Top-k entities query processing on uncertainly fused multi-sensory data

Abstract

Sensor fusion is the combining of sensory data from disparate sources such that the resulting information is in some sense better than would be possible when these sources were used individually. The natural uncertainty exists in these data because sensors are not precise enough. Hence, the intuitive method to store this kind of data is using uncertain database. Finding the top-k entities according to one or more attributes is a powerful technique when the uncertain database contains large quantity of data. However, compared to top-k in traditional databases, queries over uncertain database are more complicated because of the existence of exponential possible worlds. We propose a method to process entity---based global top-k aggregate queries in uncertain database, which returns the top-k entities that have the highest aggregate value. Our method has two levels, entity state generation and G-topk-E query processing. In the former level, entity states, which satisfy the properties of x-tuple, are generated one after the other according to their aggregate values, while in the latter level, dynamic programming---based global top-k entity query processing is employed to return the answers. Comprehensive experiments on different data sets demonstrate the effectiveness of the proposed solutions.