Mining knowledge graphs to map heterogeneous relations between the internet of things patterns

Abstract

<span lang="EN-US">Patterns for the internet of things (IoT) which represent proven solutions used to solve design problems in the IoT are numerous. Similar to object-oriented design patterns, these IoT patterns contain multiple mutual heterogeneous relationships. However, these pattern relationships are hidden and virtually unidentified in most documents. In this paper, we use machine learning techniques to automatically mine knowledge graphs to map these relationships between several IoT patterns. The end result is a semantic knowledge graph database which outlines patterns as vertices and their relations as edges. We have identified four main relationships between the IoT patterns-a pattern is similar to another pattern if it addresses the same use case problem, a large-scale pattern uses a small- scale pattern in a lower level layer, a large pattern is composed of multiple smaller scale patterns underneath it, and patterns complement and combine with each other to resolve a given use case problem. Our results show some promising prospects towards the use of machine learning techniques to generate an automated repository to organise the IoT patterns, which are usually extracted at various levels of abstraction and granularity.</span>

Insights on the internet of things: past, present, and future directions

“…Before transmission, data must be filtered and compressed to an ideal size. 3) Edge IT data processing [67], [94], [100], [109], [122], [133], [140], [168]- [170], [216], [220]- [223] Before reaching the cloud center, the digitized and aggregated IoT data is processed further. Edge devices do sophisticated analytics and preprocessing, which may include machine learning and visual representation.…”