It has expertise in voice recognition, data mining, wearables, and of course, hardware and software design.
Experiments are performed, using a handwritten analysis application together with a benchmark dataset, to evaluate and illustrate the feasibility, efficiency, and flexibility of reconfigurable hardware for data mining applications.
There are now at least a dozen kinds of "aaS", including data mining (DMaaS), virtualisation (VaaS) and even hardware (HaaS).
Watson was a bundle of advanced technologies, including speech recognition, machine learning, natural-language processing, data mining and ultrafast in-memory computer hardware.
Our main objective is to provide efficient dynamic reconfigurable hardware architectures for data mining applications on mobile and embedded devices.
As demonstrated in [30], although reconfigurable hardware typically consumes less power than microprocessor-based software-only designs, we are planning and designing experiments to evaluate the power consumption in reconfigurable hardware designs for data mining applications.
Since our intention is to provide reconfigurable hardware architectures for data mining applications on mobile and embedded devices, we decided to utilize a data set that is appropriate for applications on these devices.
The scheme measured (normalization phase), attuned (database for patterns) and approximated (via data mining techniques) the RSSI values irrespective of the hardware devices to counter the effects of doors and walls on RSSI.
The fact that computational power has reached the ability to handle Big Data through efficient algorithms (as well as hardware advances, of course) lets data mining handle the Big Volume, Velocity, Variety, Veracity, and Value of the data generated by Health Informatics (traditional or otherwise).
There are bright prospects for big data mining by using quantum-based search algorithm when the hardware of quantum computing has become mature.
