Public Sentiment Big Data Query Processing and Optimization with Unified Storage of Source and Meta Data

Abstract

Abstract Public sentiment big data has the characteristics of mass, multi-source, heterogeneity and multi-mode. At present, it mostly adopts separate storage strategies and has low storage and query efficiency. To this end, a distributed storage model is proposed in which structured and unstructured data correspond and are stored uniformly. By establishing a unified storage framework for source metadata, the original source data and characteristic metadata are stored uniformly according to different storage methods. Based on the above storage architecture, a hierarchical index structure is proposed to improve the efficiency of big data query. The unified storage model of source metadata is compared with the common storage methods, and is in the leading position in terms of Block number and query processing efficiency.