Dell Products Inc.

Emc IP Holding Company LLC

Geetha Venkatesan

Hung The Dinh

Sabu K Syed

Rajesh Krishnan

Vijaya Panguluru Sekhar

Puneet Kumar Arora

Abhijit Mishra

Kannappan Ramu

Akanksha Goel

Manikandan Pammal Rathinavelu

Jatin Kamlesh Thakkar

Seshadri Srinivasan

Kiran Kumar Pidugu

Sourav Datta

Pradeepta Ranjan Choudhury

Muhammed Mohiuddin

Selvaraj Subbaian

Debashish Sahu

Ranjani Muthyam Venkata

Lakshman Kumar Tiwari

Sethukarasi Sockalingam

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.

Predicting statistics gathering times for database activity data using machine learning techniques

Methods, apparatus, and processor-readable storage media for device manufacturing cycle time reduction using machine learning techniques are provided herein. An example computer-implemented method includes obtaining video input related to one or more manufacturing resources in a manufacturing environment; determining availability status information for at least one of the one or more manufacturing resources by applying one or more machine learning models to the obtained video input; and outputting the determined availability status information to at least one user device associated with the manufacturing environment.

Device manufacturing cycle time reduction using machine learning techniques

Methods, apparatus, and processor-readable storage media for transaction visibility framework implemented using artificial intelligence are provided herein. An example computer-implemented method includes monitoring data related to a transaction flow across multiple layers of an enterprise system; determining which of the multiple layers correspond to at least one detected problem within the transaction flow by applying artificial intelligence techniques to the data; generating a visualization of the transaction flow, wherein generating the visualization comprises generating a sequential view of the transaction flow, generating one or more parallel sub-transaction flows pertaining to one or more dependencies of the transaction flow, and producing a visual indication of the detected problem; determining, based on analyzing the generated visualization, one or more automated actions related to the detected problem and the determined layer of the enterprise system corresponding to the detected problem; and automatically initiating the one or more automated actions.

Transaction visibility frameworks implemented using artificial intelligence

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Bengaluru, India

Geetha Venkatesan