Efficiently answer top-k queries on typed intervals

Consider a database consisting of a set of tuples, each of which contains an interval, a type and a weight. These tuples are called typed intervals and used to support applications involving diverse intervals. In this paper, we study top-k queries on typed intervals. The query reports k intervals intersecting the query time, containing a particular type and having the largest weight. The query time can be a point or an interval. Further, we define top-k continuous queries that return qualified intervals at each time point during the query interval. To efficiently answer such queries, a key challenge is to build an index structure to manage typed intervals. Employing the standard interval tree, we build the structure in a compact way to reduce the I/O cost, and provide analytically derived partitioning methods to manage the data. Query algorithms are proposed to support point, interval and continuous queries. An auxiliary main-memory structure is developed to report continuous results. Using large real and synthetic datasets, extensive experiments are performed in a prototype database system to demonstrate the effectiveness, efficiency and scalability. The results show that our method significantly outperforms alternative methods in most settings.