Abstract: A system creates an abstraction layer surrounding a diverse data system including multiple different databases. Data is received from data sources and ingested into the various databases according to a core model. New instances of the core model are created and added to a larger linked data model (LDM) when new data sources are added to the system. The LDM captures the linkages between different linked data objects and links across different databases. Accordingly, applications are able to access or explore the linked data stored in different databases without prior knowledge of the linking relationships.

Abstract: The present disclosure relates to a system and a method for model control platform stack. The method includes, at an input layer of a model control platform stack, receiving input data. At a governance layer of the model control platform stack, the method includes maintaining a probe and model inventories; selecting a model, a monitoring location point, and a probe; and deploying, based on the selections of the probe and the model, a container to an orchestration layer of the model control platform stack. At the orchestration layer of the model control platform stack, the method includes accessing the container; using the container to deploy the probe and the model; scheduling an execution of the model to determine inference associated with the input data; during the execution, extracting probe data, using the probe, from the monitoring location point; and adjusting, based on the probe data and the inference, the model.

Abstract: A system provides feedback driven end-to-end state control of a data model. A data model may be used to model the behavior of a petrochemical refinery to predict future events. The system may be used to ensure proper operation of the data model. Contingency data models may be executed when a failure is detected. Further, when the system detects accuracy that is out of tolerance, the system may initiate retraining of the data model being currently used.

Abstract: A system maintains a knowledge layout to support the building of event and analytics models in parity. The system uses the event models to provide a snapshot of the relevant conditions present when a challenge event occurs. The system uses the analytics models to select one or more actions (which may include robotic tasks) to respond to the challenge condition. In some cases, the system may render continued response compulsory until a successful response to the challenge event is achieved.

Abstract: A method for certifying and deploying instruction code includes communicating, from a developer system, a first record to a decentralized database that indicates an availability of the instruction code. A second record is received by the developer system from the decentralized database and indicates a request to certify the instruction code, the second request having been communicated by a certification system. In response to receipt of the second record the developer system communicates a third record to the decentralized database that authorizes the certification system to retrieve the instruction code from the developer system. The developer system subsequently communicates the instruction code to the certification system. Communication of the instruction code facilitates execution of the instruction code on the certification system to thereby certify functionality of the instruction code.

Abstract: A systems implements a gradient descent calculation, regression calculation, or other machine learning calculation on a dataset (e.g., a global dataset) using a coordination node including coordination circuitry that coordinates multiple worker nodes to create a distributed calculation architecture. In some cases, the worker nodes each hold a portion of the dataset and operate on their respective portion. In some cases, the gradient descent calculation, regression calculation, or other machine learning calculation is used to implement a targeted maximum likelihood scheme for causal inference estimation. The targeted maximum likelihood scheme may be used to conduct causal analysis of the observational data.

Abstract: A system provides feedback driven end-to-end state control of a data model. A data model may be used to model the behavior of a monitored system to predict future events. The system may be used to ensure proper operation of the data model. Contingency data models may be executed when a failure is detected. Further, when the system detects accuracy that is out of tolerance, the system may initiate retraining of the data model being currently used.

Abstract: A model management tool is provided for performing analysis on data in a knowledge graph representation and enforcing data standardization to increase performance when reusing existing models to develop new artificial intelligence applications.

Abstract: Complex computer system architectures are described for providing a machine learning model management tool that monitors, detects, and makes revisions to machine learning models to prevent declines and maintain robustness and fairness in machine learning model performance in production over time. The machine learning model management tool achieves its goals via intelligent management, organization, and orchestration of detection, inspection, and correction engines.

Abstract: A model retraining tool is provided for utilizing a knowledge graph to retrain analytical models used in production. The model retraining tool retrains the analytical models to improve performance of the analytical models in an efficient and resource conserving manner.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating multiple distinct test plans using a computing system. The system merges the each of the test plans to produce a first test plan and obtains performance data about automated execution of the first test plan by a robotics system. The system generates learned inferences for determining predictions about execution of the first test plan based on analysis of the performance data and adjusts the first test plan based on the learned inferences. The system generates a second test plan for execution by the computing system based on adjustments to the first test plan and performs the second test plan to test multiple devices using the robotics system.

Abstract: A system includes circuitry for cryptographic data share controls for distributed ledger technology based data constructs. The system may support placement of compute data on to a distributed ledger technology based data construct. The compute data may have multiple layers of encryption to support permissions and coordination of processing operations for application to the compute data. The multiple layers of encryption may include a homomorphic layer to allow sharing of the compute data for processing by a compute party without divulging the content of the compute data with the compute party. While in the homomorphically encrypted form, the homomorphic compute data supports the application of processing operations while maintaining the secrecy of the underlying data.

Abstract: A data pipeline architecture is integrated with an analytics processing stack. The data pipeline architecture may receive incoming data streams from multiple diverse endpoint systems. The data pipeline architecture may include converter interface circuitry with multiple dynamic converters configured to convert the diverse incoming data stream into one or more interchange formats for processing by the analytics processing stack. The analytics processing stack may include multiple layers with insight processing layer circuitry above analysis layer circuitry. The analysis layer circuitry may control analytics models and rule application. The insight processing layer circuitry may monitor output from the analysis layer circuitry and generate insight adjustments responsive to rule changes and analytics model parameter changes produced at the analysis layer circuitry.

Abstract: Increases in global population have led to unprecedented challenges in meeting every day needs. Vehicle parking is one such challenge given the limited parking spaces available at any destination, whether at work, at a shopping center, or any other location. A parking resource analysis system includes a stepwise simulator system and a role based access system. The stepwise simulator system tests, simulates, and tunes specific models for issuing parking control actions with respect to a specific lot. The role based access system may be physically located at the parking lot. The role based access system implements dynamic loading or dynamic access of role specific models for issuing parking control actions tailored to the role of the particular individual searching for a parking space.

Abstract: A method, computer-readable medium, and system for provisioning computing resources across multiple cloud providers and/or data centers are disclosed. A graphical user interface is used to select a plurality of computing resources and at least one cloud provider and/or at least one data center for providing the plurality of computing resources. Scripts associated with the at least one cloud provider and/or at least one data center are accessed, where each script is capable of automatically setting up a computing resource on an associated cloud provider or associated data center. The scripts are then used to automatically allocate and/or configure the computing resources on the at least one cloud provider and/or at least one data center. As such, computing resources can be automatically provisioned using a generic graphical user interface and without a user having skills or credentials specific to each cloud provider and/or data center.

Abstract: A system receives sensor data from sensing parameters of a piece of factory equipment. The system includes a first model to generate predicted degradation states of the piece of factory equipment by being trained to generate a stochastic degradation model for classification of the predicted degradation states of a particular asset. The system includes a second model to which the predicted degradation states are provided. The second model trained to generate a covariate indicative of a failure condition of the piece of factory equipment. The system may supply the covariate to the first model to generate predicted degradation states compensated with the covariate. From the predicted degradation states compensated with the covariate a policy of a maintenance action may be generated with the system to optimize life expectancy of the piece of factory equipment. The system may adjust operation of the piece of factory equipment based on the maintenance action.

Abstract: A system includes circuitry for cryptographic data share controls for distributed ledger technology based data constructs. The system may support placement of compute data on to a distributed ledger technology based data construct. The compute data may have multiple layers of encryption to support permissions and coordination of processing operations for application to the compute data. The multiple layers of encryption may include a homomorphic layer to allow sharing of the compute data for processing by a compute party without divulging the content of the compute data with the compute party. While in the homomorphically encrypted form, the homomorphic compute data supports the application of processing operations while maintaining the secrecy of the underlying data.

Abstract: A data pipeline architecture is integrated with an analytics processing stack. The data pipeline architecture may receive incoming data streams from multiple diverse endpoint systems. The data pipeline architecture may include converter interface circuitry with multiple dynamic converters configured to convert the diverse incoming data stream into one or more interchange formats for processing by the analytics processing stack. The analytics processing stack may include multiple layers with insight processing layer circuitry above analysis layer circuitry. The analysis layer circuitry may control analytics models and rule application. The insight processing layer circuitry may monitor output from the analysis layer circuitry and generate insight adjustments responsive to rule changes and analytics model parameter changes produced at the analysis layer circuitry.

Abstract: A system includes circuitry for cryptographic data share controls for distributed ledger technology based data constructs. The system may support placement of compute data on to a distributed ledger technology based data construct. The compute data may have multiple layers of encryption to support permissions and coordination of processing operations for application to the compute data. The multiple layers of encryption may include a homomorphic layer to allow sharing of the compute data for processing by a compute party without divulging the content of the compute data with the compute party. While in the homomorphically encrypted form, the homomorphic compute data supports the application of processing operations while maintaining the secrecy of the underlying data.

Abstract: A method for certifying and deploying instruction code includes communicating, from a developer system, a first record to a decentralized database that indicates an availability of the instruction code. A second record is received by the developer system from the decentralized database and indicates a request to certify the instruction code, the second request having been communicated by a certification system. In response to receipt of the second record the developer system communicates a third record to the decentralized database that authorizes the certification system to retrieve the instruction code from the developer system. The developer system subsequently communicates the instruction code to the certification system. Communication of the instruction code facilitates execution of the instruction code on the certification system to thereby certify functionality of the instruction code.