Alteration of historical data representations using data repository performance metrics

Abstract

Techniques are disclosed relating to using machine learning techniques to predict storage configurations for historical data. In some embodiments, a computer system stores representations of historical data according to a current set of storage parameters. The representations may include snapshots of historical data in a data repository at different points in time. The computer system may receive queries for historical data specifying points in time from which to retrieve the historical data. In some embodiments, the computer system responds to the queries using the stored representations and determines performance metrics for the responses. In some embodiments, the computer system trains a machine learning model using the performance metrics. Based on output of the trained model, the computer system updates the current set of storage parameters. The updating may affect subsequent storage of representations in the data repository, which may advantageously improve query response times and decrease repository storage size.