New Algorithm Could Reduce Complexity Of Big Data Whenever a scientific experiment is conducted, the results are turned into numbers, often producing huge datasets. In order to reduce the size of the data, computer programmers use algorithms that can find and extract the principal features that represent the most salient statistical properties. But many such algorithms cannot be applied directly to these large volumes of data. Reza Oftadeh, doctoral student in the Department of Computer Science and Engineering at Texas A&M University, advised by Dylan Shell, faculty in the department, developed an algorithm applicable to large datasets that can directly order features from most salient to least.