Abstract: A self-learning computer-based system has access to multiple runtime modules that are each capable of performing a particular algorithm. Each runtime module implements the algorithm with different code or runs in a different runtime environment. The system responds to a request to run the algorithm by selecting the runtime module or runtime environment that the system predicts will provide the most desirable results based on parameters like accuracy, performance, cost, resource-efficiency, or policy compliance. The system learns how to make such predictions through training sessions conducted by a machine-learning component. This training teaches the system that previous module selections produced certain types of results in the presence of certain conditions. After determining whether similar conditions currently exist, the system uses rules inferred from the training sessions to select the runtime module most likely to produce desired results.

Abstract: A self-learning computer-based system has access to multiple runtime modules that are each capable of performing a particular algorithm. Each runtime module implements the algorithm with different code or runs in a different runtime environment. The system responds to a request to run the algorithm by selecting the runtime module or runtime environment that the system predicts will provide the most desirable results based on parameters like accuracy, performance, cost, resource-efficiency, or policy compliance. The system learns how to make such predictions through training sessions conducted by a machine-learning component. This training teaches the system that previous module selections produced certain types of results in the presence of certain conditions. After determining whether similar conditions currently exist, the system uses rules inferred from the training sessions to select the runtime module most likely to produce desired results.

Abstract: Methods and apparatus, including computer program products, implementing and using techniques for populating a data cache on a server. Data requests received by the server are collected in a repository. A data mining algorithm is applied to the collected data requests to predict a set of data that is likely to be requested during an upcoming time period. It is determined whether the complete set of predicted data exists in the data cache. If the complete set of predicted data does not exist in the data cache, the missing data is retrieved from a database and added to the data cache.

Abstract: Methods and apparatus, including computer program products, implementing and using techniques for populating a data cache on a server. Data requests received by the server are collected in a repository. A data mining algorithm is applied to the collected data requests to predict a set of data that is likely to be requested during an upcoming time period. It is determined whether the complete set of predicted data exists in the data cache. If the complete set of predicted data does not exist in the data cache, the missing data is retrieved from a database and added to the data cache.