Event Trend Aggregation Under Rich Event Matching Semantics

TL;DR

Cogra is a system that efficiently aggregates event trends under complex matching semantics by maintaining coarser aggregates, significantly reducing time and memory costs in streaming applications.

Contribution

Cogra supports diverse event matching semantics and incrementally maintains coarser aggregates, improving efficiency over existing systems.

Findings

Achieves up to 10,000x speed-up

Reduces memory usage by up to 100 million times

Supports rich event matching semantics

Abstract

Streaming applications from health care analytics to algorithmic trading deploy Kleene queries to detect and aggregate event trends. Rich event matching semantics determine how to compose events into trends. The expressive power of state-of-the-art systems remains limited in that they do not support the rich variety of these semantics. Worse yet, they suffer from long delays and high memory costs because they opt to maintain aggregates at a fine granularity. To overcome these limitations, our Coarse-Grained Event Trend Aggregation (Cogra) approach supports this rich diversity of event matching semantics within one system. Better yet, Cogra incrementally maintains aggregates at the coarsest granularity possible for each of these semantics. In this way, Cogra minimizes the number of aggregates -- reducing both time and space complexity. Our experiments demonstrate that Cogra achieves up to four orders of magnitude speed-up and up to eight orders of magnitude memory reduction compared to state-of-the-art approaches.