A NoSQL-based Data Management Infrastructure for Bridge Monitoring Database

Jeong, Seongwoon; Zhang, Yilan; Lynch, Jerome P.; Sohn, Hoon; Law, Kincho H.

doi:10.12783/shm2015/196

Cited by 5 publications

(4 citation statements)

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“…In addition, we use a single collection named repos to manage the sensor data. We take advantage of MongoDB's hierarchical data structure to categorize sensor data for ease of data retrieval (Jeong et al 2015b). The root node for a single data acquisition (DAQ), named daqevent, contains the timestamp of the DAQ event.…”

Section: Data Schema Descriptionmentioning

confidence: 99%

“…For this study, we employ an older version of MongoDB (version 2.0.6) since the onsite computers and controllers installed in some bridge monitoring and sensor network systems employ older versions of the Microsoft operating system and do not support a recent version (version 2.2 or higher) of the MongoDB system. Two interface programs written in Python are developed to automate the data flow (Jeong et al 2015b). The first program, named onsite.py, is in charge of sending new sensor data to the MongoDB's repository.…”

Section: Onsite Computermentioning

confidence: 99%

“…Since some data analysis modules require very expensive computational costs, the decentralized strategy helps the main server to be isolated from such operations and to maintain its performance as the central data repository. To automate data flow from Apache Cassandra database in the main server to the analysis software in the local computer, several interface programs written in Python are developed (Jeong et al 2015b). In addition to Cassandra Driver, we use MATLAB Engine (an interface between MATLAB and Python), scikit-learn (a tool for machine learning in Python), and rpy2 (an interface to R for Python process) to demonstrate a diverse set of data analysis platforms.…”

Section: Local Computermentioning

confidence: 99%

“…We utilize a Matlab-based Subspace Identification module (Overschee 2012) to extract modal properties from the acceleration data stored in the main server. The modal properties are computed on the local computer using the following steps (Jeong et al 2015b): (1) Retrieve the sensor IDs of all the accelerometers that operated during the defined period.…”

Section: Data Analysis Using Long Term Sensor Datamentioning

confidence: 99%

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A NoSQL data management infrastructure for bridge monitoring

Jeong

Zhang

O’Connor

et al. 2016

Smart Structures and Systems

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Advances in sensor technologies have led to the instrumentation of sensor networks for bridge monitoring and management. For a dense sensor network, enormous amount of sensor data are collected. The data need to be managed, processed, and interpreted. Data management issues are of prime importance for a bridge management system. This paper describes a data management infrastructure for bridge monitoring applications. Specifically, NoSQL database systems such as MongoDB and Apache Cassandra are employed to handle time-series data as well the unstructured bridge information model data. Standard XML-based modeling languages such as OpenBrIM and SensorML are adopted to manage semantically meaningful data and to support interoperability. Data interoperability and integration among different components of a bridge monitoring system that includes on-site computers, a central server, local computing platforms, and mobile devices are illustrated. The data management framework is demonstrated using the data collected from the wireless sensor network installed on the Telegraph Road Bridge,

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Section: Data Schema Descriptionmentioning

confidence: 99%

Section: Onsite Computermentioning

confidence: 99%

Section: Local Computermentioning

confidence: 99%

Section: Data Analysis Using Long Term Sensor Datamentioning

confidence: 99%

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