1. A method comprising: creating or receiving, by one or more computers, a language syntax to define at least in part: a plurality of rules for assigning token types to a stream of messages to obtain a stream of contextualized token instances; a plurality of production rules for detecting deterministic event instances from the stream of contextualized token instances; a plurality of state type rules for detecting state type instances, wherein a state type rule comprises one or more deterministic event types with respective probability weights, wherein a probability weight is the likelihood that, detecting the deterministic event type that results in the system, the respective state type is executed; and a plurality of respective pattern rules to define respective Markov chains, with each of the pattern rules comprising a set of state types and transition probability weights between the state types; receiving or obtaining or being presented, by the one or more computers, with the run-time data comprising a stream of messages for systems of a first plurality of the user IDs, wherein the runtime data for a respective one of the user IDs comprises and/or is derived from run-time instantiations of objects occurring on the monitored computer for the respective user ID, with the objects comprising one or more selected from the group of methods, system objects, object events, dynamic GUI objects, network component objects, wherein the object events comprise at least one selected from the group of operating system events, application events, and web browser events and network events generated or affecting directly or indirectly the respective monitored computer; parsing, by the one or more computers, to apply the language syntax to the stream of messages to extract a stream of the contextualized token instances from the stream of messages based at least in part on the language syntax; detecting, by the one or more computers, from the stream of the token instances, a stream of the deterministic user event instances, based at least in part on the language syntax; selecting, by the one or more computers, a plurality of the Markov chains containing one or more of the state type instances that are implied from the stream of messages, based at least in part on the respective probability weights of the state types from the deterministic user event instances that were detected; computing, by the one or more computers, a probability that the observed system is executing the respective pattern type for each of the respective Markov chains selected; and determining, by the one or more computers, based at least in part on one or more of the selected Markov chains and next state transitions in the respective Markov chains selected and/or one or more of predicted states in the respective Markov chains selected, predicted prioritization data for electronic resources for the observed system and/or a predicted level of electronic resources that may be needed by the system generating the messages.