Abstract: An apparatus includes circuitry configured to maintain a record of a plurality of owners and at least one test operation owned by an owner; prompt automatically the owner in response to a failure of the one test operation; maintain a log of actions taken on the one test operation, and provide availability to the log of actions; update an estimated time to completion, and notify a management entity of the updated estimated time to completion; mark and prioritize an order related to the one test operation, in response to the estimated time to completion being within a threshold of a delivery date; rank the marked order with other marked orders by a risk of not being able to meet the delivery date; and notify the owner of the ranking with an urgent message, in response to the marked order failing to meet the delivery date.

Abstract: An apparatus includes circuitry configured to maintain a record of a plurality of owners and at least one test operation owned by an owner; prompt automatically the owner in response to a failure of the one test operation; maintain a log of actions taken on the one test operation, and provide availability to the log of actions; update an estimated time to completion, and notify a management entity of the updated estimated time to completion; mark and prioritize an order related to the one test operation, in response to the estimated time to completion being within a threshold of a delivery date; rank the marked order with other marked orders by a risk of not being able to meet the delivery date; and notify the owner of the ranking with an urgent message, in response to the marked order failing to meet the delivery date.

Abstract: Methods and systems to provide a form of probabilistic labeling to associate an outage with a disturbance, which could itself be either known based on the available data or unknown. In the latter case, labeling is especially challenging, as it necessitates the discovery of the disturbance. One approach incorporates a statistical change-point analysis to time-series events that correspond to service tickets in the relevant geographic sub-regions. The method is calibrated to separate the regular periods from the environmental disturbance periods, under the assumption that disturbances significantly increase the rate of loss-causing events. To obtain the probability that a given loss-causing event is related to an environmental disturbance, the method leverages the difference between the rate of events expected in the absence of any disturbances (baseline) and the rate of actually observed events. In the analysis, the local disturbances are identified and estimators of their duration and magnitude are provided.

Abstract: A computer implemented method of preparing process data for use in an artificial intelligence (AI) model includes collecting and storing raw data as episodic data for each episode of a process. An episode data generator assigns an episode identifier each set of episodic data. The raw data per episode is transformed into a standardized episodic data format that is usable by the AI model. Metrics are assigned to the episodic data and the episodic data is aggregated in an episode store. The data in the episode store is used by a feature extraction and learning module to extract and rank features.

Abstract: A computer implemented method includes generating a pipeline graph having a plurality of layers, each of the plurality of layers having one or more machine learning components for performing a predictive modeling task. A plurality of pipelines are operated through the pipeline graph on a training dataset to determine a respective plurality of results. Each of the plurality of pipelines are distinct paths through selected ones of the one or more machine learning components at each of the plurality of layers. The plurality of results are compared to known results based on a user-defined metric to output one or more leader pipelines.

Abstract: Embodiments of the invention are directed a computer-implemented method for efficiently assessing data quality metrics. A non-limiting example of the computer-implemented method includes receiving, using a processor, a plurality of updates to data points in a data stream. The processor is further used to provide a plurality of data quality metrics (DQMs), and to maintain information on how much the plurality of DQMs are changing over time. The processor also maintains information on computational overhead for the plurality of DQMs, and also updates data quality information based on the maintained information.

Abstract: Embodiments of the invention are directed a computer-implemented method for efficiently assessing data quality metrics. A non-limiting example of the computer-implemented method includes receiving, using a processor, a plurality of updates to data points in a data stream. The processor is further used to provide a plurality of data quality metrics (DQMs), and to maintain information on how much the plurality of DQMs are changing over time. The processor also maintains information on computational overhead for the plurality of DQMs, and also updates data quality information based on the maintained information.

Abstract: Machine logic to change steps included in and/or parameters/parameter value used in artificial intelligence (“AI”) pipelines. For example, the machine logic may control what types of data (for example, sensor data) are received by the AI pipeline and/or have the data is culled in the pipeline prior to application of a machine learning and/or artificial intelligence algorithm.

Abstract: Embodiments of the invention are directed a computer-implemented method for assessing data quality. A non-limiting example of the computer-implemented method includes using a processor to receive a plurality of updates to data points in a data stream. The processor is further used to compute instances of a data quality metric (DQM) from the data points in the data stream. The instances of the DQM are configured to differentiate the data points in the data stream by time and assign a higher weight to the instances of the DQM computed from more recent data points in the data stream. The instances of the DQM are continuously updated as more of the data points are received by the processor while limiting cycles of the processor consumed by updating the instances of the DQM to a threshold.

Abstract: A computing device, method, and system are provided of improving data quality to conserve computational resources. The computing device receives a raw dataset. One or more data quality metric goals corresponding to the received raw dataset are received. A schema of the dataset is determined. An initial set of validation nodes is identified based on the schema of the dataset. The initial set of validation nodes are executed. A next set of validation nodes are iteratively expanded and executed based on the schema of the dataset until a termination criterion is reached. A corrected dataset of the raw dataset is provided based on the iterative execution of the initial and next set of validation nodes.