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A member and category technology, applied in the field of big data offline processing, can solve problems such as processor work pressure
Pending Publication Date: 2022-05-06
SUNING CLOUD COMPUTING CO LTD
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However, this method has two disadvantages in operation: on the one hand, member behavior information contains various types of data, and not every type of data will be used to classify members. If all data of member behavior information is directly extracted and then member Classification will cause a certain amount of work pressure on the processor; on the other hand, if the membership category is classified based on ordinary member behavior information data, it is necessary to repeatedly read the behavior information data of all members each time, which will generate a lot of repetitive work content
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Embodiment 1
[0048] An embodiment of the present invention provides a method for classifying membership categories, the method may include steps:
[0049] S1 constructs a full database and an incremental database.
[0050] S11, building a full database. Determine the starting point of time, and build a full database with all member behavior information before the starting point. The full database includes at least member data, and the member data is sorted sequentially according to member code data. The member data at least includes member code data, behavior date data and behavior fact value.
[0051]In order to make the subsequent membership category division of the full database more convenient, a corresponding logical relationship is established for the member code data, behavior date data, and behavior fact values in the full database through a JSON array. That is [member code data, {behavior date data, behavior fact value}], the behavior fact value refers to the number of times ...
Embodiment 2
[0094] An embodiment of the present invention provides a device for classifying membership categories, which includes the following content:
[0095] A database building module, which can select all order data before a point in time, extract valid data from all order data, and build a full database.
[0096] For example: order data includes member code data, behavior date data, behavior fact value, commodity code data, store code data, etc.
[0097] The above-mentioned database building module can select orders before October 1, 2020 and eliminate invalid data such as product code data and store code data, and only extract member code data, behavior date data, and behavior fact values.
[0098] The data comparison module, after constructing the full database and the incremental database through the above database construction module, can compare the information in the incremental database with the information in the full database through the data comparison module, thereby upd...
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Abstract
The invention discloses a member category division method, which comprises the steps of determining a time starting point, constructing a full database according to member behavior information before the time starting point, constructing an incremental database according to member behavior information after the time starting point for a period of time, and executing a member data comparison process of the full database and the incremental database. And judging whether member code data which is the same as that in the incremental database exists in the full-amount database, updating the full-amount database according to a judgment result, obtaining a final full-amount database, and dividing member categories according to the final full-amount database. According to the method, the full database is updated in real time by constructing the full database and generating the incremental database in real time, so that the problem of huge IO overhead of reading factual table partition data on a large scale each time is avoided, the overhead of performing aggregation calculation on the large-scale data is reduced to a greater extent, and the calculation cost is greatly saved.
Description
technical field [0001] The invention belongs to the field of off-line processing of big data, and in particular relates to a method for classifying membership categories. Background technique [0002] At present, the division of membership categories is mainly based on the behavior data generated by members, that is, extracting the membership number in the behavior information data, the date data of purchase and browsing behavior, the number of behavior facts of purchase and browsing, etc., and then classifying the membership based on the above data divided. However, this method has two disadvantages in operation: on the one hand, member behavior information contains various types of data, and not every type of data will be used to classify members. If all the data of member behavior information is directly extracted and then member Classification will cause a certain amount of work pressure on the processor; on the other hand, if the membership category is divided based on...
Claims
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