An Effective Uncertain Data Streams Top-K Query Algorithm

ABSTRACT

Large scale uncertain data streams are produced in many modern applications, such as RFID technology and sensor networks. Top-K query processing is one of the important techniques in the management of uncertain data streams. Existing Top-K queries processing does not consider the score and uncertainty of tuples. This paper first analyzes the uncertain data model and possible world semantic model, and then defines new Top-K queries semantics for uncertain data streams, and finally designs and realizes an effective Top-K queries algorithm on uncertain data streams. This algorithm sorts the score of each tuple and selects the k tuples with the highest probabilities to form the set, Top-K queries results. Compared to CSQ and SCSQ algorithms, the experiments show that this algorithm is more practical and effective than the others.

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