Abstract: Systems, computer program products, and methods are described herein for evaluating computing programming code changes using AI to improve computing performance and component consumption. The present invention is configured to identify at least one code commit change; determine whether the at least one code commit change matches at least one runtime restriction statement; classify the at least one code commit change as a restriction code commit change; identify at least one object reference, at least one class, and at least one method associated with the restriction code commit change; initiate a trigger-class method trace module on the restriction code commit change, wherein the trigger-class method trace module generates at least one runtime restriction weight associated with each restriction code commit change; and initiate a trigger flow trace module on the restriction code commit change and generate at least one suggested code commit change.

Abstract: A method, computer system, and a computer program product for data pipeline prioritization is provided. Embodiments may include receiving, by a cognitive rules engine, one or more data pipelines. Embodiments may then include analyzing, using a computational method of the cognitive rules engine, the one or more data pipelines. Embodiments may lastly include prioritizing the one or more data pipelines based on a result of the computational method of the cognitive rules engine.

Abstract: A method of data classification includes: identifying a cluster of data classes; classifying columns of a current data set; identifying the cluster in the current data set; determining, based on the cluster, an expected column is missing from the current data set; determining a neighboring data set; identifying the expected column in the neighboring data set; classifying the expected column in the neighboring data set; creating a new data class in the current data set; and classifying an unclassified column in the current data set or the neighboring data set with the new data class.

Abstract: A method of data classification includes: identifying a cluster of data classes; classifying columns of a current data set; identifying the cluster in the current data set; determining, based on the cluster, an expected column is missing from the current data set; determining a neighboring data set; identifying the expected column in the neighboring data set; classifying the expected column in the neighboring data set; creating a new data class in the current data set; and classifying an unclassified column in the current data set or the neighboring data set with the new data class.

Abstract: The present disclosure describes the generation and use of word embeddings as part of natural language understanding (NLU) processing performed by a natural language processing system. In at least some examples, the word embeddings may be generated from text corpuses including at least text (representing spoken user inputs) output from automatic speech recognition (ASR) processing. In at least some examples, the word embeddings may be generated from text output from ASR processing and natural language text corresponding to one or more Internet webpages.

Abstract: A method and system for improving data and memory reorganization and storage technology is provided. The method includes configuring data capture and analysis settings of a database system resulting in configured data capture settings. A data and associated memory analysis request is received and specified test code is selected. A specified portion of data and associated memory is selected and the specified analysis code is executed resulting in execution of said specified type of analysis with respect to the specified portion of said data and associated memory. The specified portion of said data and associated memory is modified and stored.

Abstract: A method and system for improving data and memory reorganization and storage technology is provided. The method includes configuring data capture and analysis settings of a database system resulting in configured data capture settings. A data and associated memory analysis request is received and specified test code is selected. A specified portion of data and associated memory is selected and the specified analysis code is executed resulting in execution of said specified type of analysis with respect to the specified portion of said data and associated memory. The specified portion of said data and associated memory is modified and stored.

Abstract: A method, computer system, and a computer program product for data pipeline prioritization is provided. Embodiments may include receiving, by a cognitive rules engine, one or more data pipelines. Embodiments may then include analyzing, using a computational method of the cognitive rules engine, the one or more data pipelines. Embodiments may lastly include prioritizing the one or more data pipelines based on a result of the computational method of the cognitive rules engine.