The difficulty of identifying all the inherent aspects and the needs or processing large set of data has led to design of food description and food aggregation systems.
Processing large sets of information in reasonable time is another non-trivial task.
However, processing such large sets of data presents new challenges, which is partly a result of the current advances in the field of microprocessors.
With Hadoop (the software platform for distributed processing of large sets of data) at the core, open source data architectures have reached a new level of maturity that some companies may not yet fully appreciate.
Radar systems generate very large sets of data, and processing these in a timely way is important not only for data throughput but also because development of advanced radar often requires many iterations to produce products with the accuracies and capabilities we seek.
Humans are very capable in the explorative learning of patterns from relatively few samples, while classic supervised ML needs large sets of data and long processing time.
It's different from conventional data processing software in that it distributes both the storage and processing over a set of nodes (which can scale to the thousands), providing a much more efficient system for processing large amounts of data.
Similarly, traditional data processing techniques have several limitations of processing large amount of data.
Parallel processing is essential in processing large volumes of data.
There is a broad range of needs for processing large amounts of data.
