Communications of the ACM recently selected a SIGMOD paper co-authored by Nan Zhang (with A. Asudeh, J. Augustine, A. Nazi, S. Thirumuruganathan, G. Das and D. Srivastava), “Efficient Signal Reconstruction for a Broad Range of Applications”, as a Research Highlight. An updated version of the article targeting the general ACM readership, “Scalable Signal Reconstruction for a Broad Range of Applications”, will be published in an upcoming issue of Communications of the ACM. Our work adapts techniques developed for scalable similarity joins in database systems to speed up how one can solve an under-determined system of linear equations by finding the closest solution to a given prior.
This selection followed the selection of the work for an ACM SIGMOD Research Highlight Award, which “is an award for the database community to showcase a set of research projects that exemplify core database research, address an important problem, represent a definitive milestone in solving the problem, and have the potential of significant impact”.
See here for the editor’s highlights of the paper, and here for a compact, 8-page, extended abstract of the work.
An extended version of the paper, “Scalable Algorithms for Signal Reconstruction by Leveraging Similarity Joins”, was accepted for publication in The VLDB Journal.