Indexing possibilistic numerical data using Interval B+-trees

When record sets become large, indexing becomes a required technique for speeding up querying. This paper proposes an indexing technique for interval data. Such data are common in possibility based relational databases but are also frequently used in other applications. Our approach is an adaptation of a B+-tree, which is currently still one of the most efficient indexing techniques. Because it can store interval data, we name it the Interval B+-tree (IBPT). It is illustrated how an IBPT index can be built and applied in practice to speed up the evaluation of fuzzy queries on possibilistic relational databases.