Dell Products Inc.

Ponnayan Sekar

Dhilip S Kumar

Hung The Dinh

Bijan Kumar Mohanty

Sathish Kumar Bikumala

Deepa Sreekumar

Methods, apparatus, and processor-readable storage media for data archival recommendation systems using artificial intelligence techniques are provided herein. An example computer-implemented method includes processing data associated with database operations performed across multiple database storage zones in one or more databases; generating, using one or more artificial intelligence techniques and based at least in part on the processing of data associated with the database operations performed across the multiple database storage zones, one or more temporal-based data archival recommendations for at least a portion of the multiple database storage zones in the one or more databases; and performing one or more automated actions based at least in part on the one or more temporal-based data archival recommendations.

Data archival recommendation systems using artificial intelligence techniques

A method including: generating a conversation flow signature based on a set of communication transcripts, each of the communication transcripts being associated with a support request for a product, each of the communication transcripts being a text transcript of a communication between a respective customer and a respective customer support agent; classifying the conversation flow signature into one of a plurality of categories, the conversation flow signature being classified by using a machine learning classifier that is trained based on customer support records, each of the plurality of categories corresponding to a respective set of steps for configuring or repairing the product; and outputting an indication of the respective set of steps that is associated with the category in which the conversation flow signature is classified.

Collaborative resolution framework

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.

Automated database operation classification using artificial intelligence techniques

Methods and systems are provided for concurrently upgrading a primary database and a standby database that is synchronized with the primary database, while still protecting the stored data from the risk of hardware or other failure during the upgrade process. The standby database is mounted to an NFS (Network File System) located on a database access node. The upgraded primary and standby databases are built and mounted to the NFS mount point. A level-zero backup and one or more incremental backups of the deprecated standby database are generated. Each backup may be concurrently transferred to the upgraded databases via the mount point. Upon generation of a final incremental backup, the primary database is frozen and the tablespace metadata to transferred to the upgraded databases via the mount point. The upgraded primary database may be started upon importing of the tablespace metadata.

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Bengaluru, India

Ponnayan Sekar