Abstract: Example implementations described herein are directed to project management systems and milestone management. In example implementations, for input of a project having project data and employee data, such implementations involve executing feature extraction on the project data and the employee data to generate features; executing a self-profiling algorithm configured with unsupervised machine learning on the generated features to derive clusters and anomalies of the project; executing a performance monitoring process on the generated features to determine a probability of a transition to a milestone associated with a key performance indicator; and executing a supervised machine learning model on the generated features, the derived clusters, the derived anomalies, the probability of the transition to the milestone associated with a key performance indicator to generate a predicted performance of the project.

Abstract: Example implementations described herein can be directed to detecting a human disturbance from sensor data streamed from one or more sensors in a network of sensors; processing the detected human disturbance to determine a probability of a chainsaw event (e.g., light chainsaw or dense chainsaw event) and an estimated lead time for the chainsaw event for an area associated with the one or more sensors; and determining, for neighboring sensors to the one or more sensors in the network of sensors, a probability of a change of state to the human disturbance or the chainsaw event for other areas associated with the neighboring sensors.

Abstract: Example implementations described herein are directed to a system with a plurality of sensors associated with a plurality of apparatuses, which involves, for receipt of a hypothesis canvas and meta-data from the plurality of sensors, generating a plurality of meta-pipeline stages, the plurality of meta-pipeline stages having the meta-data and one or more stages, each of the one or more stages being an analytics application or a machine learning application stacked in execution order based on the meta-data and the hypothesis canvas; and executing the plurality of meta-pipeline stages in execution order on the meta-data to generate an analytics model for the system.

Abstract: Example implementations described herein are directed to a novel Automated Machine Learning (AutoML) framework that is generated on an AutoML library so as to facilitate functionality to incorporate multiple machine learning model libraries within the same framework through a solution configuration file. The example implementations further involve a solution generator that identifies solution candidates and parameters for machine learning models to be applied to a dataset specified by the solution configuration file.

Abstract: Example implementations described herein are directed to management of a system comprising a plurality of apparatuses providing unlabeled sensor data, which can involve executing feature extraction on the unlabeled sensor data to generate a plurality of features; executing failure detection by processing the plurality of features with a failure detection model to generate failure detection labels, the failure detection model generated from a machine learning framework that applies supervised machine learning on unsupervised machine learning models generated from unsupervised machine learning; and providing extracted features and the failure detection label to a failure prediction model to generate failure prediction and a sequence of features.

Abstract: Example implementations described herein are directed to generating time series features from structured data and unstructured data managed in a data lake; executing a feature selection process on the time series features; conducting supervised training on the selected time series features across a plurality of different types of models iteratively to generate a plurality of models; selecting a best model from the plurality of models for deployment; and continuously retraining the model from the structured and unstructured data while the best model exceeds a predetermined criteria.

Abstract: Example implementations described herein are directed to systems and methods for generation and deployment of automated and autonomous self-learning machine learning models, which can include generating a predictive model and a prescriptive model through an offline learning process at a first system; controlling operations of a second system through deploying the predictive model and the prescriptive model to the second system; and autonomously updating the predictive model and the prescriptive model from feedback from the second system through an online learning process while the prescriptive model and the predictive model are deployed on the second system.

Abstract: Example implementations described herein are directed to a novel Automated Machine Learning (AutoML) framework that is generated on an AutoML library so as to facilitate functionality to incorporate multiple machine learning model libraries within the same framework through a solution configuration file. The example implementations further involve a solution generator that identifies solution candidates and parameters for machine learning models to be applied to a dataset specified by the solution configuration file.