Abstract: A method including: storing, by a computing device, obfuscated metadata from a plurality of interconnected computing environments into respective data puddles; identifying, by the computing device, a behavior of a first computing environment of the plurality of interconnected computing environments; determining, by the computing device, an expected future performance issue associated with a second computing environment of the plurality of interconnected computing environments based on the identified behavior of the first computing environment; identifying, by the computing device, a locus of the expected future performance issue associated with the second computing environment based on the identified behavior of the first computing environment; and outputting, by the computing device and to an operator of the second computing environment, an impact notification and remedial steps being taken to prevent the expected future performance issue.

Abstract: Automatically create abstractions of large sets of data and then probabilistic inferences based on the abstractions. The probabilistic inference is derived from the logical hierarchy using Bayesian statistics to infer a probabilistic event based upon a characteristic of the data in a hierarchy of synthetic events. The logical hierarchy of a set of a plurality of synthetic events is related by at least one characteristic of data is built by accessing a first set of data. The first set of data is organized based on a first characteristic. A second set of data different than the first set of data is accessed. A second set of data based is organized based on a second characteristic. The first characteristic and the second characteristic are processed to generate a synthetic event. The synthetic event is a third set of data representing a result of a mathematical computation defined by an operation S(p1)==>F(p2).

Abstract: Automatically create abstractions of large sets of data and then probabilistic inferences based on the abstractions. The probabilistic inference is derived from the logical hierarchy using Bayesian statistics to infer a probabilistic event based upon a characteristic of the data in a hierarchy of synthetic events. The logical hierarchy of a set of a plurality of synthetic events is related by at least one characteristic of data is built by accessing a first set of data. The first set of data is organized based on a first characteristic. A second set of data different than the first set of data is accessed. A second set of data based is organized based on a second characteristic. The first characteristic and the second characteristic are processed to generate a synthetic event. The synthetic event is a third set of data representing a result of a mathematical computation defined by an operation S(p1)==>F(p2).