Cisco Technology, Inc.

James D Stanley, Iii

Carlos M Pignataro

Gonzalo A Salgueiro

Rajesh Vargheese

David A Maluf

Sateesh Kumar Addepalli

Raghuram S Sudhaakar

Ammar Rayes

Hazim Hashim Dahir

David Prantl

Matthew R Engle

Prashant R Prabhudesai

A plurality of time and motion data sets are correlated at a network connected device. The time and motion data sets track an item of interest, and at least one of the time and motion data sets comprises energy consumption data or wireless local area network location data. Physical presence for the item of interest is determined based upon the correlating of the multiple time and motion data sets. Based upon the physical presence of the item of interest, an unacceptable condition for the item of interest may be determined. The unacceptable condition for the item of interest is remedied.

Time and motion data fusion for determining and remedying issues based on physical presence

A plurality of data sets are compiled in a memory. A first data set comprises a location mapping of an item of interest within a predetermined environment, and a second set comprises energy usage data within the predetermined environment. The first data set is correlated, via a processor, with the second data set to locate a subset of energy usage data in the second data set associated with the item of interest. A third data set is generated by combining the first data set with the subset of energy usage data in the second data set to increase an accuracy of the location mapping of the item of interest based upon the subset of energy usage data. The third data set is applied to a computing resource.

Time and motion data fusion for high fidelity data sets

In one embodiment, a stream of data packets originated by a visual data source is received at an edge device in a network. The data packets include at least one of video data, image data, and geo spatial data. Next, a visual data attribute is extracted at the edge device from the stream of data packets according to an edge-based extraction algorithm. The extracted visual data attribute is vectorized at the edge device via quantization vectors. The vectorized visual data attribute is then indexed at the edge device in a schema-less database that stores indexed visual data attributes.

Line rate visual analytics on edge devices

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Austin, TX, United States of America

James D Stanley, Iii