towards correcting input data errors probabilistically using integrity constraints
Mobile and pervasive applications frequently rely on devices such as RFID antennas or sensors (light, temperature, motion) to provide them information about the physical world. These devices, however, are unreliable. They produce streams of information where portions of data may be missing, duplicated, or erroneous. Current state of the art is to correct errors locally ( e.g. , range constraints for temperature readings) or use spatial/temporal correlations ( e.g. , smoothing temperature readings). However, errors are often apparent only in a global setting, e.g., missed readings of objects that are known to be present, or exit readings from a parking garage without matching entry readings.In this paper, we present StreamClean, a system for correcting input data errors automatically using application defined global integrity constraints. Because it is frequently impossible to make corrections with certainty, we propose a probabilistic approach, where the system assigns to each input tuple the probability that it is correct.We show that StreamClean handles a large class of input data errors, and corrects them sufficiently fast to keep-up with input rates of many mobile and pervasive applications. We also show that the probabilities assigned by StreamClean correspond to a user's intuitive notion of correctness.
correctness input data errors use integrity constraint constraint mobility pervasive applications device sensor lighting temperature motion informal physical art spatial temporal correlation smooth global object matching paper systems probability class user probabilistic databases data cleaning entropy maximization stream processing