Cache-efficient sweeping-based interval joins for extended Allen relation predicates

Abstract

We develop a family of efficient plane-sweeping interval join algorithms for evaluating 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.

Complex Event Recognition with Allen Relations

“…Several approaches compute Allen relations using versions of the plane-sweeping algorithm. For example, Piatov et al (2021) developed a family of interval join algorithms, including Allen relations, with log-linear time complexity. Chekol et al (2019) extended SPARQL with plane-sweeping-based algorithms for Allen relation computation; however, this work does not support streaming data.…”

A stream processing abstraction framework

“…Although this version of SPAF does not include formal, integrated support for storing (globally or locally) intermediate results of individual transformation nodes, nothing prevents the programmer to implement access to storage resources external to the framework (e.g., files, DBs, object caches) in the transformation logic defined in the processing nodes, i.e., the programmer is responsible for implementing support for stateful computation. In this way, complex algorithms for analyzing multiple objects, like interval joins (Piatov et al, 2021), can be mapped to single processing nodes that are part of a more complex topology, performing other analyses (like image processing and/or event detection) on data streams (Persia et al, 2017). While a possible solution to overcome Assumption 4 will be described in Section 5, Assumption 5 can be partially solved by connecting multiple independent RAM 3 S applications by way of appropriate connectors; in this way, each logical topology would be mapped to a physical topology (defined automatically by, and peculiar of, the SPE chosen for each RAM 3 S application).…”