融合传感器数据与系统元数据，用神经符号方法挖掘更通用、更简洁的物联网关联规则
Learning Semantic Association Rules from Internet of Things Data
- 结合动态传感数据与静态系统元数据，构建面向物联网的关联规则挖掘流程
- 提出Aerial方法，通过自编码器压缩数据并提取高质量规则，规则数量减少30%以上
- 适合需要低资源消耗、高泛化能力的物联网监控与决策场景
关联规则挖掘（ARM）旨在以逻辑蕴含形式发现数据中的共性，广泛应用于物联网（IoT）的监控与决策。然而，现有方法对物联网特有的异构性与海量数据考虑不足，且未利用日益丰富的领域特定描述数据，如知识图谱。本文提出一种新型物联网关联规则挖掘流程，同时利用动态传感器数据与静态系统元数据。进一步提出基于自编码器的神经符号关联规则挖掘方法（Aerial），以应对物联网数据量大带来的挑战，并减少需处理的规则总数。Aerial通过自编码器的重构机制学习数据的神经表示，并从中提取关联规则。在两个领域共3个物联网数据集上的大量实验表明，结合静态与动态数据可生成更具泛化性的规则；Aerial相比当前最优方法，在保持数据集全覆盖的前提下，能学习到更精炼、高质量的规则集。
原文摘要 · Abstract (English)
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.
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