Abstract Summary
This paper evaluates several data models for high-volume time-series bridge sensor data. Remote sensor technologies are becoming increasingly more reliable and affordable for ensuring safety and guaranteeing appropriate timing for bridge maintenance. Data returned from these sensors tends to be of a very high volume, with modern sensors returning hundreds of readings per second. When working with such a large volume of data, concerns arise with how efficiently the data can be ingested and retrieved. Inefficient data ingestion can cause the data transportation system to be overwhelmed and incorrect or incomplete data to be pushed to the database. Inefficient retrieval will limit the ability of bridge stakeholders to make real-time data-driven decisions about the safety of bridges. Data structure becomes a critical part of ensuring that these two operations can be performed as fast as possible with the technology available. This paper reviews the top current technologies for managing both structured and semi-structured data through the lens of high-volume time-series data from bridge sensors. We propose several potential structured and semi-structured data models for bridge sensor data and implement them with the appropriate technologies for each. The proposed data models are tested using data collected from actual bridges. We conclude by recommending a semi-structured data model that allows for data to be both more easily collected and ingested, without making any concessions in terms of the speed at which the data can be retrieved for data visualizations and analysis.
