Spatial-temporal trajectory big data storage method for database

Abstract

The invention relates to a spatial-temporal trajectory big data storage method for a database. The method comprises the steps that information collection is conducted on state information of an object with a position information sampling function through an information collection terminal; after information collection, collected information is transmitted to a data processing system through transmission equipment; noise filtering preprocessing and data cleaning preprocessing are conducted on the received original data information in the data processing system, the preprocessed data information is stored in a trajectory database by serving as source spatial data, and then a data storage layer is obtained, wherein the source spatial data is stored in a dispersion-type sampling storage mode; the source spatial data is modeled and processed, after interception, projection, measurement and built-in function fitting operations, track data is obtained, and then a trajectory layer is constructed; a complex query function of spatial-temporal trajectory data analysis is provided. The method has the advanced technical advantages of large-scale source data concurrency loading, lightweight trajectory data representing, flexible query processing and the like.

Description

technical field [0001] The invention relates to the fields of database management system, space-time database, floating vehicle tracking, traffic information analysis, location-related services, big data calculation, etc., and proposes a database-oriented space-time track big data storage system and method, belonging to the field of computer Internet technology. Background technique [0002] With the continuous promotion and popularization of GPS-enabled mobile terminals (such as vehicle navigators, smart phones, and Internet of Things locators, etc.) and online map services (such as Google Maps, Baidu Maps, MapQuest), people can record their current geographic location. Coordinates and share their sports information to the Internet, such as Internet applications Bikely, GPS-Way-points, Share-My-Routes and Microsoft's GeoLife and other projects. At the same time, more and more social networks, such as Weibo, WeChat, Twitter, Foursquare and Facebook, also provide functions ba...

AI Technical Summary

Problems solved by technology

In the existing trajectory database prototypes, the GPS source data is converted into a trajectory data model. Whether it is real-time data injection or batch data import, its data loading performance and scalability are very limited

(2) It is difficult to support complex data analysis scenarios

The former means that data needs to be exchanged at high speed between clusters (such as the shuffle technology under the MapReduce architecture or the interconnect technology under the MPP architecture), resulting in a communication storm; the latter means that if a trajectory query interface close to high-level semantics is not provided, users will It is difficult to carry out efficient and quick development, and even cause very complex query plans that are difficult to interpret and tune through business intelligence software (BI), blocking the big data analysis and calculation queue

[0006] To sum up, for the database-oriented trajectory big data storage method, the existing related solutions are difficult to apply to the practical application of big data level, it is difficult to support large-scale and high-concurrency data injection, and it is difficult to support complex data analysis scenarios

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