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ECA Rules in EmbedDB : Enabling Lightweight, Event-Driven Decision Making for Resource-Constrained Devices Richards, MacKenzie E.

Abstract

Resource-constrained Internet of Things (IoT) and edge devices require autonomous, real-time decision-making capabilities without relying on cloud systems or energy-intensive polling. This thesis introduces event-condition-action (ECA) rule support in EmbedDB, transforming it into an active database system that executes reactive queries with minimal latency and memory overhead. We present a lightweight API for defining ECA rules that trigger on data insertion, enabling devices to evaluate conditions (e.g., sliding-window aggregates) and execute actions (e.g., alerts or actuation) entirely on-device. Our implementation maintains EmbedDB’s efficiency, operating in under 4 KB of RAM, while adding active capabilities. Benchmarks show that EmbedDB processes ECA rules in 49µs per insert, outperforming InfluxDB/Kapacitor by over 200 times in end-to-end latency.

Item Metadata

Title
ECA Rules in EmbedDB : Enabling Lightweight, Event-Driven Decision Making for Resource-Constrained Devices
Creator
Richards, MacKenzie E.
Date Issued
2025-04
Description
Resource-constrained Internet of Things (IoT) and edge devices require autonomous, real-time decision-making capabilities without relying on cloud systems or energy-intensive polling. This thesis introduces event-condition-action (ECA) rule support in EmbedDB, transforming it into an active database system that executes reactive queries with minimal latency and memory overhead. We present a lightweight API for defining ECA rules that trigger on data insertion, enabling devices to evaluate conditions (e.g., sliding-window aggregates) and execute actions (e.g., alerts or actuation) entirely on-device. Our implementation maintains EmbedDB’s efficiency, operating in under 4 KB of RAM, while adding active capabilities. Benchmarks show that EmbedDB processes ECA rules in 49µs per insert, outperforming InfluxDB/Kapacitor by over 200 times in end-to-end latency.
Genre
Graduating Project
Type
Text
Language
eng
Series
University of British Columbia. COSC_O 449
Date Available
2025-05-08
Provider
Vancouver : University of British Columbia Library
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
DOI
10.14288/1.0448796
URI
http://hdl.handle.net/2429/91060
Affiliation
Science, Irving K. Barber Faculty of (Okanagan); Computer Science, Mathematics, Physics and Statistics, Department of (Okanagan)
Peer Review Status
Unreviewed
Scholarly Level
Undergraduate
Rights URI
http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository
DSpace

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Attribution-NonCommercial-NoDerivatives 4.0 International