Research Article
Multi-mode Retrieval Method for Big Data of Economic Time Series Based on Machine Learning Theory
@INPROCEEDINGS{10.1007/978-3-030-19086-6_13, author={Hai-ying Chen and Lan-fang Gong}, title={Multi-mode Retrieval Method for Big Data of Economic Time Series Based on Machine Learning Theory}, proceedings={Advanced Hybrid Information Processing. Second EAI International Conference, ADHIP 2018, Yiyang, China, October 5-6, 2018, Proceedings}, proceedings_a={ADHIP}, year={2019}, month={5}, keywords={First machine learning Second economic time series Third big data Forth retrieval}, doi={10.1007/978-3-030-19086-6_13} }- Hai-ying Chen
Lan-fang Gong
Year: 2019
Multi-mode Retrieval Method for Big Data of Economic Time Series Based on Machine Learning Theory
ADHIP
Springer
DOI: 10.1007/978-3-030-19086-6_13
Abstract
For traditional search methods affected by the index build time, resulting in poor search results, a multi-mode retrieval method for big data of economic time series based on machine learning theory is proposed. According to the good extensibility of big data, construct a retrieval model and use binary data conversion methods to match big data. The binary sequence is defined by the relationship between different data, the similarity of data features is calculated, and the candidate candidate sequence is filtered. Data with no similar features are filtered, and each sub-sequence set matching the pattern is given by similarity size. After the threshold is added, on the basis of slightly reducing the filtering amplitude, the calculation of the similarity matching in the big data retrieval process is greatly reduced, and combined with the fixed interval sampling matching method to determine the characteristics of big data, thereby realizing the machine learning theory. The multi-mode retrieval method for big data of economic time series based on machine learning theory retrieval. According to the experimental comparison results, the retrieval efficiency of the method can reach 95%, which provides effective help for large-scale retrieval of massive data.