AN EFFICIENT GRAPH MODELING APPROACH FOR STORING AND ANALYZING HETEROGENEOUS IOT DATA
Abstract
In an Internet of Thing (IoT) environment, entities with different attributes and capacities are going to be connected in a highly connected fashion. Specifically, not only the mechanical and electronic devices but also other entities such as people, locations, and applications are connected to each other. Most IoT applications must work with dynamic and speedily changing systems due to new entities are coming online and/or the connection between these entities can change regularly. This requires a data model that enables to easily represent the entities and support adding, deleting, and updating relations between entities without impacting application availability. Fortunately, graph databases are purposely-built to store highly connected data with nodes representing entities and edges representing relationships between these entities. In this paper, we propose a general graph model that can be used to design graph databases in order to support effectively storing and analyzing IoT data. We represent IoT data based on a graph model and consider smart building data management as a case study. Through the analysis and comparison of experimental results in various aspects, we find that our graph modeling approach is applicable for storing and analyzing the IoT connected data.
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