Abstract: Techniques for placing content in virtual universes at locations contextually compatible with the content are disclosed. A system trains a machine learning model to identify virtual environments compatible with content based on attributes representing contexts of the environments. Using the machine learning model, the system determines a contextual environment for a target content item. The system selects the particular contextual environment for placement of the target content item based on the compatibility score.

Abstract: Techniques for determining perceptibility of content displayed within virtual universes are disclosed. Operations include calculating a cube map of a scene in the virtual universe at a location with a view of a target object. Using the cube map, first and second visibility scores are computed for the target object. A perceptibility score for the target object is determined by combining the first and second visibility scores. Computing the visibility scores includes generating an image representing the scene in a face of the cube map including obstructions and visual effects. The operations further include generating another image representing the scene in the face of the cube map without including the obstructions and visual effects. Additionally, the operations include determining the visibility score by comparing the two images.

Abstract: Techniques for performing analytics using automatically generated labels for time series data and numerical lists are disclosed. In some embodiments, a system loads a set of one or more time series datasets. A respective time series dataset may include a set of data points based on varying values of a metric of one or more computing resources over a window of time. The system assigns labels to a subset of the data points in the time series datasets. The label assigned to a given data point may be descriptive of a pattern reflected by the data point relative to other data points in the time series. The system further identifies a pattern of automatically assigned labels that is indicative of an event affecting the one or more computing resources. Responsive to identifying the pattern of labels, the system may trigger a responsive action.

Abstract: Techniques for performing analytics using automatically generated labels for time series data and numerical lists are disclosed. In some embodiments, a system loads a set of one or more time series datasets. A respective time series dataset may include a set of data points based on varying values of a metric of one or more computing resources over a window of time. The system assigns labels to a subset of the data points in the time series datasets. The label assigned to a given data point may be descriptive of a pattern reflected by the data point relative to other data points in the time series. The system further identifies a pattern of automatically assigned labels that is indicative of an event affecting the one or more computing resources. Responsive to identifying the pattern of labels, the system may trigger a responsive action.

Abstract: Techniques for generating plug-in application recipe (PIAR) extensions are disclosed. A PIAR management application discovers a particular data type within one or more data values for a particular field of a plug-in application, where the particular data type is (a) different from a data type of the particular field as reported by the plug-in application and (b) narrower than the data type of the particular field while complying with the data type of the particular field. The PIAR management application identifies one or more mappings between (a) the particular data type and (b) one or more data types for fields accepted by actions of plug-in applications. The PIAR management application presents a user interface including one or more candidate PIAR extensions based on the mapping(s). Based on a user selection of a candidate PAIR extension, the PIAR management application executes a PIAR that includes the selected PIAR extension.

Abstract: Techniques for generating plug-in application recipe (PIAR) extensions are disclosed. A PIAR management application discovers a particular data type within one or more data values for a particular field of a plug-in application, where the particular data type is (a) different from a data type of the particular field as reported by the plug-in application and (b) narrower than the data type of the particular field while complying with the data type of the particular field. The PIAR management application identifies one or more mappings between (a) the particular data type and (b) one or more data types for fields accepted by actions of plug-in applications. The PIAR management application presents a user interface including one or more candidate PIAR extensions based on the mapping(s). Based on a user selection of a candidate PAIR extension, the PIAR management application executes a PIAR that includes the selected PIAR extension.

Abstract: Techniques are described for triggering conditional automated social posts. According to an embodiment, a set of one or more conditions is received through a user interface by a system executing on one or more computing devices. The system monitors one or more social media channels for target content that has been posted on at least one social media channel of the one or more social media channels. In response to detecting, by the system executing on one or more computing devices, that the target content has been posted on at least one social media channel of the one or more social media channels, the system determines whether the set of one or more conditions are satisfied. In response to determining that the set of one or more conditions are satisfied, the system triggers an action responsive to the target content.

Abstract: Techniques for generating plug-in application recipe (PIAR) extensions are disclosed. A PIAR management application discovers a particular data type within one or more data values for a particular field of a plug-in application, where the particular data type is (a) different from a data type of the particular field as reported by the plug-in application and (b) narrower than the data type of the particular field while complying with the data type of the particular field. The PIAR management application identifies one or more mappings between (a) the particular data type and (b) one or more data types for fields accepted by actions of plug-in applications. The PIAR management application presents a user interface including one or more candidate PIAR extensions based on the mapping(s). Based on a user selection of a candidate PAIR extension, the PIAR management application executes a PIAR that includes the selected PIAR extension.

Abstract: Techniques for generating plug-in application recipe (PIAR) extensions are disclosed. A PIAR management application discovers a particular data type within one or more data values for a particular field of a plug-in application, where the particular data type is (a) different from a data type of the particular field as reported by the plug-in application and (b) narrower than the data type of the particular field while complying with the data type of the particular field. The PIAR management application identifies one or more mappings between (a) the particular data type and (b) one or more data types for fields accepted by actions of plug-in applications. The PIAR management application presents a user interface including one or more candidate PIAR extensions based on the mapping(s). Based on a user selection of a candidate PAIR extension, the PIAR management application executes a PIAR that includes the selected PIAR extension.

Abstract: Techniques for semantic analysis-based generation of plug-in application recipes (PIAR's) are disclosed. Responsive to receiving a data item that specifies (a) a desired genus of actions and/or (b) a desired genus of triggers, a PIAR management application performs semantic analysis on the data item to identify one or more candidate PIAR's. The candidate PIAR(s) is/are identified based at least in part on mapping of actions and/or triggers to the desired genus of actions and/or the desired genus of triggers. The mapping is based at least in part on metadata, associated with profiles for plug-in applications, corresponding to actions and/or triggers. The PIAR management application stores, for each plug-in application, a corresponding profile to define the plug-in application for use in one or more future PIAR's. Based on user input approving a particular PIAR in the one or more candidate PIAR's, the PIAR management application executes the particular PIAR.

Abstract: Techniques for user-assisted plug-in application recipe (PIAR) execution are disclosed. During execution of a PIAR, a PIAR management application applies one or more data values for a plug-in application field to a machine learning model, to obtain: (a) a candidate mapping between one or more sub-values discovered within the data value(s) and another field accepted by an action of another plug-in application, the data value(s) being of a data type different from a reported data type of the other field, and (b) a confidence metric associated with the candidate mapping, based at least in part on whether the sub-value(s) fit(s) one or more stored formats mapped to the other data type. Based on a determination that the confidence metric does not satisfy a threshold confidence criterion, the PIAR management application obtains user input affirming or rejecting the candidate mapping, and applies the user input to execution of the PIAR.

Abstract: Techniques for generating plug-in application recipe (PIAR) extensions are disclosed. A PIAR management application discovers a particular data type within one or more data values for a particular field of a plug-in application, where the particular data type is (a) different from a data type of the particular field as reported by the plug-in application and (b) narrower than the data type of the particular field while complying with the data type of the particular field. The PIAR management application identifies one or more mappings between (a) the particular data type and (b) one or more data types for fields accepted by actions of plug-in applications. The PIAR management application presents a user interface including one or more candidate PIAR extensions based on the mapping(s). Based on a user selection of a candidate PAIR extension, the PIAR management application executes a PIAR that includes the selected PIAR extension.

Abstract: Systems and methods are described for efficiently detecting an optimal number of behaviors to model software system performance data and the aspects of the software systems that best separate the behaviors. The behaviors may be ranked according to how well fitting functions partition the performance data.

Abstract: Techniques are described for triggering conditional automated social posts. According to an embodiment, a set of one or more conditions is received through a user interface by a system executing on one or more computing devices. The system monitors one or more social media channels for target content that has been posted on at least one social media channel of the one or more social media channels. In response to detecting, by the system executing on one or more computing devices, that the target content has been posted on at least one social media channel of the one or more social media channels, the system determines whether the set of one or more conditions are satisfied. In response to determining that the set of one or more conditions are satisfied, the system triggers an action responsive to the target content.

Abstract: Techniques are described for triggering conditional automated social posts. According to an embodiment, a set of one or more conditions is received through a user interface by a system executing on one or more computing devices. The system monitors one or more social media channels for target content that has been posted on at least one social media channel of the one or more social media channels. In response to detecting, by the system executing on one or more computing devices, that the target content has been posted on at least one social media channel of the one or more social media channels, the system determines whether the set of one or more conditions are satisfied. In response to determining that the set of one or more conditions are satisfied, the system triggers an action responsive to the target content.

Abstract: Techniques for performing analytics using automatically generated labels for time series data and numerical lists are disclosed. In some embodiments, a system loads a set of one or more time series datasets. A respective time series dataset may include a set of data points based on varying values of a metric of one or more computing resources over a window of time. The system assigns labels to a subset of the data points in the time series datasets. The label assigned to a given data point may be descriptive of a pattern reflected by the data point relative to other data points in the time series. The system further identifies a pattern of automatically assigned labels that is indicative of an event affecting the one or more computing resources. Responsive to identifying the pattern of labels, the system may trigger a responsive action.

Abstract: Techniques for user-assisted plug-in application recipe (PIAR) execution are disclosed. During execution of a PIAR, a PIAR management application applies one or more data values for a plug-in application field to a machine learning model, to obtain: (a) a candidate mapping between one or more sub-values discovered within the data value(s) and another field accepted by an action of another plug-in application, the data value(s) being of a data type different from a reported data type of the other field, and (b) a confidence metric associated with the candidate mapping, based at least in part on whether the sub-value(s) fit(s) one or more stored formats mapped to the other data type. Based on a determination that the confidence metric does not satisfy a threshold confidence criterion, the PIAR management application obtains user input affirming or rejecting the candidate mapping, and applies the user input to execution of the PIAR.

Abstract: Techniques for generating plug-in application recipe (PIAR) extensions are disclosed. A PIAR management application discovers a particular data type within one or more data values for a particular field of a plug-in application, where the particular data type is (a) different from a data type of the particular field as reported by the plug-in application and (b) narrower than the data type of the particular field while complying with the data type of the particular field. The PIAR management application identifies one or more mappings between (a) the particular data type and (b) one or more data types for fields accepted by actions of plug-in applications. The PIAR management application presents a user interface including one or more candidate PIAR extensions based on the mapping(s). Based on a user selection of a candidate PAIR extension, the PIAR management application executes a PIAR that includes the selected PIAR extension.

Abstract: Techniques for semantic analysis-based generation of plug-in application recipes (PIAR's) are disclosed. Responsive to receiving a data item that specifies (a) a desired genus of actions and/or (b) a desired genus of triggers, a PIAR management application performs semantic analysis on the data item to identify one or more candidate PIAR's. The candidate PAIR(s) is/are identified based at least in part on mapping of actions and/or triggers to the desired genus of actions and/or the desired genus of triggers. The mapping is based at least in part on metadata, associated with profiles for plug-in applications, corresponding to actions and/or triggers. The PIAR management application stores, for each plug-in application, a corresponding profile to define the plug-in application for use in one or more future PIAR's. Based on user input approving a particular PIAR in the one or more candidate PIAR's, the PIAR management application executes the particular PIAR.

Abstract: Systems and methods are described for efficiently detecting an optimal number of behaviors to model software system performance data and the aspects of the software systems that best separate the behaviors. The behaviors may be ranked according to how well fitting functions partition the performance data.