  In the big data era researchers face a series of problems. Even standard
approaches/methodologies, like linear regression, can be difficult or
problematic with huge volumes of data. Traditional approaches for regression in
big datasets may suffer due to the large sample size, since they involve
inverting huge data matrices or even because the data cannot fit to the memory.
Proposed approaches are based on selecting representative subdata to run the
regression. Existing approaches select the subdata using information criteria
and/or properties from orthogonal arrays. In the present paper we improve
existing algorithms providing a new algorithm that is based on D-optimality
approach. We provide simulation evidence for its performance. Evidence about
the parameters of the proposed algorithm is also provided in order to clarify
the trade-offs between execution time and information gain. Real data
applications are also provided.
