Approximate Query Processing for Group-By Queries based on Conditional Generative Models

• URL: http://arxiv.org/abs/2101.02914v1
• Date: Fri, 8 Jan 2021 08:49:21 GMT
• Title: Approximate Query Processing for Group-By Queries based on Conditional Generative Models
• Authors: Meifan Zhang and Hongzhi Wang
• Abstract summary: Group-by query involves multiple values, which makes it difficult to provide sufficiently accurate estimations for all the groups. Stratified sampling improves the accuracy compared with the uniform sampling, but the samples chosen for some special queries cannot work for other queries. Online sampling chooses samples for the given query at query time, but it requires a long latency. The proposed framework can be combined with stratified sampling and online aggregation to improve the estimation accuracy for group-by queries.
• Score: 3.9837198605506963
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Group-By query is an important kind of query, which is common and widely used in data warehouses, data analytics, and data visualization. Approximate query processing is an effective way to increase the querying efficiency on big data. The answer to a group-by query involves multiple values, which makes it difficult to provide sufficiently accurate estimations for all the groups. Stratified sampling improves the accuracy compared with the uniform sampling, but the samples chosen for some special queries cannot work for other queries. Online sampling chooses samples for the given query at query time, but it requires a long latency. Thus, it is a challenge to achieve both accuracy and efficiency at the same time. Facing such challenge, in this work, we propose a sample generation framework based on a conditional generative model. The sample generation framework can generate any number of samples for the given query without accessing the data. The proposed framework based on the lightweight model can be combined with stratified sampling and online aggregation to improve the estimation accuracy for group-by queries. The experimental results show that our proposed methods are both efficient and accurate.

Related papers

• Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity [59.57065228857247]
  Retrieval-augmented Large Language Models (LLMs) have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA) We propose a novel adaptive QA framework, that can dynamically select the most suitable strategy for (retrieval-augmented) LLMs based on the query complexity. We validate our model on a set of open-domain QA datasets, covering multiple query complexities, and show that ours enhances the overall efficiency and accuracy of QA systems.
  arXiv  Detail & Related papers  (2024-03-21T13:52:30Z)
• MeaeQ: Mount Model Extraction Attacks with Efficient Queries [6.1106195466129485]
  We study model extraction attacks in natural language processing (NLP) We propose MeaeQ, a straightforward yet effective method to address these issues. MeaeQ achieves higher functional similarity to the victim model than baselines while requiring fewer queries.
  arXiv  Detail & Related papers  (2023-10-21T16:07:16Z)
• Let's Sample Step by Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMs [60.58434523646137]
  A popular approach for improving the correctness of output from large language models (LLMs) is Self-Consistency. We introduce Adaptive-Consistency, a cost-efficient, model-agnostic technique that dynamically adjusts the number of samples per question. Our experiments show that Adaptive-Consistency reduces sample budget by up to 7.9 times with an average accuracy drop of less than 0.1%.
  arXiv  Detail & Related papers  (2023-05-19T17:49:25Z)
• Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios [79.02009938011447]
  We propose a sampling scheme, which selects optimal subsets of unlabeled samples with fixed batch size from the unlabeled data pool. Experimental results show its effectiveness on both classical Machine Learning (ML) and Deep Learning (DL) tasks.
  arXiv  Detail & Related papers  (2022-07-04T04:11:44Z)
• Electra: Conditional Generative Model based Predicate-Aware Query Approximation [10.056919500568013]
  ELECTRA is a predicate-aware AQP system that can answer analytics-style queries with a large number of predicates with much smaller approximation errors. Our evaluations with four different baselines on three real-world datasets show that ELECTRA provides lower AQP error for large number of predicates compared to baselines.
  arXiv  Detail & Related papers  (2022-01-28T21:13:26Z)
• How to Query An Oracle? Efficient Strategies to Label Data [59.89900843097016]
  We consider the basic problem of querying an expert oracle for labeling a dataset in machine learning. We present a randomized batch algorithm that operates on a round-by-round basis to label the samples and achieves a query rate of $O(fracNk2)$. In addition, we present an adaptive greedy query scheme, which achieves an average rate of $approx 0.2N$ queries per sample with triplet queries.
  arXiv  Detail & Related papers  (2021-10-05T20:15:35Z)
• Local policy search with Bayesian optimization [73.0364959221845]
  Reinforcement learning aims to find an optimal policy by interaction with an environment. Policy gradients for local search are often obtained from random perturbations. We develop an algorithm utilizing a probabilistic model of the objective function and its gradient.
  arXiv  Detail & Related papers  (2021-06-22T16:07:02Z)
• Text Summarization with Latent Queries [60.468323530248945]
  We introduce LaQSum, the first unified text summarization system that learns Latent Queries from documents for abstractive summarization with any existing query forms. Under a deep generative framework, our system jointly optimize a latent query model and a conditional language model, allowing users to plug-and-play queries of any type at test time. Our system robustly outperforms strong comparison systems across summarization benchmarks with different query types, document settings, and target domains.
  arXiv  Detail & Related papers  (2021-05-31T21:14:58Z)
• DeepSampling: Selectivity Estimation with Predicted Error and Response Time [7.23389716633927]
  This paper proposes DeepSampling, a deep-learning-based model that predicts the accuracy of a sample-based AQP algorithm. DeepSampling is the first system that provides a reliable tool for existing spatial databases to control the accuracy of AQP.
  arXiv  Detail & Related papers  (2020-08-16T03:23:01Z)
• Approximating a Target Distribution using Weight Queries [25.392248158616862]
  We propose an interactive algorithm that iteratively selects data set examples and performs corresponding weight queries. We derive an approximation bound on the total variation distance between the reweighting found by the algorithm and the best achievable reweighting.
  arXiv  Detail & Related papers  (2020-06-24T11:17:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.