Batch Query Memory Prediction Using Deep Query Template Representations

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dc.contributor.advisorManos Papagelis
dc.contributor.advisorMarin Litoiu
dc.contributor.authorNicolas Andres Jaramillo
dc.date.accessioned2023-08-04T18:14:19Z
dc.date.available2023-08-04T18:14:19Z
dc.date.issued2023-08-04
dc.date.updated2023-08-04T18:14:19Z
dc.degree.disciplineComputer Science
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractThis thesis introduces a novel approach called LearnedWMP for predicting the memory cost demand of a batch of queries in a database workload. Existing techniques focus on estimating the resource demand of individual queries, failing to capture the net resource demand of a workload. LearnedWMP leverages the query plan and groups queries with similar characteristics into pre-built templates. A histogram representation of these templates is generated for the workload, and a regressor predicts the resource demand, specifically memory cost, based on this histogram. Experimental results using three database benchmarks demonstrate a 47.6% improvement in memory estimation compared to the state-of-the-art. Additionally, the approach outperforms various machine and deep learning techniques for individual query prediction, offering a 3x to 10x faster and at least 50% smaller model size.
dc.identifier.urihttps://hdl.handle.net/10315/41413
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectComputer science
dc.subjectArtificial intelligence
dc.subject.keywordsResource Demand Prediction
dc.subject.keywordsDatabase Optimization
dc.subject.keywordsWorkload Memory Prediction
dc.subject.keywordsDistribution Regression
dc.subject.keywordsLearnedWMP
dc.subject.keywordsQuery plan
dc.subject.keywordsQuery Templates
dc.subject.keywordsMemory Cost Estimation
dc.subject.keywordsMemory Cost Prediction
dc.subject.keywordsPerformance Improvement
dc.subject.keywordsDatabase
dc.subject.keywordsDatabase Resource Demand
dc.subject.keywordsMachine Learning
dc.subject.keywordsArtificial Intelligence
dc.subject.keywordsDeep Learning
dc.subject.keywords
dc.titleBatch Query Memory Prediction Using Deep Query Template Representations
dc.typeElectronic Thesis or Dissertation

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