Abstract: Described are techniques to enable computers to efficiently determine if they should run a program based on an immediate (i.e., real-time, etc.) analysis of the program. Such an approach leverages highly trained ensemble machine learning algorithms to create a real-time discernment on a combination of static and dynamic features collected from the program, the computer's current environment, and external factors. Related apparatus, systems, techniques and articles are also described.

Abstract: A sample of data is placed within a directed graph that comprises a plurality of hierarchical nodes that form a queue of work items for a particular worker class that are used to process the sample of data. Subsequently, work items are scheduled within the queue for each of a plurality of workers by traversing the nodes of the directed graph. The work items are then served to the workers according to the queue. Results can later be received from the workers for the work items (the nodes of the directed graph are traversed based on the received results). In addition, in some variations, the results can be classified so that one or models can be generated. Related systems, methods, and computer program products are also described.