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. 2015 Dec 8:11:53.
doi: 10.1186/s13007-015-0097-z. eCollection 2015.

SensorDB: a virtual laboratory for the integration, visualization and analysis of varied biological sensor data

Ali Salehi  1 , Jose Jimenez-Berni  2 , David M Deery  2 , Doug Palmer  1 , Edward Holland  3 , Pablo Rozas-Larraondo  2 , Scott C Chapman  3 , Dimitrios Georgakopoulos  4 , Robert T Furbank  5
Affiliations

SensorDB: a virtual laboratory for the integration, visualization and analysis of varied biological sensor data

Ali Salehi et al. Plant Methods. .

Abstract

Background: To our knowledge, there is no software or database solution that supports large volumes of biological time series sensor data efficiently and enables data visualization and analysis in real time. Existing solutions for managing data typically use unstructured file systems or relational databases. These systems are not designed to provide instantaneous response to user queries. Furthermore, they do not support rapid data analysis and visualization to enable interactive experiments. In large scale experiments, this behaviour slows research discovery, discourages the widespread sharing and reuse of data that could otherwise inform critical decisions in a timely manner and encourage effective collaboration between groups.

Results: In this paper we present SensorDB, a web based virtual laboratory that can manage large volumes of biological time series sensor data while supporting rapid data queries and real-time user interaction. SensorDB is sensor agnostic and uses web-based, state-of-the-art cloud and storage technologies to efficiently gather, analyse and visualize data.

Conclusions: Collaboration and data sharing between different agencies and groups is thereby facilitated. SensorDB is available online at http://sensordb.csiro.au.

Keywords: Big data; High frequency data; NoSQL; Phenomics; Real-time statistics.

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Figures

Fig. 1
Fig. 1
SensorDB data model and data life cycle
Fig. 2
Fig. 2
A typical field experiment mapped to the SensorDB data model at the levels: experiment (long dash); node (short dash) and stream (solid)
Fig. 3
Fig. 3
High level architecture of SensorDB
Fig. 4
Fig. 4
SensorDB data processing model at the stream level
Fig. 5
Fig. 5
Screenshots of the SensorDB web interface (available at http://sensordb.csiro.au) highlighting key features
Fig. 6
Fig. 6
Benchmark of SensorDB, MySQL and PostgreSQL. Query times for different data sizes and aggregation levels using; a MySQL, b PostgreSQL and c SensorDB. Note that for the 1-year and 1-day aggregation levels, the query time for SensorDB was less than one second
Fig. 7
Fig. 7
Write benchmarks for varying numbers of client threads, experiments, nodes, streams and points inserted
Fig. 8
Fig. 8
Read benchmark for varying aggregation types—one thread
Fig. 9
Fig. 9
Read benchmarks for varying number of client threads, streams and request sizes
Fig. 10
Fig. 10
Write and Read Benchmarks for varying numbers of streams. Write Benchmark—one thread and experiment, 10 nodes. Read Benchmark—one experiment, 10 nodes, 4 threads
See this image and copyright information in PMC

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