A software placement device of a software placement system includes an information acquiring unit configured to acquire a finally-found location and a finally-found time, a deployment destination determining unit configured to calculate a data presence probability indicating a probability of a search target being detected at a certain time using the finally-found location and the finally-found time, to calculate a total cost using the data presence probability calculated and a computational cost and a network cost of each of base computers, and to select a base computer and a time interval for which the calculated total cost is a minimum, and a software distributing unit configured to distribute software to the selected base computer and transmit an analysis instruction for data of the selected time interval to the selected base computer.

A valve state grasping system, a display device and a rotary valve, a valve state grasping program, a recording medium, and a valve state grasping method that enable efficient valve system monitoring and accumulation of information. The system includes a valve V, a sensor unit, a server including a database, a terminal device including a display unit, and a system control unit. The database includes a position information unit, a history information unit, and an inference information unit. The position information unit includes unique information and pipe attachment information, and the history information unit includes at least measurement information and diagnosis information. The system control unit accumulates information of the position information unit and information of the history information unit in association with each other and outputs predetermined inference information from the inference information unit based on information of the position information unit and information of the history information unit.

Techniques for geometric aging data reduction for machine learning applications are disclosed. In some embodiments, an artificial-intelligence powered system receives a first time-series dataset that tracks at least one metric value over time. The system then generates a second time-series dataset that includes a reduced version of a first portion of the time-series dataset and a non-reduced version of a second portion of the time-series dataset. The second portion of the time-series dataset may include metric values that are more recent than the first portion of the time-series dataset. The system further trains a machine learning model using the second time-series dataset that includes the reduced version of the first portion of the time-series dataset and the non-reduced version of the second portion of the time-series dataset. The trained model may be applied to reduced and/or non-reduced data to detect multivariate anomalies and/or provide other analytic insights.

The disclosure relates to system and method for facilitating designing of an Internet of Things (IoT) infrastructure for deploying an IoT application. The method includes determining a Manhattan distance between each of a plurality of existing requirements and a new requirement, identifying one or more of the plurality of existing requirements corresponding to a minimum Manhattan distance, determining a relevancy score for each of the one or more identified existing requirements based on a similarity between the each of the one or more identified existing requirements and the new requirement, and providing one or more IoT components and one or more IoT designs corresponding to a similar existing requirement for facilitating designing of the IoT infrastructure. The similar existing requirement comprises one of the one or more identified existing requirement with a maximum relevancy score.

The disclosure relates to system and method for facilitating designing of an Internet of Things (IoT) infrastructure for deploying an IoT application. The method includes determining a Manhattan distance between each of a plurality of existing requirements and a new requirement, identifying one or more of the plurality of existing requirements corresponding to a minimum Manhattan distance, determining a relevancy score for each of the one or more identified existing requirements based on a similarity between the each of the one or more identified existing requirements and the new requirement, and providing one or more IoT components and one or more IoT designs corresponding to a similar existing requirement for facilitating designing of the IoT infrastructure. The similar existing requirement comprises one of the one or more identified existing requirement with a maximum relevancy score.

First sensor data can be received from a first set of IoT devices. Sensor data collection rates can be determined for a first artificial intelligence model by analyzing the first sensor data using a second artificial intelligence model. Based on the sensor data collection rates, sensor control commands can be communicated to a second set of Internet of Things devices. The sensor control commands can specify, to the second set of Internet of Things devices, sensor data communication rates that respective ones of the second set of Internet of Things devices are to implement for communicating, to the first artificial intelligence model, second sensor data generated by sensors of the respective ones of the second set of Internet of Things devices.

First sensor data can be received from a first set of IoT devices. Sensor data collection rates can be determined for a first artificial intelligence model by analyzing the first sensor data using a second artificial intelligence model. Based on the sensor data collection rates, sensor control commands can be communicated to a second set of Internet of Things devices. The sensor control commands can specify, to the second set of Internet of Things devices, sensor data communication rates that respective ones of the second set of Internet of Things devices are to implement for communicating, to the first artificial intelligence model, second sensor data generated by sensors of the respective ones of the second set of Internet of Things devices.

In an approach for monitoring data usage in an IoT network, a processor identifies a set of IoT devices. A processor collects a set of baseline readings for the set of IoT devices. A processor collects a set of IoT data from the set of IoT devices as a user answers a survey question. A processor compares the set of baseline readings to the set of IoT data. A processor derives an emotional state of the user while answering the survey question. A processor builds a CART model using the set of baseline readings, the set of IoT data, and a set of survey data. A processor applies the emotional state as a weight to the CART model. A processor outputs the weighted CART model.

In an approach for monitoring data usage in an IoT network, a processor identifies a set of IoT devices. A processor collects a set of baseline readings for the set of IoT devices. A processor collects a set of IoT data from the set of IoT devices as a user answers a survey question. A processor compares the set of baseline readings to the set of IoT data. A processor derives an emotional state of the user while answering the survey question. A processor builds a CART model using the set of baseline readings, the set of IoT data, and a set of survey data. A processor applies the emotional state as a weight to the CART model. A processor outputs the weighted CART model.

The present invention generally relates to systems and methods for detecting and/or isolating any causes of defective and/or partially defective IoT device or individual sensor device(s). In embodiments the present invention generally relates to fixing, replacing, and/or troubleshooting IoT devices and/or individual sensor device(s) that are defective and/or partially defective.