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Efficient approximate query processing algorithm based on conditional generative model

A technology for condition generation and processing algorithms, applied in the field of information retrieval, can solve problems such as model collapse and difficult internal network balance, and achieve the effects of eliminating collapse, improving performance, and reducing errors

Active Publication Date: 2021-07-27
HARBIN INST OF TECH AT WEIHAI
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, it is difficult for GAN to ensure the balance of the internal network during the training process, and it is prone to model collapse

Method used

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  • Efficient approximate query processing algorithm based on conditional generative model
  • Efficient approximate query processing algorithm based on conditional generative model
  • Efficient approximate query processing algorithm based on conditional generative model

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Embodiment Construction

[0036] The high-efficiency approximate query processing algorithm based on the conditional generation model provided by the present invention, the specific implementation process and steps are described in detail as follows:

[0037] 1. Construction of Conditional Variation Generative Adversarial Network Model Based on Wasserstein

[0038] The Conditional Variational Wasserstein Generative Adversarial Network (CVWGAN) model based on Wasserstein provided by the present invention is based on the network structure of CGAN and integrated into the encoding network in CVAE to ensure the stability of the overall model. The specific structure of the model is as figure 1 shown.

[0039] The model consists of an encoding network (Encoder, E), a generating network (Generator, G) and a discriminator network (Discriminator, D). Among them, the encoding network maps the unknown distribution of real data to the common distribution in the latent layer space (LatentSpace, LS). parameters su...

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Abstract

The invention belongs to the technical field of information retrieval, and particularly relates to an approximate query processing algorithm. The invention discloses an efficient approximate query processing algorithm based on a conditional generative model. The algorithm comprises the following steps: acquiring a pre-aggregation value of user query by adopting aggregation pre-calculation; processing the user query to obtain a new query newQ for estimating the difference between the user query and the pre-aggregation range and a selected pre-aggregation value; constructing a Wasserstein-based conditional variation generative adversarial network model, and generating a data sample for a new query newQ by using the trained model; and filtering the generated data sample, combining the filtered data sample with the selected pre-aggregation value, and calculating to obtain a final query estimation value. According to the method, an efficient depth generation model is constructed, a Wasserstein distance is introduced as error measurement, and model collapse is eliminated; the model is applied to approximate query and is combined with aggregation pre-calculation, and meanwhile, an approximate query error is reduced by adopting a voting algorithm.

Description

technical field [0001] The invention belongs to the technical field of information retrieval, and in particular relates to an approximate query processing algorithm. Background technique [0002] With the rapid development of information technology, the amount of data continues to grow at an explosive rate, making it difficult for traditional database system software to answer user aggregate queries within interactive response time. In specific decision analysis tasks, users usually only need to obtain general trends from the data, and do not require precise results. Moreover, in actual situations, the data distribution is not uniform, and there is a serious skew problem. Therefore, how to obtain query results with higher precision with faster response speed in massive skewed data is of great significance. [0003] Approximate Query Processing (AQP) Algorithm (CHAUDHURI S, DINGB, KANDULA S. Approximate query processing: no silver bullet[C] / / Proceedings of the 2017ACM Inter...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/2458G06K9/62G06N3/04G06N3/08
CPCG06F16/2462G06N3/08G06N3/045G06F18/23213G06F18/2411G06F18/24323
Inventor 白文超韩希先何京璇
Owner HARBIN INST OF TECH AT WEIHAI
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