Probabilistic threshold query optimization based on threshold classification using ELM for uncertain data

Probabilistic threshold query (PTQ), which returns all the objects satisfying the query with probabilities higher than a 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 extreme thresholds may lead to empty result or too many results. It is difficult for a user to set 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 range of result number which is more intuitive and easier to choose. We first introduce the features selected for the two most important PTQs, which are nearest neighbor (NN) and reverse nearest neighbor (RNN) queries. Then a threshold classification algorithm (TCA) using ELM is proposed to set a suitable threshold for the query, where plurality voting method is applied. Further, the PTQ 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 PTQs algorithms. In addition, ELM outperforms SVM with regard to both the response time and classification accuracy.