Expand your Training Limits! Generating Training Data for ML-based Data Management
Francesco Ventura, Zoi Kaoudi, Jorge-Arnulfo Quiané-Ruiz, Volker Markl
Abstract
Machine Learning (ML) is quickly becoming a prominent method in many data management components, including query optimizers which have recently shown very promising results. However, the low availability of training data (i.e., large query workloads with execution time or output cardinality as labels) widely limits further advancement in research and compromises the technology transfer from research to industry. Collecting a labeled query workload has a very high cost in terms of time and money due to the development and execution of thousands of realistic queries/jobs.
Topics & Concepts
Computer scienceWorkloadCardinality (data modeling)Training (meteorology)Training setData managementData modelingData miningDatabaseArtificial intelligenceOperating systemPhysicsMeteorologyData Stream Mining TechniquesAdvanced Database Systems and QueriesMachine Learning and Data Classification