Abstract: Examples of systems are described for calculating a probability of exceeding storage capacity of a virtualized system in a particular time period using probabilistic models. The probabilistic models may advantageously take variances of storage capacity into consideration.

Abstract: Rapid knowledge base discovery techniques. A database describes associations between knowledge base articles and closed problem cases. In periodic batch operations, the associations are used to generate solution probability predictors, each of which predictor corresponds to a probability that a particular knowledge base article was used to resolve a particular problem or case. The solution probability predictors comprise probability predictor parameter values associated with the set of words that occur in closed customer problem cases. A specialized data structure is populated with the probability predictor parameter values. When a new active customer problem case is opened, a set of words pertaining to the new, active case is constructed. The active case words are used with the specialized data structure to generate a probability value for each of a set of knowledge base articles. The knowledge base articles having the highest probability values are identified and presented in an ordered list.

Abstract: A machine learning model is trained to quantify the relationship of specific terms or groups of terms to the outcome of an event. To train the model, a set of data including structured and unstructured data and information describing previous outcomes of the event is received. The unstructured data is analyzed and features corresponding to one or more terms are identified, extracted, and merged together with features extracted from the structured data. The model is trained based at least in part on a set of the merged features, each of which is associated with a value quantifying a relationship of the feature to the outcome of the event. An output is generated based at least in part on a likelihood of the outcome of the event that is predicted using the model and input values corresponding to at least some of the set of features used to train the model.

Abstract: Examples of systems are described for calculating a probability of exceeding storage capacity of a virtualized system in a particular time period using probabilistic models. The probabilistic models may advantageously take variances of storage capacity into consideration.