A model-matching algorithm based on improved BP over out-of-order streams

Due to the explosive increment of data in big data era, it is a challenging task to analyze and extract meaningful data for users. Data needs to be timely operated because of the time sensitivity, so it faces enormous pressure in storage and computing. To deal with the problem that it is hard to achieve valuable information from out-of-order streams over big data in short time, a model-matching algorithm based on improved BP (Back Propagation) is proposed. In the algorithm, the matching model is set dynamically. Information is extracted for users according to the order of data´s arriving time. Furthermore, the algorithm parameters are automatically adjusted in the process of learning and matching. Accordingly, the responding speed of learning is accelerated and the time of matching reduces. In the simulation, a group of optimum parameters of improved BP are achieved by using self-adapting adjusting mechanism. The threshold (TH), connecting weight (CW) and learning rate (LR) are equal to 1.5, 3 and 1, respectively. We implement our model-matching algorithm on 10000 sets of out-of-order streams with these parameters. Results indicate that the proposed algorithm can obviously improve the accuracy and speed of matching and achieve better stability.