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Speeding Algorithms by Shrinking Data

MIT News (11/13/12) Kimberly Allen

Massachusetts Institute of Technology (MIT) researchers have developed a technique to represent data so that it takes up much less space in memory but can still be processed in conventional ways. The researchers say the approach will lead to faster computations, and could be more generally applicable than other big-data techniques because it can work with existing algorithms. The researchers tested the technique on two-dimensional location data generated by global positioning system receivers used in cars. The algorithm approximates the straight line that is made by the different points at which a car turns. The most important aspect of the algorithm is that it can compress data on the fly by using a combination of linear approximations and random samples, says MIT’s Dan Feldman. Although some of the information is lost during compression, the researchers were able to provide mathematical guarantees that the error introduced will stay beneath a low threshold. The researchers are now investigating how the algorithm can be applied to video data analysis, in which each line segment represents a scene, and the junctures between line segments represent cuts.

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