Abstract: Methods, apparatus, and processor-readable storage media for implementing an automated data archival framework using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining data associated with one or more storage systems; determining one or more storage-related features within the obtained data by processing at least a portion of the obtained data; predicting at least one data archival class, from a set of multiple predetermined data archival classes, for at least a portion of the obtained data by processing the one or more storage-related features using one or more artificial intelligence techniques; and performing one or more automated actions based at least in part on the at least one predicted data archival class.

Abstract: Methods, apparatus, and processor-readable storage media for automatically predicting device recycling opportunities using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining data associated with one or more devices; determining end of life-related information for the one or more devices by processing at least a portion of the obtained data; predicting at least one device recycling opportunity for at least one of the one or more devices by processing at least a portion of the determined end of life-related information using one or more artificial intelligence techniques; and performing one or more automated actions based at least in part on the at least one predicted device recycling opportunity.

Abstract: Methods, apparatus, and processor-readable storage media for automatically predicting fail-over of message-oriented middleware systems are provided herein. An example computer-implemented method includes obtaining one or more message-oriented middleware parameter values for at least a portion of multiple message-oriented middleware systems; detecting one or more fail-over-related anomalies associated with at least one of the multiple message-oriented middleware systems by processing at least a portion of the one or more message-oriented middleware parameter values using one or more machine learning techniques; and automatically migrating, based at least in part on the one or more detected fail-over-related anomalies, at least a portion of data associated with the at least one message-oriented middleware system associated with the one or more detected fail-over-related anomalies to at least one of the other of the multiple message-oriented middleware systems.

Abstract: Methods, apparatus, and processor-readable storage media for automatically predicting dispatch-related data using machine learning techniques are provided herein. An example computer-implemented method includes obtaining data, from one or more user channels, pertaining to at least one issue; determining, based at least in part on the obtained data, that at least one dispatch is to be carried out in connection with attempting to resolve the at least one issue; predicting, by processing at least a portion of the obtained data using one or more machine learning techniques, an approval mode associated with the at least one dispatch and at least one outcome associated with the at least one dispatch; and performing one or more automated actions based at least in part on one or more of the predicted approval mode and the at least one predicted outcome.

Abstract: Methods, apparatus, and processor-readable storage media for automated database operation classification using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining at least one query related to one or more database operations; obtaining information pertaining to a set of multiple storage resources; classifying the at least one query as associated with at least one of multiple subsets of storage resources among the set of multiple storage resources by processing at least a portion of the query and at least a portion of the information pertaining to the set of multiple storage resources using one or more artificial intelligence techniques; and performing one or more automated actions based at least in part on the classifying of the at least one query.

Abstract: Methods, apparatus, and processor-readable storage media for implementing extensible upgrade and modification as a service are provided herein. An example computer-implemented method includes processing one or more modifiers, wherein each modifier includes an independent processing unit having a given canonical structure and is configured to execute one or more automated actions related to at least one of application modification and application migration; obtaining data pertaining to multiple applications across multiple computing environments; determining, based at least in part on processing at least a portion of the obtained data, at least one of the one or more modifiers applicable for use in executing at least one of the one or more automated actions in connection with at least a portion of the multiple applications; and executing the at least one of the one or more automated actions using the at least one determined modifier.

Abstract: Methods, apparatus, and processor-readable storage media for automatically summarizing event-related data using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining text-based data and non-text-based data associated with at least one virtual event comprising one or more participants; generating a content-related summarization of one or more of at least a portion of the text-based data and at least a portion of the non-text-based data using at least a first set of one or more artificial intelligence techniques; generating a participant sentiment-related summarization associated with one or more of at least a portion of the text-based data and at least a portion of the non-text-based data using at least a second set of one or more artificial intelligence techniques; and performing one or more automated actions based at least in part on one or more of the content-related summarization and the participant sentiment-related summarization.

Abstract: Methods, apparatus, and processor-readable storage media for automated database operation classification using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining at least one query related to one or more database operations; obtaining information pertaining to a set of multiple storage resources; classifying the at least one query as associated with at least one of multiple subsets of storage resources among the set of multiple storage resources by processing at least a portion of the query and at least a portion of the information pertaining to the set of multiple storage resources using one or more artificial intelligence techniques; and performing one or more automated actions based at least in part on the classifying of the at least one query.

Abstract: Methods, apparatus, and processor-readable storage media for automatically managing event-related communication data using machine learning techniques are provided herein.

Abstract: Methods, apparatus, and processor-readable storage media for automatically classifying cloud infrastructure components for prioritized multi-tenant cloud environment resolution using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining historical data pertaining to a multi-tenant cloud environment; training one or more artificial intelligence techniques, using at least a portion of the obtained historical data, for classifying cloud infrastructure components for prioritizing incident-related resolution; classifying one or more cloud infrastructure components, within the multi-tenant cloud environment and associated with one or more server-related issues, into one or more of multiple resolution priority classes; and performing one or more automated actions based at least in part on the classifying of the one or more cloud infrastructure components.

Abstract: Methods, apparatus, and processor-readable storage media for generating and processing personal information chains using machine learning techniques are provided herein. An example computer-implemented method includes processing data, from one or more data sources, pertaining to one or more events involving an individual; generating a personal information chain associated with the individual by processing at least a portion of the processed data using at least one cryptographic function and linking that at least a portion of the processed data in accordance with at least one temporal parameter; performing anomaly detection by processing at least a portion of the personal information chain associated with the individual using one or more machine learning techniques; and performing one or more automated actions based at least in part on one or more of the personal information chain and results from the anomaly detection.

Abstract: Methods, apparatus, and processor-readable storage media for automatically classifying images for storage-related determinations using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining at least one image associated with an enterprise; classifying one or more portions of the at least one image into one or more enterprise-related categories by processing at least a portion of the at least one image using one or more artificial intelligence techniques; and performing, based at least in part on the classifying of the one or more portions of the at least one image, one or more automated actions pertaining to storing the at least one image in at least one of multiple storage options.

Abstract: Methods, apparatus, and processor-readable storage media for generating data replication configurations using AI techniques are provided herein. An example computer-implemented method includes obtaining input data pertaining to at least one data replication operation; determining a set of configuration parameters for the at least one data replication operation by applying one or more AI techniques to at least a portion of the input data; and performing one or more automated actions based at least in part on the determined set of configuration parameters for the at least one data replication operation.

Abstract: Methods, apparatus, and processor-readable storage media for automated configuration determinations for data center devices using artificial intelligence are provided herein. An example computer-implemented method includes obtaining input information pertaining to one or more device-related changes to a data center; obtaining telemetry data attributed to one or more devices in the data center; determining one or more device configurations for implementation in at least one device in the data center in connection with the one or more device-related changes by processing the input information and the obtained telemetry data using one or more artificial intelligence techniques; and performing at least one automated action based at least in part on the one or more determined device configurations.

Abstract: Methods, apparatus, and processor-readable storage media for automatically predicting fail-over of message-oriented middleware systems are provided herein. An example computer-implemented method includes obtaining one or more message-oriented middleware parameter values for at least a portion of multiple message-oriented middleware systems; detecting one or more fail-over-related anomalies associated with at least one of the multiple message-oriented middleware systems by processing at least a portion of the one or more message-oriented middleware parameter values using one or more machine learning techniques; and automatically migrating, based at least in part on the one or more detected fail-over-related anomalies, at least a portion of data associated with the at least one message-oriented middleware system associated with the one or more detected fail-over-related anomalies to at least one of the other of the multiple message-oriented middleware systems.

Abstract: Methods, apparatus, and processor-readable storage media for automatically performing varied security scans on distributed files using machine learning techniques are provided herein. An example computer-implemented method includes obtaining at least one input file from one of multiple source channels; identifying a data security scan operation, from a set of multiple data security scan operations, for the at least one input file by processing historical data attributed to the at least one input file using machine learning techniques; executing the identified data security scan operation on the at least one input file; generating a hash of the at least one input file and information pertaining to results of the executed data security scan operation; caching the generated hash in at least one cache; and performing automated actions based on the caching of the generated hash in the at least one cache.

Abstract: Methods, apparatus, and processor-readable storage media for automatically classifying cloud infrastructure components for prioritized multi-tenant cloud environment resolution using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining historical data pertaining to a multi-tenant cloud environment; training one or more artificial intelligence techniques, using at least a portion of the obtained historical data, for classifying cloud infrastructure components for prioritizing incident-related resolution; classifying one or more cloud infrastructure components, within the multi-tenant cloud environment and associated with one or more server-related issues, into one or more of multiple resolution priority classes; and performing one or more automated actions based at least in part on the classifying of the one or more cloud infrastructure components.

Abstract: Methods, apparatus, and processor-readable storage media for automatically prioritizing supply chain-related demand using artificial intelligence techniques are provided herein.

Abstract: Methods, apparatus, and processor-readable storage media for generating temporal predictions for provisioning cloud resources using trained machine learning techniques are provided herein. An example computer-implemented method includes training one or more machine learning techniques using historical data pertaining to cloud resource provisioning and related order information; obtaining data pertaining to at least one order related to one or more hardware components, wherein the at least one order includes one or more cloud resources; generating at least one temporal prediction for provisioning the one or more cloud resources, in fulfillment of the at least one order, by processing at least a portion of the obtained data using the one or more trained machine learning techniques; and performing one or more automated actions based at least in part on the at least one generated temporal prediction.

Abstract: Methods, apparatus, and processor-readable storage media for a neural network-based message communication framework with summarization and on-demand audio output generation are provided herein.