AMID: Approximation of MultI-measured Data using SVD

Approximate query answering has recently emerged as an effective method for generating a viable answer. Among various techniques for approximate query answering, wavelets have received a lot of attention. However, wavelet techniques minimizing the root squared error (i.e., the image norm error) have several problems such as the poor quality of reconstructed data when the original data is biased. In this paper, we present AMID (Approximation of MultI-measured Data using SVD) for multi-measured data. In AMID, we adapt the singular value decomposition (SVD) to compress multi-measured data. We show that SVD guarantees the root squared error, and also drive an error bound of SVD for an individual data value, using mathematical analyses. In addition, in order to improve the accuracy of approximated data, we combine SVD and wavelets in AMID. Since SVD is applied to a fixed matrix, we use various properties of matrices to adapt SVD to the incremental update environment. We devise two variants of AMID for the incremental update environment: incremental AMID and local AMID. To the best of our knowledge, our work is the first to extend SVD to incremental update environments.