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44results about How to "Guaranteed search efficiency" patented technology

Method and equipment for compression and searching of index data

The invention discloses a method and equipment for compression and searching of index data. The method includes the steps that after an index information list is obtained, whether the column index data in every two adjacent lines and in the same column are the same is sequentially compared according to the sequence of column index data in the index information list; when the column index data in every two adjacent lines and in the same column are the same, the same column index data are compressed; when the column index data in every two adjacent lines and in the same column are different, the column index data with different comparison results in the every two adjacent lines and in the same column and corresponding position information are recorded, and the column index data in every two adjacent lines and in other columns after the same column where the column index data with different comparison results are located are compressed; after the column index data of each line in the index information list are compressed, an index array for the index information list is generated according to the compressed column index data, the memory capacity of the index array is reduced, the data amount is small, and the searching efficiency of the column index data is guaranteed.
Owner:BEIJING OCEANBASE TECH CO LTD

Data management method, device, server and system

The embodiment of the invention provides a data management method, device, server and system. The data management method is applied to a management server and comprises the following steps: receivinga data search instruction; selecting a storage device with the highest storage level from storage devices corresponding to the different storage levels which are not read unsuccessfully, and reading the data conforming to the search instruction; when reading fails, returning to execute the operation of selecting the storage device with the highest level from the storage devices corresponding to the different storage levels which do not fail to be read yet, and reading the data conforming to the search instruction, otherwise, taking the successfully read data as a search result of the search instruction; wherein the storage level is obtained by dividing according to the difference of the data search efficiency of each storage device and the difference of the size of the storage space; according to the sequence of the storage levels from low to high, the data search efficiency is improved step by step, and the size of the storage space is reduced step by step. According to the scheme, the effect of considering the search efficiency and integrity of the data can be achieved.
Owner:北京乐我无限科技有限责任公司

Method for Automatic Generation of AI Model Based on Computational Graph Evolution

Provided is an AI model automatic generation method based on computational graph evolution. The method mainly comprises the following steps: pre-setting data; utilizing a genetic algorithm operator to generate a first-generation computational graph model, and computing the performance of the model according to a computational graph structure thereof; removing an invalid model and a repeated model, and taking the remaining models as candidate models and reserving same as seeds for the next generation; selecting a number of optimal models; the candidate models generating a new computational graph model by using the genetic algorithm operator; determining whether the new computational graph model generated in the last step has been generated; storing the new model as a computational graph model for a new generation, and determining whether same satisfies the pre-set data and an evolution ending condition; and summarizing evolution computational results, and selecting an optimal model. In the present invention, machine learning and deep learning can be carried out simultaneously; the repeated computation of the same model is prevented, and the model design efficiency is improved; the local optimum is jumped out of; the decline in the ability to search for a network is prevented; and evaluation can be directly carried out without training by means of actual data.
Owner:BEIJING BENYING NETWORK TECH CO LTD
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