Intervals represent a fundamental data type for temporal, scientific, and spatial databases where time stamps and point data are extended to time spans and range data, respectively. For OLTP and OLAP applications on large amounts of data, not only intersection queries have to be processed efficiently but also general interval relationships including before, meets, overlaps, starts, finishes, contains, equals, during, startedBy, finishedBy, overlappedBy, metBy, and after. Our new algorithms use the Relational Interval Tree, a purely SQL-based and objectrelationally wrapped index structure. The technique therefore preserves the industrial strength of the underlying RDBMS including stability, transactions, and performance. The efficiency of our approach is demonstrated by an experimental evaluation on a real weblog data set containing one million sessions.