The sheer amount of available data demands the introduction of algorithmic approaches.
The data collection tool, namely the MTUHA books, were shown to be partly inconsistent: A significant amount of the secondary data demanded could not be generated by the primary data books.
Applications that cater to the needs of disaster incident response generate large amount of data and demand large computational resource access.
As companies from small startups to large enterprise continue to generate an ever-increasing amount of data, the demand for affordable and scalable databases also increases.
The massive amount of available data demands that data no longer be stored on a single device, but rather distributed among several storage nodes in a network, hence usually referred to as distributed storage systems (DSSs) (see [4] for a nice introduction).
Increase of huge amount of data in every application demands an incremental learning technique for data analysis.
The four billion cell phones in use around the world create massive amounts of data and demand for crowdsourcing technology to aggregate, categorize, and otherwise make sense of it.
On the other hands, increase of smart devices requiring a large amount of data traffics has caused demands to exponentially expand wireless data traffic [11].
The generation of multiview stereoscopic images of large volume rendered data demands an enormous amount of calculations.
