|
|
|
@inproceedings{DBLP:conf/vldb/ParkerSV92,
author = {Douglas Stott Parker Jr. and
Eric Simon and
Patrick Valduriez},
editor = {Li-Yan Yuan},
title = {SVP: A Model Capturing Sets, Lists, Streams, and Parallelism},
booktitle = {18th International Conference on Very Large Data Bases, August
23-27, 1992, Vancouver, Canada, Proceedings},
publisher = {Morgan Kaufmann},
year = {1992},
isbn = {1-55860-151-1},
pages = {115-126},
ee = {db/conf/vldb/ParkerSV92.html},
crossref = {DBLP:conf/vldb/92},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
We describe the SVP data model. The goal of SVP is to model both set and stream data, and to model parallelism in bulk data processing. SVP also shows promise for other parallel processing applications.
SVP models collections, which include sets and streams as special cases. Collections are represented as ordered tree structures, and divide-and-conquer mappings are easily defined on these structures. We show that many useful database mappings (queries) have a divide-and-conquer format when specified using collections, and that this specification exposes parallelism.
We formalize a class of divide-and-conquer mappings on collections called SVP-transducers. SVP-transducers generalize aggregates, set mappings, stream transductions, and scan computations. At the same time, they have a rigorous semantics based on continuity with respect to collection orderings, and permit implicit specification of both independent and pipeline parallelism.
Copyright © 1992 by the VLDB Endowment. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by the permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment.
