@inproceedings{DBLP:conf/vldb/JermaineDO99, author = {Chris Jermaine and Anindya Datta and Edward Omiecinski}, editor = {Malcolm P. Atkinson and Maria E. Orlowska and Patrick Valduriez and Stanley B. Zdonik and Michael L. Brodie}, title = {A Novel Index Supporting High Volume Data Warehouse Insertion}, booktitle = {VLDB'99, Proceedings of 25th International Conference on Very Large Data Bases, September 7-10, 1999, Edinburgh, Scotland, UK}, publisher = {Morgan Kaufmann}, year = {1999}, isbn = {1-55860-615-7}, pages = {235-246}, ee = {db/conf/vldb/JermaineDO99.html}, crossref = {DBLP:conf/vldb/99}, bibsource = {DBLP, http://dblp.uni-trier.de} }
While the desire to support fast, ad hoc query processing for large data warehouses has motivated the recent introduction of many new indexing structures, with a few notable exceptions (namely, the LSM-Tree [4] and the Stepped Merge Method [1]) little attention has been given to developing new indexing schemes that allow fast insertions. Since additions to a large warehouse may number in the millions per day, indices that require a disk seek (or even a significant fraction of a seek) per insertion are not acceptable.
In this paper, we offer an alternative to the B+-tree called the Y-tree for indexing huge warehouses having frequent insertions. The Y-tree is a new indexing structure supporting both point and range queries over a single attribute, with retrieval performance comparable to the B+-tree. For processing insertions, however, the Y-tree may exhibit a speedup of 100 times over batched insertions into a B+-tree.
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