Querying Interval Data on Steroids

A wide range of applications manage interval data with, e.g., in temporal analytics, statistics, data anonymization etc. In this context, selection queries and overlap joins being the two most fundamental querying operations. Selection queries are typically evaluated using interval indexing. However, the state-the-of-art HINT index and its competitors, are only designed to service single query requests while modern systems receive a large number of queries at the same time. In view of this challenge, we study the batch processing of selection queries on HINT. We propose two novel evaluation strategies termed level-based and partition-based, which both operate in a per-level fashion, i.e., they collect the results for all queries at an index level before moving to the next. The new strategies reduce the cache misses when climbing the index hierarchy or accessing multiple partitions per level, and in particular, partition-based can prevent scanning every index partition more than once. Our experimental analysis on real-world intervals showed that our batch processing strategies always outperform a baseline which executes queries in a serial fashion, and that partition-based is overall the most efficient one. Motivated by our shared computation techniques for query batches, we also study overlap joins anew across the entire spectrum of different setups, based on the (pre)-existence of interval indexing. For unindexed inputs, we enhance the state-of-the-art optFS join algorithm with effective partitioning techniques recently proposed for HINT and for indexed inputs, we propose a novel algorithm HINT-join which concurrently scans the HINT input indices, joining partition pairs with optFS. Our tests showed the advantage of HINT-join over indexed nested-loops solutions that employ either B$^+$-trees or probing a single HINT even if powered by our partition-based batch processing.