Abstract: The system can include a set of joints, a controller, and a model engine; and can optionally include a support structure and an end effector. Joints can include: a motor, a transmission mechanism, an input sensor, and an output sensor. The system can enable articulation of the plurality of joints.

Abstract: Methods, systems, and computer-readable storage media for providing an insight provider including a logic component and a configuration component, the logic component including a domain-specific model, the configuration component including one or more parameter values for processing data using the domain-specific model, receiving a set of assets including data indicative of one or more assets, retrieving asset data associated with at least one asset of the first set of assets, the asset data including OT data and IT data, the OT data being provided from one or more networked devices, the IT data being provided from one or more enterprise systems, and processing the OT data and the IT data using the domain-specific model of the logic component to provide a result set, the result set including one or more of a second set of assets and enriched data.

Abstract: Techniques and solutions are provided for processing data in conjunction with one or more hyperscale computing systems. An interface is provided for translating calls from an application into a format used by a hyperscale computing system. The calls can be to read data from, or write data to, a hyperscale computing system. In particular examples, data to be read or written is data from a plurality of IOT devices, where each IOT device has one or more hardware sensors. An interface can also be used to configure how the hyperscale computing system processes data, such as determining how IOT data is stored or how aggregates are generated from IOT data.

Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving an identification of a machine learning model, executing a machine learning pipeline comprising a plurality of services which train the machine learning model via at least one of an unsupervised learning process and a supervised learning process, the machine learning pipeline being controlled by an orchestration module that triggers ordered execution of the services, and storing the trained machine learning model output from the machine learning pipeline in a database associated with the machine learning pipeline.

Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving a request to create a machine learning model for failure mode detection associated with an asset, retrieving historical notification data of the asset, generating an unsupervised machine learning model via unsupervised learning on the historical notification data, wherein the unsupervised learning comprises identifying failure topics from text included in the historical notification data and mapping the identified failure topics to a plurality of predefined failure modes for the asset, and storing the generated unsupervised machine learning model via a storage device.

Abstract: Techniques and solutions are provided for integrating master data from multiple applications. Master data from multiple applications can be integrated for use in processing data associated with internet of things (IOT) devices, such as by joining master data with timeseries data (including aggregated values). Integrating master data from multiple applications can include converting master data from a schema used by an application into an analytics schema. New or updated master data can be indicated in a message sent by an application. In processing the message, additional master data, or data used to determine how master data should be processed, can be retrieved.

Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving an identification of a machine learning model, executing a machine learning pipeline comprising a plurality of services which train the machine learning model via at least one of an unsupervised learning process and a supervised learning process, the machine learning pipeline being controlled by an orchestration module that triggers ordered execution of the services, and storing the trained machine learning model output from the machine learning pipeline in a database associated with the machine learning pipeline.

Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving a request to create a machine learning model for failure mode detection associated with an asset, retrieving historical notification data of the asset, generating an unsupervised machine learning model via unsupervised learning on the historical notification data, wherein the unsupervised learning comprises identifying failure topics from text included in the historical notification data and mapping the identified failure topics to a plurality of predefined failure modes for the asset, and storing the generated unsupervised machine learning model via a storage device.

Abstract: A global filter allows data filtering using attributes across multiple Analysis Tools (ATs), by broadcasting semantic filter context objects. Upon selecting object attribute values, the filter context object is created with attribute names and values. A processing engine resolves the filter context object to a data object, and then subsequently to target data. A lateral filter finds related entities in a relational database, without having to maintain and/or duplicate all of the data into a graph database. The processing engine resolves lateral filters using an entity graph path calculation conducted in conjunction with the generation of a bootstrapped graph structure. That graph structure is constructed (bootstrapped) utilizing available database schematic information—e.g., pre-calculated (key) relations and metadata read from the relational database. From that information, relationships in the bootstrapped graph structure are determined.

Abstract: A data container for a content package comprising one or more semantics for populating the content package with selected types of information associated with a product or service is received by a computing device of a digital services framework. An organizational structure between and within networked tenants of the digital services framework is analyzed to identify one or more recipients for the content package. A data topology associated with the product or service is analyzed to generate announcements indicative of individualized content packages for the identified recipients for the content package. The announcements are sent to the identified recipients. Requests are received for subscriptions to the content package. Based on the analysis of the organizational structure and data topology and user-defined rules and semantics, instances of the container are selectively populating for tenants who have subscribed to the content package.

Abstract: A global filter allows data filtering using attributes across multiple Analysis Tools (ATs), by broadcasting semantic filter context objects. Upon selecting object attribute values, the filter context object is created with attribute names and values. A processing engine resolves the filter context object to a data object, and then subsequently to target data. A lateral filter finds related entities in a relational database, without having to maintain and/or duplicate all of the data into a graph database. The processing engine resolves lateral filters using an entity graph path calculation conducted in conjunction with the generation of a bootstrapped graph structure. That graph structure is constructed (bootstrapped) utilizing available database schematic information—e.g., pre-calculated (key) relations and metadata read from the relational database. From that information, relationships in the bootstrapped graph structure are determined.

Abstract: Systems and methods are provided for receiving, from a computing device, a selection of a template for a custom microservice and configuration parameters for the custom microservice, generating the template for the custom microservice using the configuration parameters, the template for the custom microservice comprising defined interfaces for accessing core microservices, defined integration points for integration with a system providing the core microservices, and stubs for custom components for the custom microservice, and providing the template for the custom microservice to the computing device, wherein custom components for the custom microservice are added to the template via the computing device using the stubs for the custom components. The systems and methods further provide for registering the custom microservice to be exposed to and accessed by a tenant with authorization to access the custom microservice along with the core microservices.

Abstract: A selection indicative of one or more types of information associated with a product or service is received by a computing device of a digital services framework. A data container is generated for a content package comprising one or more semantics for populating the content package with the selected types of information. Based on the product or service, one or more recipients for the content package are identified. An announcement indicative of the content package is sent to the identified recipients. Requests for subscriptions to the content package identified recipients are received. Instances of the container are populated for identified recipients who have subscribed to the content package. The populating comprises populating the content package with the selected types of information based on the one or more semantics. The populated instances of the content package are sent to the subscribed recipients.

Abstract: Provided is a system and method for predicting leading indicators for predicting occurrence of an event at a target asset. Rather than rely on traditional manufacturer-defined leading indicators for an asset, the examples herein predict leading indicators for a target asset based on actual operating conditions at the target asset. Accordingly, unanticipated operating conditions can be considered. In one example, the method may include receiving operating data of a target resource, the operating data being associated with previous occurrences of an event at the target resource, predicting one or more leading indicators of the event at the target resource based on the received operating data, each leading indicator comprising a variable and a threshold value for the variable, and outputting information about the one or more predicted leading indicators of the target resource for display via a user interface.

Abstract: Techniques and solutions are provided for integrating master data from multiple applications. Master data from multiple applications can be integrated for use in processing data associated with internet of things (IOT) devices, such as by joining master data with timeseries data (including aggregated values). Integrating master data from multiple applications can include converting master data from a schema used by an application into an analytics schema. New or updated master data can be indicated in a message sent by an application. In processing the message, additional master data, or data used to determine how master data should be processed, can be retrieved.

Abstract: Techniques and solutions are provided for processing data in conjunction with one or more hyperscale computing systems. An interface is provided for translating calls from an application into a format used by a hyperscale computing system. The calls can be to read data from, or write data to, a hyperscale computing system. In particular examples, data to be read or written is data from a plurality of IOT devices, where each IOT device has one or more hardware sensors. An interface can also be used to configure how the hyperscale computing system processes data, such as determining how IOT data is stored or how aggregates are generated from IOT data.

Abstract: Techniques for processing sensor data are provided. Sensor data, such as individual messages or data points from devices having one or more hardware sensors, can be annotated with one or more metadata elements to facilitate sensor data processing. An annotation rule for sensor data can be determined and sensor data annotated according to the annotation rule. Sensor data can be written to a relational database table, where the table has a schema that provides columns for storing data for particular indicators of an indicator group having a plurality of indicators.

Abstract: A global filter allows data filtering using attributes across multiple Analysis Tools (ATs), by broadcasting semantic filter context objects. Upon selecting object attribute values, the filter context object is created with attribute names and values. A processing engine resolves the filter context object to a data object, and then subsequently to target data. A lateral filter finds related entities in a relational database, without having to maintain and/or duplicate all of the data into a graph database. The processing engine resolves lateral filters using an entity graph path calculation conducted in conjunction with the generation of a bootstrapped graph structure. That graph structure is constructed (bootstrapped) utilizing available database schematic information—e.g., pre-calculated (key) relations and metadata read from the relational database. From that information, relationships in the bootstrapped graph structure are determined.

Abstract: A global filter allows data filtering using attributes across multiple Analysis Tools (ATs), by broadcasting semantic filter context objects. Upon selecting object attribute values, the filter context object is created with attribute names and values. A processing engine resolves the filter context object to a data object, and then subsequently to target data. A lateral filter finds related entities in a relational database, without having to maintain and/or duplicate all of the data into a graph database. The processing engine resolves lateral filters using an entity graph path calculation conducted in conjunction with the generation of a bootstrapped graph structure. That graph structure is constructed (bootstrapped) utilizing available database schematic information—e.g., pre-calculated (key) relations and metadata read from the relational database. From that information, relationships in the bootstrapped graph structure are determined.

Abstract: Systems and methods are provided for receiving a request to access a service catalog from a computing device associated with a tenant with authorization to access a custom microservice and the core microservices of the service catalog, and determining that the service catalog associated with the tenant comprises the custom microservice. The systems and methods further provide for determining routing information from the service catalog to make a request to the custom microservice, routing the request to the custom microservice based on the determined routing information, wherein the request is routed to a tenant computing system associated with the custom microservice, receiving a payload from the tenant computing system associated with the custom microservice, and generating a user interface comprising representations corresponding to the custom microservice and each of the core microservices, the representations corresponding to the custom microservice based on the received payload.