Managing uncertainty of large spatial databases

Abstract

Spatial data are prevalent in location-based services (LBS), sensor networks, and RFID monitoring systems. Data readings collected in these applications are often imprecise. The uncertainty in the data can arise from multiple sources, including measurement errors due to the sensing instrument and discrete sampling of the measurements. It is often important to record the imprecision and also to take it into account when processing the spatial data. The challenges of handling the uncertainty in spatial data includes modeling, semantics, query operators and types, efficient execution, and user interfaces. Probabilistic models have been proposed for handling the uncertainty. In this paper, we examine the modeling and querying issues of this kind of databases.