Abstract
With the increasing popularity of a large number of Internet-based services and a large number of services hosted on cloud platforms, a more powerful back-end storage system is needed to support these services. At present, it is very difficult or impossible to implement a distributed storage to meet all the above assumptions. Therefore, the focus of research is to limit different characteristics to design different distributed storage solutions to meet different usage scenarios. Economic big data should have the basic requirements of high storage efficiency and fast retrieval speed. The large number of small files and the diversity of file types make the storage and retrieval of economic big data face severe challenges. This paper is oriented to the application requirements of cross-modal analysis of economic big data. According to the source and characteristics of economic big data, the data types are analyzed and the database storage architecture and data storage structure of economic big data are designed. Taking into account the spatial, temporal, and semantic characteristics of economic big data, this paper proposes a unified coding method based on the spatiotemporal data multilevel division strategy combined with Geohash and Hilbert and spatiotemporal semantic constraints. A prototype system was constructed based on Mongo DB, and the performance of the multilevel partition algorithm proposed in this paper was verified by the prototype system based on the realization of data storage management functions. The Wiener distributed memory based on the principle of Wiener filter is used to store the workload of each workload distributed storage window in a distributed manner. For distributed storage workloads, this article adopts specific types of workloads. According to its periodicity, the workload is divided into distributed storage windows of specific duration. At the beginning of each distributed storage window, distributed storage is distributed to the next distributed storage window. Experiments and tests have verified the distributed storage strategy proposed in this article, which proves that the Wiener distributed storage solution can save platform resources and configuration costs while ensuring Service Level Agreement (SLA).
Highlights
With the continuous development of Internet technology, the amount of data generated and accumulated on the Internet has exploded. e information generated by people’s activities on the Internet, such as posting Weibo, shopping, and comments, is eventually converted into binary data and stored
Multimodal characteristics refer to the same spatiotemporal object with many different forms of data description, and different modal data have the characteristics of heterogeneous low-level features and high-level semantic correlation. e multimodal characteristics of Journal of Mathematics multisource economic big data provide the possibility to support multisource spatiotemporal data in-depth crossmodal analysis, mining, and application. erefore, how to realize the storage of economic big data and provide database support for cross-modal analysis applications has become the focus of current research
Main Technologies and Methods of Distributed Economic Big Data Computing and Analysis Platform is section introduces the distributed storage technology and its principles at the core of the platform and analyzes the technical theoretical principles of the platform’s distributed storage. en we introduce the core big data calculation and analysis principles that will be used and based on the platform to realize the analysis and calculation of economic big data, which indirectly proves the feasibility of the distributed economic big data calculation and analysis platform
Summary
With the continuous development of Internet technology, the amount of data generated and accumulated on the Internet has exploded. e information generated by people’s activities on the Internet, such as posting Weibo, shopping, and comments, is eventually converted into binary data and stored. E multimodal characteristics of Journal of Mathematics multisource economic big data provide the possibility to support multisource spatiotemporal data in-depth crossmodal analysis, mining, and application. Erefore, how to realize the storage of economic big data and provide database support for cross-modal analysis applications has become the focus of current research. Most of the analysis of economic census data is still based on the methods of purely using statistics and artificial intelligence. Is method can take into account the spatial proximity, temporal relevance, and semantic similarity of economic big data under the premise of satisfying the efficiency of the algorithm.
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