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A spatio-temporal data association analysis method based on big data technology

A big data technology and spatiotemporal data technology, which is applied in the field of association analysis of spatiotemporal trajectory data by mining frequent itemsets algorithm, can solve the problems of low performance, large time and space complexity of Apriori algorithm, and cannot meet the requirements of fast data association and data mining. requirements and other issues, to achieve the effect of improving accuracy and improving the speed of correlation analysis

Active Publication Date: 2022-04-15
SHENZHEN XINYI TECH CO LTD
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AI Technical Summary

Problems solved by technology

Therefore, before the Apriori algorithm generates a complete set of frequent patterns, it needs to scan the database multiple times, and at the same time generate a large number of candidate frequent sets, which makes the time and space complexity of the Apriori algorithm larger.
The performance of the Apriori algorithm is often low when mining long-term frequent patterns
[0005] In the context of the advent of the big data era, the above-mentioned traditional spatio-temporal trajectory data association methods are completely unable to meet people's needs for fast data association and data mining, and the speed problem is also the main shortcoming of traditional technologies

Method used

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  • A spatio-temporal data association analysis method based on big data technology

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Embodiment Construction

[0012] Such as figure 1 As shown, a spatio-temporal data association analysis method based on big data technology includes the following steps.

[0013] Step 1. Start the memory analysis service cluster, then start the analysis service container, and then obtain various data from various data sources to form a distributed dataset RDD.

[0014] In the specific implementation, by combining the use of Spark's memory-based computing technology, the spatio-temporal trajectory data to be analyzed is pulled into the memory of the Spark cluster to form a distributed data set RDD.

[0015] Several spatio-temporal trajectory data belong to different types of data respectively.

[0016] Several spatio-temporal trajectory data respectively data different spatial and temporal dimensions.

[0017] For example, according to the conditions, the license plate data set to be analyzed, the IMSI data set, and the Mac data set are obtained from HBase.

[0018] The second step. Group the distrib...

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Abstract

The invention relates to a spatio-temporal data association analysis method based on big data technology, which can generate frequent itemsets according to the spatial and temporal relationship of data during spatio-temporal trajectory data mining, and according to a certain data type, the set with the smallest total amount of data Generate all frequent itemsets in a loop, increasing the time complexity of traditional data mining analysis algorithms from O(n^n) to O(n); by optimizing the input parameters of mining frequent itemsets algorithms, changing frequency to frequency, The accuracy of data analysis is improved; and combined with the big data technology based on memory computing, the speed of association analysis of spatiotemporal trajectory data is greatly improved. The present invention is especially suitable for the field of data mining where there are various types of association analysis data, each type of data is extremely large, and the data association results need to be quickly analyzed.

Description

technical field [0001] The invention relates to a spatio-temporal data correlation analysis method, in particular to a method for spatio-temporal trajectory data correlation analysis based on the big data of a specific industry and using a frequent itemset mining algorithm. Background technique [0002] With the development of computer technology, various analysis methods are now widely used in data comparison, data statistics, data mining and other fields. It is relatively slow and inefficient. The traditional analysis techniques are described in detail as follows. [0003] The specific steps of the first typical spatio-temporal trajectory data association analysis method are to extract a type of data that needs to be associated from the data, cycle through the list of such data, and then compare the data to be associated with the associated data one by one. Satisfy the association conditions, and feedback the comparison results one by one. Therefore, when there are many ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/2458G06F16/29G06N20/00
Inventor 向刚马中旺王杰洪启祥贾伟刘聪厦率航黄志远施楚强周颢王嘉骏张涛黄毅沈文凯邓世春罗珊珊
Owner SHENZHEN XINYI TECH CO LTD
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