Abstract: Knowledge graph systems are disclosed for implementing multiple approaches, including stand alone or combined approaches, for knowledge graph pruning. The approaches are based on graph sampling work such as, for example, information gain theory. The approaches are applied by a knowledge graph system to perform schema pruning, automatic graph pruning, and query correlation for improving query performance.

Abstract: A lifecycle platform for creation, ingestion, version control, and contextual query of knowledge graph is disclosed. Such a platform may be used to create and deploy a knowledge graph by reusing and merging knowledge defined in existing and validated data models. The platform tracks changes made to the knowledge graph after being deployed and provides version tracking of the knowledge graph and its underlying namespaces. The platform further provides a subscribable service for contextual viewing and query of portions and/or subset versions of the knowledge graph. Such a platform may be provided as an agnostic plugin to a specific vendor knowledge graph solution space.

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: In some implementations, an application programming interfaces (API) manager may receive, at a set of artificial intelligence (AI) APIs, a set of inputs from a set of on-site devices. Accordingly, the API manager may route the set of inputs to a corresponding set of remote servers and may receive, from at least one server of the corresponding set of remote servers, at least one response based on at least one input, from the set of inputs, routed to the at least one server. The API manager may transmit the at least one response to a corresponding device from the set of on-site devices. Further, the API manager may modify at least one API, of the set of AI APIs, based on a traffic pattern associated with the set of inputs and the at least one response.

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 maintains a knowledge layout to support the building of event response recommendations. Meta-graph patterns may be used to determine semantic relatedness between events and actions in response. Event-action node pairs are then constructed.

Abstract: Knowledge graph systems are disclosed for implementing multiple approaches, including stand alone or combined approaches, for knowledge graph pruning. The approaches are based on graph sampling work such as, for example, information gain theory. The approaches are applied by a knowledge graph system to perform schema pruning, automatic graph pruning, and query correlation for improving query performance.

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 device may receive content data identifying content created by users and metadata associated with the content. The device may receive rules data identifying rules associated with utilization of the content. The device may utilize the metadata to generate digital DNA signatures for the content in near-real time. The device may store, in a repository, the rules data, the content, the digital DNA signatures, and relationships between the digital DNA signatures. The device may receive, from a client device, new content that is generated based on particular content of the content data and new metadata associated with the new content. The device may utilize the new metadata to generate a new digital DNA signature for the new content. The device may process the new digital DNA signature, the rules data, and the digital DNA signatures to determine whether the new content violates one or more rules of the rules data.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support providing secure backup and recovery of files from edge devices during ransomware attacks or other cyberattacks. Secure data, such as medical records, may be stored at one or more networked storage nodes and backup images (e.g., snapshots) may be stored at a disconnected storage node (e.g., an air-gapped storage node) that is isolated from the networked storage nodes. Application programming interface (API) calls may be managed and monitored to detect an alarm state (e.g., a ransomware attack), and based on the alarm state, storage and retrieval from the networked storage nodes may be stopped. Additionally, a recent backup image from the disconnected storage node may be retrieved for use in performing system recovery operations.

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: The present disclosure relates to a system, a method, and a product for optimizing hyper-parameters for generation and execution of a machine-learning model under constraints. The system includes a memory storing instructions and a processor in communication with the memory. When executed by the processor, the instructions cause the processor to obtain input data and an initial hyper-parameter set; for an iteration, to build a machine learning model based on the hyper-parameter set, evaluate the machine learning model based on the target data to obtain a performance metrics set, and determine whether the performance metrics set satisfies the stopping criteria set. If yes, the instructions cause the processor to perform an exploitation process to obtain an optimal hyper-parameter set, and exit the iteration; if no, perform an exploration process to obtain a next hyper-parameter set, and perform a next iteration with using the next hyper-parameter set as the hyper-parameter set.

Abstract: A causal inference stack implements a targeted maximum likelihood scheme to conduct causal analysis of the observational data. At a data-handling layer, the causal inference stack obtains one or more memory locations for a dataset and establishes analysis nodes to setup localized data handling for the memory locations. At a data classification layer, the causal inference stack characterizes the missingness of the dataset. At a pipeline layer, the causal inference stack obtains a data element dependency query from a user and sets up an end-to-end solution path to determine the presence of a causal relationship between data elements identified in the data element dependency query.

Abstract: In some implementations, a control system may obtain historical data associated with usage of a distillation column during a historical time period. The control system may configure a prediction model to monitor the distillation column for a hazardous condition. The prediction model may be trained based on training data that is associated with occurrences of the hazardous condition. The control system may monitor, using the prediction model, the distillation column to determine a probability that the distillation column experiences the hazardous condition within a threshold time period. The prediction model may be configured to determine the probability based on measurements from a set of sensors of the distillation column. The control system may perform, based on the probability satisfying a probability threshold, an action associated with the distillation column to reduce a likelihood that the distillation column experiences the hazardous condition within the threshold time period.

Abstract: Embodiments of the present disclosure provide systems and methods for controlling a manufacturing process in a manner that protects sensitive information from misuse by different entities involved in the manufacturing process. According to the present disclosure, a blueprint providing information regarding subcomponents of a product to be manufactured may be provided to a synthesizer device. The synthesizer device may engage in two-party computation with IP providers to generate a set of machine commands, which may be encrypted, and then provide a message including the set of machine commands to a manufacturer device. The manufacturer device may obtain authorization from the IP provider(s) based on the message, where the authorization may enable the manufacturer device to configure a manufacturing process in accordance with the set of machine commands to manufacture the subcomponents of the product.

Abstract: A system maintains a knowledge layout to support the analysis of active events and determination of epicenter and aftershock nodes via an event reach stack. At an input layer of the event reach stack, the system may receive active event data. At a semantic layer, the system may parse the active event data to determine event phrases. Based on the event phrases, the system may identify epicenter nodes directly affected by the active event. At an analytic model layer, the system may successively determine aftershock nodes by traversing the knowledge layout outward from the epicenter nodes. The system then directs the response to the active event to the aftershock and epicenter nodes, via action at a focus response layer of the event reach stack.

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: An Artificial Intelligence (AI)-based data matching and alignment system identifies similar data sources for a target data source from a data corpus and generates a knowledge graph that enables downstream applications seamless access to data in the data corpus. The system extracts column features at different levels for the target data source and a plurality of data sources from the data corpus. Feature matrices are built from the features of the target data source and the plurality of data sources. Candidate data sources similar to the target data source are filtered from the plurality of data sources using the feature matrices. The tree-based similarity is estimated and K Nearest Neighbor (KNN) graphs are built to identify columns from the candidate data sources that are similar to columns of the target data source to build the knowledge graph.

Abstract: A resource prediction system for executing machine learning models and method are provided. The system includes non-transitory memory storing instructions and a processor configured to execute the instructions to obtain input data including a targeted objective and the constraints, select a deployable machine learning model having an evaluation score that meets a predetermined criterion from among candidate machine learning models, virtually execute the deployable machine learning model on each of candidate hardware platforms according to the constraints, generate an assessment report of the virtual performance metrics set of the deployable machine learning model executed on each of the candidate hardware platforms, and select the suggested hardware platform meeting the predetermined criterion from among the candidate hardware platforms.

Abstract: There has been exponential growth in the capture and retention of immense quantities of information in a globally distributed manner. A closed-loop unified metadata architecture includes a universal metadata repository and implements data quality and data lineage analyses. The architecture solves significant technical challenges to provide a meaningful, consistent and normalized view of the metadata that describes the information, as well as to determine data lineage and meaningful data quality metrics.