An Adaptive Overflow Technique for B-trees

We present a new overrow technique for B-trees. The technique is a hybrid of partial expansions and unbalanced splits. This technique is asymmetric and adaptive. Considering a growing le (only insertions), the storage utilization is 77% for random keys, 70% for sorted keys, and over 75% for non-uniform distributed keys. Similar results are achieved when we have deletions mixed with insertions. One of the main properties of this technique is that the storage utilization is very stable with respect to changes of the data distribution. This technique may be used for other bucket-based le structures, like extendible hashing or bounded disorder les.