Cache-Efficient Sweeping-Based Interval Joins for Extended Allen Relation Predicates (Extended Version)

TL;DR

This paper introduces cache-efficient plane-sweeping algorithms for interval joins that support complex temporal relations, significantly improving speed and scalability for real-time event processing in temporal databases.

Contribution

It presents a flexible framework for interval join algorithms that leverage cache-efficient data structures and indexing methods, advancing the state-of-the-art in temporal query processing.

Findings

Algorithms are several times faster than existing methods.

Scales better for large datasets and real-time applications.

Effectively supports a wide range of interval relations.

Abstract

We develop a family of efficient plane-sweeping interval join algorithms that can evaluate a wide range of interval predicates such as Allen's relationships and parameterized relationships. Our technique is based on a framework, components of which can be flexibly combined in different manners to support the required interval relation. In temporal databases, our algorithms can exploit a well-known and flexible access method, the Timeline Index, thus expanding the set of operations it supports even further. Additionally, employing a compact data structure, the gapless hash map, we utilize the CPU cache efficiently. In an experimental evaluation, we show that our approach is several times faster and scales better than state-of-the-art techniques, while being much better suited for real-time event processing.