Novel Semantics of the Top-k Queries on Uncertainly Fused Multi-Sensory Data

Multiple sensors and sensor fusion are commonly used to get more accurate information. The intuitive method to store multi-sensory data is using uncertain database because the sensors are not precise enough. Hence, like the top-k queries in traditional database, the top-k queries in uncertain databases are quite popular and useful due to its wide application. Although there are lots of top-k query semantics, most of them return tuples, which does not make sense in some cases. We define two novel kinds of top-k query semantics in uncertain database, Uncertain x-kRanks queries (U-x-kRanks) and Global x-Top-k queries (G-x-Top-k), which return k x-tuples according to the score and the confidence of alternatives in x-tuples, respectively. Moreover, in order to reduce the search space, we present an efficient algorithm to process U-x-kRanks queries and G-x- Top-k queries. Comprehensive experiments and analysis on different artificial data sets demonstrate the effectiveness of the proposed strategies.