International Business Machines Corporation
COGNITIVELY GENERATING PARAMETER SETTINGS FOR A GRAPH DATABASE

Abstract:

An approach is provided for generating graph database parameter settings. Parameter settings for importing data into a graph database are determined. A speed of importing simulated data into the graph database and a system resource usage are determined by executing an importing of the simulated data using the parameter settings and a simulated hardware environment. A reward associated with the parameter settings is determined. Using a policy network that includes convolutional neural networks and based on the reward and the settings, candidates of adjusted parameter settings are determined. Using a Monte Carlo tree search in multiple iterations to estimate changes in speeds of importing the simulated data and changes in system resource usages for candidates of the adjusted parameter settings, rewards for the candidates are determined. Based on the rewards, a candidate is selected as including final parameter settings that optimize an importing speed and system resource usage.