Abstract: Computer-readable media, methods, and systems are disclosed for scheduling a start time and a shutdown time of one or more online resources associated with a multi-cloud resource scheduler. A request from a first user is received to access a multi-cloud resource scheduler associated with one or more online resources. Responsive to the request from the first user, credentials of the first user are validated prior to providing access to the multi-cloud resource scheduler. Based upon validating the credentials of the first user, access to the multi-cloud resource scheduler is provided. Instructions are received from the first user to schedule a start time and a shutdown time of at least one online cloud resource connected to the multi-cloud resource scheduler. An availability of the at least one online cloud resource is established for access by a second user based on the instructions.

Abstract: Some embodiments are directed to a federated machine learning, including the inference and training. Inference may be done by applying multiple machine learnable models to a mapped record. The mapped record may be obtained by applying a mapping rule to a local record. The mapping rule may generalize or extend data features in the local record.

Abstract: Computer-readable media, methods, and systems are disclosed for storing and analyzing dynamic data within a semantic data store. The dynamic data comprises one or more types of data having a normalized data schema. A dynamic data manager interfaces with the semantic data store to instruct storage of the data. The data may be received through an event service from either of an external data source or an internal data source.

Abstract: Visualization of time series data includes sending to the data source a request for visualizing sensor data within a canvas having a width of w pixels and covering a visualization time range, each pixel of the canvas being representative of a time duration Iopt, receiving consecutive sets of tuples that each covers a time interval having the time duration It, performing a M4 aggregation comprising generating, from the received tuples, a set of consecutive w groups, each group of the w groups comprising tuples covering a time interval having the time duration Iopt, and determining for each group of the w groups a set of aggregates, and displaying the w sets of aggregates on the canvas of the browser window as a chart, wherein each one of the sets of aggregates is displayed in one of pixel columns of the canvas.

Abstract: In some implementations, there is provided a method, which includes transforming, by the recommendation system, a first data set into the principal component analysis domain; rotating, by the recommendation system, the transformed first data first data set into a common axis system; comparing, by the recommendation system, the rotated, transformed first data set to at least one of a plurality of reference data sets having been rotated into the common axis system; and identifying, by the recommendation system, at least one reference data set, the identifying based on the comparing. Related systems, methods, and articles of manufacture are also disclosed.

Abstract: Visualization of time series data includes sending to the data source a request for visualizing sensor data within a canvas having a width of w pixels and covering a visualization time range, each pixel of the canvas being representative of a time duration Iopt, receiving consecutive sets of tuples that each covers a time interval having the time duration It, performing a M4 aggregation comprising generating, from the received tuples, a set of consecutive w groups, each group of the w groups comprising tuples covering a time interval having the time duration Iopt, and determining for each group of the w groups a set of aggregates, and displaying the w sets of aggregates on the canvas of the browser window as a chart, wherein each one of the sets of aggregates is displayed in one of pixel columns of the canvas.

Abstract: Techniques for implementing a digital assistant that provides proactive notifications to users, summarizes data and relevant situations, forecasts/predicts future outcomes, simulates outcomes under different assumptions, generates recommendations to improve observed or assumed situations, and provides explanations for calculated outcomes are disclosed. In some example embodiments, a computer system is configured to detect a data change in one or more data sources, the data change corresponding to a monitored data object, generate a predicted future value for the monitored data object based on the detected data change, identify a deviation between the predicted future value and a planned future value for the monitored data object, determine that the identified deviation is relevant for a specific user at a specific time and in a specific context, and cause a notification corresponding to the deviation to be displayed on a computing device based on the determination that the deviation is relevant.

Abstract: Some embodiments include reception of a time-series of a respective data value generated by each of a plurality of sensors, calculation of a regression associated with a first sensor of the plurality of sensors based on the received plurality of time-series, the regression being a function of the respective data values of the others of the plurality of data sources, reception of respective data values associated with a time from and generated by each the plurality of respective sensors, determination of a predicted value associated with the time for the first sensor based on the regression associated with the first sensor and on the respective data values associated with the time, comparison of the predicted value with the received value associated with the time and generated by the first sensor, and determination of a value indicating a likelihood of an anomaly based on the comparison.

Abstract: In some implementations, there is provided a method, which includes receiving, at a recommendation system, a failure notice representing a failure detected by a sensor monitoring an object; comparing, by the recommendation system, the failure notice to a plurality of reference failure notices to identify at least one matching failure notice, the comparing based on a domain-by-domain scoring between the failure notice and the plurality of reference failure notices; and generating, by the recommendation system, a message including a suggested solution including at least one task to remedy the failure, the suggested solution obtained from the at least one matching failure notice. Related systems, methods, and articles of manufacture are also disclosed.

Abstract: In some implementations, there is provided a method, which includes receiving, at a recommendation system, a failure notice representing a failure detected by a sensor monitoring an object; comparing, by the recommendation system, the failure notice to a plurality of reference failure notices to identify at least one matching failure notice, the comparing based on a domain-by-domain scoring between the failure notice and the plurality of reference failure notices; and generating, by the recommendation system, a message including a suggested solution including at least one task to remedy the failure, the suggested solution obtained from the at least one matching failure notice. Related systems, methods, and articles of manufacture are also disclosed.

Abstract: In some implementations, there is provided a method, which includes transforming, by the recommendation system, a first data set into the principal component analysis domain; rotating, by the recommendation system, the transformed first data first data set into a common axis system; comparing, by the recommendation system, the rotated, transformed first data set to at least one of a plurality of reference data sets having been rotated into the common axis system; and identifying, by the recommendation system, at least one reference data set, the identifying based on the comparing. Related systems, methods, and articles of manufacture are also disclosed.

Abstract: Some embodiments include reception of a time-series of a respective data value generated by each of a plurality of sensors, calculation of a regression associated with a first sensor of the plurality of sensors based on the received plurality of time-series, the regression being a function of the respective data values of the others of the plurality of data sources, reception of respective data values associated with a time from and generated by each the plurality of respective sensors, determination of a predicted value associated with the time for the first sensor based on the regression associated with the first sensor and on the respective data values associated with the time, comparison of the predicted value with the received value associated with the time and generated by the first sensor, and determination of a value indicating a likelihood of an anomaly based on the comparison.

Abstract: Some embodiments include reception of a time-series of a respective data value generated by each of a plurality of sensors, calculation of a regression associated with a first sensor of the plurality of sensors based on the received plurality of time-series, the regression being a function of the respective data values of the others of the plurality of data sources, reception of respective data values associated with a time from and generated by each the plurality of respective sensors, determination of a predicted value associated with the time for the first sensor based on the regression associated with the first sensor and on the respective data values associated with the time, comparison of the predicted value with the received value associated with the time and generated by the first sensor, and determination of a value indicating a likelihood of an anomaly based on the comparison.

Abstract: Some embodiments include reception of a time-series of a respective data value generated by each of a plurality of sensors, calculation of a regression associated with a first sensor of the plurality of sensors based on the received plurality of time-series, the regression being a function of the respective data values of the others of the plurality of data sources, reception of respective data values associated with a time from and generated by each the plurality of respective sensors, determination of a predicted value associated with the time for the first sensor based on the regression associated with the first sensor and on the respective data values associated with the time, comparison of the predicted value with the received value associated with the time and generated by the first sensor, and determination of a value indicating a likelihood of an anomaly based on the comparison.