Abstract: In general, embodiments relates to a method for managing a technical support session, comprising: obtaining customer identification information for a technical support session, extracting at least one keyword for the technical support session, identifying a plurality of historical technical support sessions using the at least one keyword and the customer identification information, and displaying at least one of the plurality of historical technical support sessions to a technical support person (TSP) during the technical support session.

Abstract: In general, embodiments relates to a method for managing a technical support session, comprising: obtaining technical support question from a technical support person (TSP) that is conducting the technical support session; determining that the technical support question is a duplicate of a prior technical support question; in response to the determination, obtaining a quality score for the technical support question; and displaying the quality score to the TSP in a user interface on a technical support system that the TSP is using the conduct the technical support session.

Abstract: Methods, apparatus, and processor-readable storage media for automatically detecting data offloading methods using data bucketing and machine learning techniques are provided herein. An example computer-implemented method includes obtaining operations data and configuration data for one or more storage objects in a database; determining one or more times at which data offloading is to be carried out for at least one of the storage objects in the database, wherein determining the one or more times includes processing at least a portion of the operations data using one or more machine learning techniques; generating at least one data offloading protocol, comprising one or more data offloading methods, by processing at least a portion of the configuration data; and automatically executing, in accordance with the one or more determined times, the at least one generated data offloading protocol for at least a portion of the one or more storage objects in the database.

Abstract: In general, embodiments relates to a method for managing a technical support session, comprising: obtaining customer identification information for a technical support session, extracting at least one keyword for the technical support session, identifying a plurality of historical technical support sessions using the at least one keyword and the customer identification information, and displaying at least one of the plurality of historical technical support sessions to a technical support person (TSP) during the technical support session.

Abstract: In general, embodiments relate to a method for managing a technical support session, comprising: determining a technical support issue (TSI) for a technical support session; identifying a question path graph (QPG) associated with the TSI; and displaying at least a portion of the QPG to a technical support person (TSP) during the technical support session.

Abstract: In general, in one aspect, embodiments relate to a method for managing technical support sessions, the method comprising: generating a first plurality of local technical support sessions, transmitting at least a portion of the first plurality of local technical support sessions to a technical support hub, and receiving a local technical support session from a second technical support system, wherein the local technical support session is presented to a technical support person (TSP) during a technical support session performed on a first technical support system.

Abstract: In general, embodiments relates to a method for managing a technical support session, comprising: obtaining technical support question from a technical support person (TSP) that is conducting the technical support session; determining that the technical support question is a duplicate of a prior technical support question; in response to the determination, obtaining a quality score for the technical support question; and displaying the quality score to the TSP in a user interface on a technical support system that the TSP is using the conduct the technical support session.

Abstract: In general, embodiments relates to a method for managing technical support sessions, the method comprising generating a first plurality of local technical support sessions, applying a sharing compliance rule to at least a portion of the first plurality of local technical support sessions to generate a second plurality of modified technical support sessions, transmitting the second plurality of modified technical support sessions to a technical support hub, and receiving a local technical support session originating from a second technical support system, wherein the local technical support session is presented to a technical support person (TSP) during a technical support session performed on a first technical support system.

Abstract: In general, embodiments relate to a method for managing a technical support session, comprising: generating a question path graph (QPG) based on a plurality of question sequences associated with technical support sessions, and displaying at least a portion of the QPG to a technical support person (TSP) during a technical support session.

Abstract: Methods, apparatus, and processor-readable storage media for device component management using deep learning techniques are provided herein. An example computer-implemented method includes obtaining telemetry data from one or more enterprise devices; determining, for each of the one or more enterprise devices, values for multiple device attributes by processing the obtained telemetry data; generating, for each of the one or more enterprise devices, at least one prediction related to lifecycle information of at least one device component by processing the determined attribute values using one or more deep learning techniques; and performing one or more automated actions based at least in part on the at least one generated prediction.

Abstract: In general, embodiments relate to a method for managing a technical support (TS) session on a technical support system. The embodiments include receiving TS correspondence from a client, wherein the TS correspondence is associated with the TS session, classifying the TS correspondence to assign it a question classification, based on the question classification, making a determination that the TS correspondence matches at least one prior received TS correspondence, wherein the at least one prior received TS correspondence is associated with the TS session, and visually identifying the TS correspondence and the at least one prior received TS correspondence on a graphical customer interface (GUI) of the technical support system.

Abstract: Techniques described herein relate to a method for managing support cases of clients. The method may include obtaining a support case associated with a client of the clients; in response to obtaining the support case, obtaining contextual information associated with the support case; obtaining client telemetry data associated with the support case; selecting a portion of training data that is similar to the support case based on the client telemetry data, the contextual information, and the support case; generating a prediction model using the portion of the training data and a classification algorithm; and generating predictions using the prediction model, the client telemetry data, the contextual information, and the support case.

Abstract: Methods, apparatus, and processor-readable storage media for predicting statistics gathering times for database activity data using machine learning techniques are provided herein. An example computer-implemented method includes obtaining activity data pertaining to at least one storage object of a given database; determining one or more times to gather statistics related to activity data pertaining to the at least one storage object of the given database by processing at least a portion of the obtained activity data using one or more machine learning techniques; and performing at least one automated action related to statistics gathering based at least in part on the one or more determined times.

Abstract: A method for managing applications includes obtaining an application resolution report from an administrative system, wherein the application resolution report comprises a customer query and a response, applying a neural network algorithm on a set of tokens associated with the application resolution report to obtain a vector representation of the application resolution report, performing a probability analysis on each vector in the vector representation, based on the probability analysis, identifying an unanswered portion of the application resolution report, and in response to the unanswered portion of the application resolution report, performing a query resolution.

Abstract: Methods, apparatus, and processor-readable storage media for automatically detecting data offloading methods using data bucketing and machine learning techniques are provided herein. An example computer-implemented method includes obtaining operations data and configuration data for one or more storage objects in a database; determining one or more times at which data offloading is to be carried out for at least one of the storage objects in the database, wherein determining the one or more times includes processing at least a portion of the operations data using one or more machine learning techniques; generating at least one data offloading protocol, comprising one or more data offloading methods, by processing at least a portion of the configuration data; and automatically executing, in accordance with the one or more determined times, the at least one generated data offloading protocol for at least a portion of the one or more storage objects in the database.

Abstract: A system and method reorder execution of a test suite to be performed on a given device according to an initial testing order. Each testing sequence in the test suite is analyzed for dependencies between test cases, and these dependencies are recorded in directed graphs. Next, a machine learning algorithm, such as the random forest algorithm, is trained on multi-dimensional historical testing data according to several testing parameters to predict success or failure of any given test. The trained algorithm is used to predict, for a given device under test, which of the test cases are likely to fail, and to compute a confidence value for each such prediction. The directed graphs then are reorganized so that graphs containing tests most likely to fail are executed early in the test suite, according to a modified testing order that accounts for both test dependencies and the confidence values.

Abstract: Methods, apparatus, and processor-readable storage media for predicting statistics gathering times for database activity data using machine learning techniques are provided herein. An example computer-implemented method includes obtaining activity data pertaining to at least one storage object of a given database; determining one or more times to gather statistics related to activity data pertaining to the at least one storage object of the given database by processing at least a portion of the obtained activity data using one or more machine learning techniques; and performing at least one automated action related to statistics gathering based at least in part on the one or more determined times.

Abstract: Methods, apparatus, and processor-readable storage media for device component management using deep learning techniques are provided herein. An example computer-implemented method includes obtaining telemetry data from one or more enterprise devices; determining, for each of the one or more enterprise devices, values for multiple device attributes by processing the obtained telemetry data; generating, for each of the one or more enterprise devices, at least one prediction related to lifecycle information of at least one device component by processing the determined attribute values using one or more deep learning techniques; and performing one or more automated actions based at least in part on the at least one generated prediction.