Abstract: A computer-implemented method executed using a first networked computer and comprising: receiving a digitally stored workflow pattern that specifies at least an input data source, a data transformation process, and an output data destination, the workflow pattern comprising a structured plurality of name declarations and value specifications that are human readable and machine readable, the data transformation process specified in the workflow pattern including one or more references to processing logic, a processing logic source outside the workflow pattern at which the processing logic is stored, and one or more available process engines that are capable of processing the processing logic; machine parsing the workflow pattern and dividing the workflow pattern into a plurality of execution units, each execution unit being associated with a particular process engine among the one or more available process engines; accessing the input data source specified in the workflow pattern and loading at least a portion

Abstract: A computer-implemented method executed using a first networked computer and comprising receiving a digitally stored workflow pattern that specifies at least an input data source, a data transformation process, an output data destination, a data quality assertion and a data quality source; the workflow pattern comprising a structured plurality of name declarations and value specifications that are human readable and machine readable; the data transformation process specified in the workflow pattern including one or more references to processing logic, a processing logic source outside the workflow pattern at which the processing logic is stored, and one or more available process engines that are capable of processing the processing logic; machine parsing the workflow pattern and dividing the workflow pattern into a plurality of execution units, each execution unit being associated with a particular process engine among the one or more available process engines; accessing the input data source specified in the

Abstract: Systems and methods for increasing the training value of input training datasets are described herein. In an embodiment, a server computer receives a plurality of input training datasets, each of the input training datasets comprising values for a plurality of parameters, a value indicating whether failure has occurred, and another value indicating the time of failure or the time of observation if no failure has occurred. For each input training dataset, the server computer generates a plurality of month-specific training datasets, each of which comprising a first value indicating a number of previous months where failure has not occurred and a second value indicating whether failure occurred during a month corresponding to the month-specific training data. The server computer trains a machine learning model using the plurality of month-specific training datasets.

Abstract: Systems and methods for augmenting incomplete training dataset for use in a machine learning system are described herein. In an embodiment, a server computer receives a plurality of input training datasets including one or more incomplete input training datasets and one or more complete datasets which contain one or more failure training datasets, the incomplete input training datasets comprising a plurality of parameters. Using the one or more failure training datasets, the server computer generates temporal failure data describing a likelihood of failure of an item as a function of time. Using the one or more complete training datasets, the server computer generates parameter specific likelihoods of failure of an item. The server computer augments the one or more incomplete input training datasets using the temporal failure data and/or the parameter specific likelihoods of failure to create one or more augmented training datasets.