Abstract: Certain aspects of the present disclosure provide techniques for generating a recommendation of third-party applications to a user by a recommendation engine. The recommendation engine includes two deep-learning models that use various data sources (e.g., user data and application data) to generate the recommendation. One deep-learning model generates a relevance score for each available third-party application. The relevance score is used to determine a relevant application(s). The other deep-learning model generates a connection score for each relevant application. The recommendation engine uses the relevance score and the connections to generate an engagement score for each relevant application to determine whether the user would use the third-party application if recommended to the user. Those relevant applications with an engagement score that meet pre-determined criteria are determined and displayed to the user in the application as a recommendation.

Abstract: Certain aspects of the present disclosure provide techniques for generating a recommendation of third-party applications to a user by a recommendation engine. The recommendation engine includes two deep-learning models that use various data sources (e.g., user data and application data) to generate the recommendation. One deep-learning model generates a relevance score for each available third-party application. The relevance score is used to determine a relevant application(s). The other deep-learning model generates a connection score for each relevant application. The recommendation engine uses the relevance score and the connections to generate an engagement score for each relevant application to determine whether the user would use the third-party application if recommended to the user. Those relevant applications with an engagement score that meet pre-determined criteria are determined and displayed to the user in the application as a recommendation.

Abstract: The example embodiments are directed to a device and method for determining a root cause of equipment failure. In one example, the method includes storing a plurality of root causes of previous equipment failures, receiving textual data associated with a current equipment failure, determining a root cause for the current equipment failure by determining a similarity of keywords of each root cause with respect to the received textual data of the current equipment failure and selecting at least one root cause based on the determined similarities of the plurality of root causes, and displaying the at least one determined root cause for the current equipment failure via a display device. The example embodiments provide a system and method that automatically determine a root cause of equipment failure rather than rely on a subject matter expert.

Abstract: The example embodiments are directed to a system and method for monitoring the health of analytical applications. In certain embodiments, these analytic applications may be a part of a broader IoT solution that may optionally be hosted on a cloud platform. In one example, the method includes receiving an output of an IoT analytic application deployed on a computing platform, selecting at least one performance metric based on a type of the application, calculating a performance of the application based on the selected performance metric, the received output of the application, and an expected output of the application, and in response to detecting the calculated performance of the application is below a predetermined threshold, outputting an alert to a user device. Accordingly, the health of an IoT analytic application can be monitored and an alert can be provided when the application operates below a predetermined threshold.

Abstract: The example embodiments are directed to a system and method for monitoring the health of analytical applications. In certain embodiments, these analytic applications may be a part of a broader IoT solution that may optionally be hosted on a cloud platform. In one example, the method includes receiving an output of an IoT analytic application deployed on a computing platform, selecting at least one performance metric based on a type of the application, calculating a performance of the application based on the selected performance metric, the received output of the application, and an expected output of the application, and in response to detecting the calculated performance of the application is below a predetermined threshold, outputting an alert to a user device. Accordingly, the health of an IoT analytic application can be monitored and an alert can be provided when the application operates below a predetermined threshold.

Abstract: The example embodiments are directed to a device and method for determining a root cause of equipment failure. In one example, the method includes storing a plurality of root causes of previous equipment failures, receiving textual data associated with a current equipment failure, determining a root cause for the current equipment failure by determining a similarity of keywords of each root cause with respect to the received textual data of the current equipment failure and selecting at least one root cause based on the determined similarities of the plurality of root causes, and displaying the at least one determined root cause for the current equipment failure via a display device. The example embodiments provide a system and method that automatically determine a root cause of equipment failure rather than rely on a subject matter expert.

Abstract: The example embodiments are directed to a system and method for monitoring the health of analytical applications. In certain embodiments, these analytic applications may be a part of a broader IoT solution that may optionally be hosted on a cloud platform. In one example, the method includes receiving an output of an IoT analytic application deployed on a computing platform, selecting at least one performance metric based on a type of the application, calculating a performance of the application based on the selected performance metric, the received output of the application, and an expected output of the application, and in response to detecting the calculated performance of the application is below a predetermined threshold, outputting an alert to a user device. Accordingly, the health of an IoT analytic application can be monitored and an alert can be provided when the application operates below a predetermined threshold.

Abstract: Provided are a system and method for determining a future delivery status of a purchase order. In one example, the method includes receiving delivery information about previous orders of a supplier, generating one or more models indicating whether an order of the supplier is going to be delivered on-time based on the delivery information about the previous orders, determining that a current order of the supplier is going to be delayed based on the one or more models, and outputting a user interface displaying an indication that the current order is going to be delayed. The system and method described herein can determine whether an industrial purchase order will be late at least six weeks in advance or more thus making it possible for customers to take alternative measures.