Indexing intervals is a fundamental problem, finding a wide range of applications, most notably in temporal and uncertain databases. We propose HINT, a novel and efficient in-memory index for range selection queries over interval collections. HINT applies a hierarchical partitioning approach, which assigns each interval to at most two partitions per level and has controlled space requirements. We reduce the information stored at each partition to the absolutely necessary by dividing the intervals in it, based on whether they begin inside or before the partition boundaries. In addition, our index includes storage optimization techniques for the effective handling of data sparsity and skewness. We show how HINT can be used to efficiently process queries based on Allen’s relationships. Experiments on real and synthetic interval sets of different characteristics show that HINT is typically one order of magnitude faster than existing interval indexing methods.

索引间隔是一个基本问题，应用广泛，尤其是在时间和不确定的数据库中。我们提出 HINT，这是一种新颖且高效的内存索引，用于区间集合上的范围选择查询。HINT 应用分层分区方法，该方法将每个间隔分配给每个级别最多两个分区，并且具有受控的空间要求。我们通过划分每个分区中的间隔，根据它们是在分区边界内还是在分区边界之前开始，将存储在每个分区中的信息减少到绝对必要的程度。此外，我们的索引包括用于有效处理数据稀疏性和偏度的存储优化技术。我们展示了如何使用 HINT 来有效地处理基于 Allen 关系的查询。