An Efficient Algorithm for top-k Queries on Uncertain Data Streams

We tackle the problem of answering maximum probabilistic top-k tuple set queries. We use a sliding-window model on uncertain data streams and present an efficient algorithm for processing sliding-window queries on uncertain streams. In each sliding window, the algorithm selects the k tuples with the highest probabilities from sets of different numbers of the tuples with the highest scores. Then, the algorithm computes existential probability of the top-k tuples, and chooses the set with the highest probability as the top-k query result. We theoretically prove the correctness of the algorithm. Our experimental results show that our algorithm requires lower time and space complexity than other existing algorithms.