Efficient interval management using object-relational database servers

Abstract

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 original two-tree technique to the single index constraint as well as an approximate adaptation which conceptually only needs a single index. As experimental results with interval intersection queries show, the plugged-in access methods deliver excellent performance compared to other techniques.

Parallel indexing technique for spatio-temporal data

“…The fundamental idea behind this approach is to transform both temporal data and spatial data into multidimensional intervals, and then deploy a parallel access method for the multi-dimensional intervals. A number of access methods for interval data have been proposed, including the Interval B-Tree (Ang and Tan, 1995), RI-Tree (Brochhaus et al, 2005), TD-Tree (Stantic et al, 2010) and compressed B + -Tree (Lin, 2008(Lin, , 2012. The compressed B + -Tree mainly focuses on movement data.…”