Integrating the Relational Interval Tree into IBM’s DB2 Universal Database Server

User-defined data types such as intervals require specialized access

methods to be efficiently searched and queried. As database implementors cannot

provide appropriate index structures and query processing methods for each conceivable

data type, present-day object-relational database systems offer extensible

indexing frameworks that enable developers to extend the set of built-in index

structures by custom access methods. Although these frameworks permit a seamless

integration of user-defined indexing techniques into query processing they do

not facilitate the actual implementation of the access method itself. In order to leverage

the applicability of indexing frameworks, relational access methods such as

the Relational Interval Tree (RI-tree), an efficient index structure to process interval

intersection queries, mainly rely on the functionality, robustness and performance

of built-in indexes, thus simplifying the index implementation significantly.

To investigate the behavior and performance of the recently released IBM DB2 indexing

framework we use this interface to integrate the RI-tree into the DB2

server. The standard implementation of the RI-tree, however, does not fit to the

narrow corset of the DB2 framework which is restricted to the use of a single index

only. We therefore present our adaptation of the originally two-tree technique to

the single index constraint. As experimental results with interval intersection queries

show, the plugged-in access method delivers excellent performance compared

to other techniques.


Authors: Brochhaus C., Enderle J., Schlosser A., Seidl T., Stolze K.
Published in: Proc. 11th GI Conference on Database Systems for Business, Technology, and the Web (BTW 2005), Karlsruhe, Germany. GI-Edition Lecture Notes in Informatics 65
Publisher: GI - Bonn,Germany
Sprache: EN
Jahr: 2005

(acceptance rate 25%)

Seiten: 67-86
ISBN: 3-88579-394-6
Konferenz: BTW
URL:BTW 2005
Typ: Tagungsbeiträge
Forschungsgebiet: Fast Access to Complex Data