Abstract: Various embodiments are provided for configuring optimization problems from one or more sources in a computing environment by a processor. A knowledge graph may be generated from a knowledge domain and one or more data sources. One or more graph pattens may be applied to match one or more entities in the knowledge graph with one or more atomic optimization templates. An optimization problem configured from the one or more atomic optimization templates and a plurality of data may be executed.

Abstract: Data from a knowledge graph associated with an enterprise may be obtained. The knowledge graph may include a plurality of node descriptors that indicate locations, assets, and sensor data feeds of the enterprise, and a plurality of relationship descriptors that indicate relationships amongst the locations, the assets, and the sensor data feeds of the enterprise. An anomaly detection or prediction model associated with a selected one of the plurality of node descriptors may be auto-created based on the data from the knowledge graph. In the auto-creation, one of a plurality of model types for the anomaly detection or prediction model may be selected based on an identified node type of the selected node descriptor.

Abstract: A system, method and a computer program product may be provided for automatically creating and parameterizing a semantically-enriched diagnosis model for an entity. The system receives a list of data points, from sensors or a database, to be used to create a diagnosis model. The system automatically creates the diagnosis model based on the received list of data points and data stored in a database and parameterizes the diagnosis model. The parameterized diagnosis model reflects rules that determine one or more potential causes of one or more abnormalities of one or more physical conditions in the entity.

Abstract: Various embodiments are provided for identifying relevant graph patterns in a knowledge graph in a computing environment by a processor. Data elements may be identified from a knowledge graph and associated datasets that is related to one or more nodes of the knowledge graph and external to the knowledge graph. One or more subgraphs may be selected and created based on missing data elements and inferred knowledge data. The knowledge graph may be modified with one or more subgraphs.

Abstract: Embodiments are provided for providing increased performance of various industrial systems and processes in a computing system by a processor. Each of a plurality of dependencies of a plurality of entities in a knowledge graph are modeled as a graph neural network (“GNN”). A reference graph model is generated based on the modeling. One or more anomalies are monitored and detected for a plurality of process based on the reference graph model.

Abstract: An approach for monitoring, detecting and localizing anomalies of HVAC system by using the combination of thermodynamics models, the energy balance of a zone in steady state, and data analytics is disclosed. The approach determines, via machine learning, the ideal thermodynamic model for an area serviced by an HVAC system. The approach retrieves reading from various sensors and insert the current sensor reading into the ideal model. In the presence of anomalies, the parameters of the model will deviate from their nominal values and an appropriate action can be taken based on the severity of the detected and localized anomalies.

Abstract: A method for additive manufacturing includes identifying a discrepancy between a three-dimensional model and an object model. The three-dimensional model is a model of a three-dimensional object that is being constructed by an additive manufacturing process, and the three-dimensional object is being constructed based on the object model. The method further includes determining a reconfiguration recommendation based on the identified discrepancy. The method further includes reconfiguring the additive manufacturing process based on the reconfiguration recommendation.

Abstract: Data from a knowledge graph associated with an enterprise may be obtained. The knowledge graph may include a plurality of node descriptors that indicate locations, assets, and sensor data feeds of the enterprise, and a plurality of relationship descriptors that indicate relationships amongst the locations, the assets, and the sensor data feeds of the enterprise. An anomaly detection or prediction model associated with a selected one of the plurality of node descriptors may be auto-created based on the data from the knowledge graph. In the auto-creation, one of a plurality of model types for the anomaly detection or prediction model may be selected based on an identified node type of the selected node descriptor.

Abstract: Provided is a method for monitoring additive manufacturing. The method comprises acquiring an image of a three-dimensional object that is being constructed using an object model. The method further includes isolating a layer of the three-dimensional object from the image. The method further includes generating a three-dimensional model from the layer. The method further includes comparing the three-dimensional model with the object model. The method further includes identifying a discrepancy between the three-dimensional model and the object model. The method further includes providing a notification of the identified discrepancy.

Abstract: An approach for monitoring, detecting and localizing anomalies of HVAC system by using the combination of thermodynamics models, the energy balance of a zone in steady state, and data analytics is disclosed. The approach determines, via machine learning, the ideal thermodynamic model for an area serviced by an HVAC system. The approach retrieves reading from various sensors and insert the current sensor reading into the ideal model. In the presence of anomalies, the parameters of the model will deviate from their nominal values and an appropriate action can be taken based on the severity of the detected and localized anomalies.

Abstract: A method for generating an operational rule associated with a building management system includes identifying, with a processing device, a first pattern associated with a series of operational observations corresponding to a property of the building management system, correlating a first contextual attribute with the first pattern, and deriving the operational rule at least in part based on the first pattern and the first contextual attribute.

Abstract: Methods, computer program products, and/or systems are provided that perform the following operations: obtaining image data, wherein the image data includes a plurality of images; generating visualizations for one or more of the plurality of images included in the image data; obtaining a selection of one of the plurality of images for which a visualization was generated; generating three-dimensional (3D) model data based on the selected one of the plurality of images; and providing the 3D model data of the selected one of the plurality of images for generation of a 3D printable object.

Abstract: Energy usage can be monitored within at least one building having a plurality of energy consuming components. A database can be generated that contains values for a set of data points corresponding to data received from the plurality of energy consuming components. A change in a configuration can be detected for the plurality of energy consuming components based upon a change in values received from plurality of energy consuming components relative to the database. Based upon the change, an additional data point can be added to the set of data points in the database. Based upon the values for the set of data points, a probability can be determined that a rule for the additional data point is valid. A message can then be generated that includes the determined probability.

Abstract: Various embodiments are provided for managing and interacting with a conversational agent in an internet of things (IoT) environment by a processor. One or more graph patterns may be extracted from a knowledge graph generated from a sensor list containing one or more IoT sensors. One or more conversation patterns and conversation dialogs may be learned from the sensor list and graph patterns. The conversational agent may be automatically configured with the one or more graph patterns, the one or more conversational patterns and conversation dialogs, sensor data and analytics from the one or more IoT sensors according to one or more queries to engage in a conversation dialog with a user.

Abstract: Embodiments for localizing abnormal energy consumption at a facility in a cloud computing environment by a processor. One or more residuals for both one or more predictors and a prediction may be generated according to one or more energy consumption measurements, weather data, and one or more characteristics of the one or more facilities, or a combination thereof. An energy consumption anomaly may be localized according to those of the one or more residuals associated with one or more predictors having an actual energy measurement deviating from a predicted actual energy measurement.

Abstract: Embodiments for detection of energy consumption anomalies in one or more energy consumption systems in a cloud computing environment by a processor. Energy consumption may be predicted for one or more facilities according to one or more energy consumption measurements, weather data, and one or more characteristics of the one or more facilities, or a combination thereof. An onset of an energy consumption anomaly may be detected according to the prediction.

Abstract: A first instruction is sent to an integrated smart meter to initialize the integrated smart meter. A second instruction is sent to the integrated smart meter to establish a connection with an energy consuming device. The established connection between the integrated smart meter and the energy consuming device is determined to be acceptable. A third instruction is sent to the integrated smart meter to monitor the energy consuming device. Energy consumption data of the energy consuming device is received from the integrated smart meter.

Abstract: Various embodiments are provided for managing and interacting with a conversational agent in an internet of things (IoT) environment by a processor. One or more graph patterns may be extracted from a knowledge graph generated from a sensor list containing one or more IoT sensors. One or more conversation patterns and conversation dialogs may be learned from the sensor list and graph patterns. The conversational agent may be automatically configured with the one or more graph patterns, the one or more conversational patterns and conversation dialogs, sensor data and analytics from the one or more IoT sensors according to one or more queries to engage in a conversation dialog with a user.

Abstract: Embodiments for using virtual sensor models in an internet of things (IoT) environment by a processor. One or more virtual sensor models are automatically identified according to a semantic graph, having a knowledge domain that links and describes a relationship between observed variables associated with one or more sensors with unobserved variables associated with the IoT environment. The one or more virtual sensor models may be selected for deployment in the IoT environment according to one or more combinations of virtual sensor inputs.

Abstract: A system, method and a computer program product may be provided for automatically creating and parameterizing a semantically-enriched diagnosis model for an entity. The system receives a list of data points, from sensors or a database, to be used to create a diagnosis model. The system automatically creates the diagnosis model based on the received list of data points and data stored in a database and parameterizes the diagnosis model. The parameterized diagnosis model reflects rules that determine one or more potential causes of one or more abnormalities of one or more physical conditions in the entity.