Monitoring Probabilistic Threshold SUM Query Processing in Uncertain Streams

Abstract

Many sources of data streams, e.g. geo-spatial streams derived from GPS-tracking systems or sensor streams provided by sensor networks are inherently uncertain due to impreciseness of sensing devices, due to outdated information, and due to human errors. In order to support data analysis on such data, aggregation queries are an important class of queries. This paper introduces a scalable approach for continuous probabilistic SUM query processing in uncertain stream environments. Here we consider an uncertain stream as a stream of uncertain values, each given by a probability distribution among the domain of the sensor values. Continuous probabilistic sum queries maintain the probability distribution of the sum of possible sensor values actually derived from the streaming environment. Our approach is able to efficiently compute the probabilistic SUM according to the possible world semantics, i.e., without any loss of information. Furthermore, we show the query’s answer can be efficiently updated in dynamic environments where attribute values change frequently. Our experimental results show that our approach computes probabilistic sum queries efficiently, and that processing queries incrementally instead of performing computation from scratch further boosts the performance of our algorithm significantly.