Data uncertainty is ubiquitous in many real-world applications such as sensor/RFID data analysis. In this paper, the authors investigate uncertain data that exhibit local correlations, that is, each uncertain object is only locally correlated with a small subset of data, while being independent of others. They propose a generic framework for dealing with this kind of uncertain and locally correlated data, in which they investigate a classical spatial query, nearest neighbor query, on uncertain data with local correlations (namely LC-PNN). Most importantly, to enable fast LC-PNN query processing, they propose a novel filtering technique via offline pre-computations to reduce the query search space.