Spatio-temporal index construction method for massive track point data

A construction method and a technology of trajectory points, applied in the field of information retrieval, can solve the problems of low index construction efficiency, high storage consumption, and inability to meet the rapid growth of spatiotemporal big data, so as to reduce the probability of data skew, reduce storage consumption, high The effect of scalability

Active Publication Date: 2020-01-21
INST OF COMPUTING TECH CHINESE ACAD OF SCI
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  • Description
  • Claims
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Problems solved by technology

Most of the existing spatio-temporal index construction methods use data records as index units. This method leads to large storage consumption and low index construction efficiency, and cannot meet the rapid growth needs of different types of spatio-temporal big data.

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  • Spatio-temporal index construction method for massive track point data
  • Spatio-temporal index construction method for massive track point data
  • Spatio-temporal index construction method for massive track point data

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Embodiment Construction

[0031] In order to make the object, technical solution and advantages of the present invention clearer, the method for constructing a spatio-temporal index for massive track point data according to the embodiment of the present invention is further described in detail below in conjunction with the accompanying drawings.

[0032] Track point data is a series of data records obtained by sampling the real-time position of a moving object. Each record includes sampling time stamp, two-dimensional position information, speed, direction and other motion information, and other relevant information such as sampling time. Track point data is a kind of structured data. The number of data columns is fixed and the meaning, type and value range of each column are also fixed. At the same time, once the track point data collected by sampling is generated, it will generally not be used During the process of modification and deletion, and shifting from online collection to offline analysis, tra...

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Abstract

The invention relates to a parallel spatial-temporal index constructing method oriented to massive track point data. According to the spatial-temporal index constructing method oriented to the massive track point data, with a track point data file being an index unit, the storage consumption of an index is lowered, so that an index structure has high expandability; meanwhile, by utilizing a Hilbert curve to divide a data file, compared with other modes from multi-dimension to one-dimension mapping, since the Hilbert curve has an excellent space filling characteristic, the dividing effect is better, and the probability of the occurrence of data skew can be lowered.

Description

technical field [0001] The invention relates to the field of information retrieval, in particular to a spatio-temporal index construction method for massive track point data. Background technique [0002] With the development of science and technology, the world today has entered the era of big data. The rapid growth of data scale requires big data to have global expressive power. Spatio-temporal big data has become one of the important big data because it can reflect time, space and the relationship between objects. However, the relatively complex relationship between spatiotemporal big data and its dynamic evolution characteristics also bring difficulties in retrieval and query. Track point data belongs to spatio-temporal big data, which specifically refers to the data information obtained by sampling the motion process of moving objects in a spatio-temporal environment. In recent years, with the rapid development of satellites, wireless networks, and positioning equipme...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/22
CPCG06F16/2246G06F16/2264
Inventor 陈昭王磊刁博宇徐勇军
Owner INST OF COMPUTING TECH CHINESE ACAD OF SCI
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