Learning Semantic Association Rules from Internet of Things Data
This addresses the need for more efficient and effective association rule mining in IoT systems, which is incremental as it builds on existing methods by incorporating metadata and neural representations.
The paper tackled the problem of association rule mining in IoT data by proposing a pipeline that uses both dynamic sensor data and static metadata, and an autoencoder-based method called Aerial to handle high volume and reduce rules; evaluations on 3 IoT datasets showed it produces more generically applicable rules and a more concise set of high-quality rules than state-of-the-art methods with full coverage.
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.