Temporal aspects have received tons of interest in Information Retrieval (IR) and related fields, including database search. The focus of temporal IR is on improving the effectiveness of search by exploiting temporal information in objects and queries. In this work, we study efficient indexing for the fundamental time-travel IR query. Given such a query q with a time interval of interest and a set of descriptive elements (e.g., keywords), the goal is to retrieve all data objects (e.g., documents) whose time interval overlaps with query's and their description contains the elements in q . Existing methods extend the inverted index to answer time-travel IR queries, with simple but ineffective temporal indexing. We propose new methods which capitalize on the state-of-the-art interval index HINT in two ways; either by extending again the inverted index or adopting the time-first irHINT approach which directly builds on HINT. Our experiments showed that irHINT outperforms all IR-first methods, while exhibiting good indexing and updating costs.

A wide range of applications manage interval data with selections and overlap joins being the most fundamental querying operations. Selection queries are typically evaluated using interval indexing. However, the statethe-of-art HINT index and its competitors, are only designed for 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 strategies termed level-based and partition-based, which 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, and in particular, partition-based can prevent scanning every index partition more than once. Our experiments on real-world intervals showed that our batch 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 proposed for HINT and for indexed inputs, we propose a novel algorithm HINT-join which concurrently scans the input indices, joining partition pairs with optFS. Our tests showed the advantage of HINT-join over indexed nestedloops solutions that employ either B+-trees or probing a single HINT even powered by our partition-based batch processing.