15. A signal-bearing medium tangibly embodying a program of machine-readable instructions executable by a digital processing apparatus to perform a method for training a neural network to rank datasets contained in a cache, where dataset accesses are logged as dataset entries of a dataset access log, the method comprising: designating multiple predetermined event triggers, each trigger comprising a predetermined event occurring in association with any one of the datasets contained in the cache; in response to the occurrence of an event trigger, the event trigger occurring at a trigger time and in association with a first dataset represented in the dataset access log, consulting the dataset access log to identify a latest access time of the first dataset; establishing one or more training times in an interval from the trigger time to the latest access time; for each training time, storing selected training input including characteristics of the first dataset in a training record, the characteristics having been exhibited by the first dataset at the training time and also storing training output including a representation of value provided by having the first dataset present in the cache to satisfy future requests for access to the first dataset; and according to a predetermined schedule, providing the training input from the training record as input to a single output back propagation neural network yielding a neural network output, and training the neural network according to any difference between the training output and the neural network output.
15. A signal-bearing medium tangibly embodying a program of machine-readable instructions executable by a digital processing apparatus to perform a method for training a neural network to rank datasets contained in a cache, where dataset accesses are logged as dataset entries of a dataset access log, the method comprising: designating multiple predetermined event triggers, each trigger comprising a predetermined event occurring in association with any one of the datasets contained in the cache; in response to the occurrence of an event trigger, the event trigger occurring at a trigger time and in association with a first dataset represented in the dataset access log, consulting the dataset access log to identify a latest access time of the first dataset; establishing one or more training times in an interval from the trigger time to the latest access time; for each training time, storing selected training input including characteristics of the first dataset in a training record, the characteristics having been exhibited by the first dataset at the training time and also storing training output including a representation of value provided by having the first dataset present in the cache to satisfy future requests for access to the first dataset; and according to a predetermined schedule, providing the training input from the training record as input to a single output back propagation neural network yielding a neural network output, and training the neural network according to any difference between the training output and the neural network output. 16. The medium of claim 15, wherein: the method further includes establishing predefined expiration criteria for dataset entries represented in the dataset access log and removing dataset entries meeting the expiration criteria from the log; and the predetermined event triggers include removal of a dataset entry from the log and access of a cached dataset.