ELM Based Efficient Probabilistic Threshold Query on Uncertain Data

Abstract

The probabilistic threshold query (PTQ), which returns all the objects satisfying the query with probabilities higher than a probability threshold, is widely used in uncertain database. Most previous work focused on the efficiency of query process, but paid no attention to the setting of thresholds. However, setting the thresholds too high or too low may lead to empty result or too many results. For a user, it is too difficult to choose a suitable threshold for a query. In this paper, we propose a new framework for PTQs based on threshold classification using ELM, where the probability threshold is replaced by the number range of results which is more intuitive and easier to choose. We first introduce the features selected for the probabilistic threshold nearest neighbor query (PTNNQ), which is one of the most important PTQ types. Then a threshold classification algorithm (TCA) using ELM is proposed to set a suitable threshold for the PTNNQ. Further, the whole PTNNQ processing integrated with TCA are presented, and a dynamic classification strategy is proposed subsequently. Extensive experiments show that compared with the thresholds those the users input directly, the thresholds chosen by ELM classifiers are more suitable, which further improves the performance of PTNNQ. In addition, ELM outperforms SVM with regards to both the response time and classification accuracy.