Learning Semantic Association Rules from Internet of Things Data

TL;DR

This paper introduces a novel association rule mining pipeline for IoT data that combines static metadata and dynamic sensor data, utilizing an autoencoder-based method to generate concise, high-quality rules suitable for IoT applications.

Contribution

It presents a new ARM pipeline for IoT that leverages knowledge graphs and introduces the Aerial autoencoder-based method for efficient rule extraction.

Findings

Aerial produces more concise rules than existing methods.

The pipeline achieves full dataset coverage with high-quality rules.

Method is validated on multiple IoT datasets across domains.

Abstract

Association Rule Mining (ARM) is the task of discovering commonalities in data in the form of logical implications. ARM is used in the Internet of Things (IoT) for different tasks including monitoring and decision-making. However, existing methods give limited consideration to IoT-specific requirements such as heterogeneity and volume. Furthermore, they do not utilize important static domain-specific description data about IoT systems, which is increasingly represented as knowledge graphs. In this paper, we propose a novel ARM pipeline for IoT data that utilizes both dynamic sensor data and static IoT system metadata. Furthermore, we propose an Autoencoder-based Neurosymbolic ARM method (Aerial) as part of the pipeline to address the high volume of IoT data and reduce the total number of rules that are resource-intensive to process. Aerial learns a neural representation of a given data and extracts association rules from this representation by exploiting the reconstruction (decoding) mechanism of an autoencoder. Extensive evaluations on 3 IoT datasets from 2 domains show that ARM on both static and dynamic IoT data results in more generically applicable rules while Aerial can learn a more concise set of high-quality association rules than the state-of-the-art with full coverage over the datasets.