QUERY-ORIENTED APPROXIMATE QUERY PROCESSING BASED ON MACHINE LEARNING TECHNIQUES

• Main Authors: Siddharth, Ghetia, Mitra, Subrata, Maddukuri, Vikas, Sheoran, Nikhil, Nair, Sapthotharan Krishnan, Jacobs, Thomas, Varshney, Jatin, Mishra, Laxmikant, Rao, Anup, Vaithyanathan, Shivakumar, Mai, Tung
• Format: Patent
• Language: English
• Published: 26.05.2022
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; ELECTRIC DIGITAL DATA PROCESSING; HANDLING RECORD CARRIERS; PHYSICS; PRESENTATION OF DATA; RECOGNITION OF DATA; RECORD CARRIERS

In some embodiments, a model training system trains a sample generation model configured to generate synthetic data entries for a dataset. The sample generation model includes a prior model for generating an estimated latent vector from a partially observed data entry, a proposal model for generating a latent vector from a data entry of the dataset and a mask corresponding to the partially observed data entry, and a generative model for generating the synthetic data entries from the latent vector and the partially observed data entry. The model training system trains the sample generation model to optimize an objective function that includes a first term determined using the synthetic data entries and a second term determined using the estimated latent vector and the latent vector. The trained sample generation model can be executed on a client computing device to service queries using the generated synthetic data entries.