Big Data refers generally to vast amounts of information collected by networked devices and systems. In this domain, data capture is technologically simple and the challenge lies in the post-capture analytics and transmission. Big Data is also prominent in other domains where the capture of data is challenging as well, such as in the medical sciences, telecom and basic research in the sciences.
In these areas, communication signals and scientific phenomena of interest tend to occur on time scales and at throughput levels that are too fast to be sampled and digitized in real time.
In other words, the Big Data problem is not just limited to analytics; it also includes data capture, storage, and transmission. Anamorphic Stretch Transform (AST) is a new mathematical transform that offers a solution for Big Data bottleneck, it slows down ultrafast signal so it can be captured with a slower instrument and at the same time it compresses the volume of the resulting data. It does so by reducing the length-bandwidth product. AST can operate on both analog and all-digital data such as on images where it outperforms JPEG and other standard compression techniques. AST is a non-iterative algorithm and does not need any feature detection, feedback.