Continuous Inverse Ranking Queries in Uncertain Streams

This paper introduces a scalable approach for continuous inverse ranking on uncertain streams. An uncertain stream is a stream of object instances with confidences, e.g. observed positions of moving objects derived from a sensor. The confidence value assigned to each instance reflects the likelihood that the instance conforms with the current true object state. The inverse ranking query retrieves the rank of a given query object according to a given score function. In this paper we present a framework that is able to update the query result very efficiently, as the stream provides new observations of the objects. We will theoretically and experimentally show that the query update can be performed in linear time complexity. We conduct an experimental evaluation on synthetic and real-world data, which demonstrates the efficiency of our approach.