{"id":{"repo_id":"york","oai_identifier":"oai:yorkspace.library.yorku.ca:10315/41413"},"canonical_url":"https://search.dev.ndltd.org/etd/york/oai:yorkspace.library.yorku.ca:10315/41413","repository":{"repo_id":"york","name":"York University","base_url":"https://yorkspace.library.yorku.ca/oai/request"},"display":{"title":"Batch Query Memory Prediction Using Deep Query Template Representations","abstract":"This 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.","abstract_html":"This 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. 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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."]},{"key":"dc:title","label":"Title","values":["Batch Query Memory Prediction Using Deep Query Template Representations"]}]}],"canonical_facts":{"dc:contributor.advisor":["Papagelis, Manos","Litoiu, Marin"],"dc:creator":["Jaramillo, Nicolas Andres"],"dc:date.accessioned":["2023-08-04T18:14:19Z"],"dc:date.available":["2023-08-04T18:14:19Z"],"dc:date.issued":["2023-08-04"],"dc:description.abstract":["This thesis introduces a novel approach called LearnedWMP for predicting the memory cost demand of a batch of queries in a database workload. 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