Research on Logistics Data Processing Scheme Based on Mashup Architecture

Abstract

With the improvement of the level of logistics informatization, the traditional stand-alone data processing method faces challenges in data storage, data loading, and data query in a massive data environment. How to effectively deal with the problem of insufficient performance of traditional data processing methods has become the top priority. Therefore, on account of the characteristics of logistics business data, particularly the time-series attribution, this paper proposes a massive structured data processing scheme and builds a mashup architecture based on the IoTDB time-series database and MPP database. First, the overall frame design of this scheme and the applicability analysis based on IoTDB technology are expounded. Then the comparison and analysis are given between the traditional data processing method and the proposed scheme. The experimental results show that our scheme performs higher efficiency than traditional routines in data processing such as data loading and data query. This research can provide some technical support and guidance for logistics data management and application.