INTEGRATIVE DESIGN AND PROCESSING OF ENVIRONMENTAL IOT AND MICROSCOPY DATA VISUALIZATION

Jain, Ribhav

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https://hdl.handle.net/2142/124950 Copy

Description

Title

INTEGRATIVE DESIGN AND PROCESSING OF ENVIRONMENTAL IOT AND MICROSCOPY DATA VISUALIZATION

Author(s)

Jain, Ribhav

Issue Date

2021-05-01

Keyword(s)

distributed systems; cloud; IoT; visualization; databases

Abstract

This thesis discusses the design and integration between the Clowder/4CeeD and Senselet frameworks. It speaks about the fusion and correlation of data in the MongoDB/Clowder system from the Senselet database InfluxDB in a secure and coordinated manner. Also, the thesis delves into research and study of Clowder/4CeeD and Senselet systems in addition to other similar data storage systems and techniques. These systems accelerate making scientific discoveries by allowing researchers to curate, store, and analyze their experimental data. The thesis will also delve into the technical details, architecture, and design of these systems in great detail while discussing and analyzing their implementation's various pros and cons. 4CeeD makes use of MongoDB for its heterogeneous data; meanwhile, Senselet uses InfluxDB to store time-series data. These cloud systems' advantages include fault-tolerance, availability, and efficient processing at scale, enabling them to revolutionize and improve the process of making scientific discoveries. The time-series data stored in InfluxDB in the Senselet system contains environmental data obtained from external sensors in laboratories used by scientists for experiments. 4CeeD, on the other hand, stores experimental microscopy data in MongoDB, which scientists wish to correlate and compare with the previously mentioned environmental data. Together the two platforms form a sensory network architecture with a cloud backend that allows researchers to retain and correlate their data with ease in real-time. This thesis goes into depth about the function and interaction between the two described systems, followed by the design discussion of a fusion framework developed to correlate the two. The framework allows scientists to easily correlate data between the two systems by visualizing the Senselet data stored in InfluxDB inside the Clowder platform. This enables researchers to study and analyze data from one platform within another. Also, researchers can extract and migrate data from InfluxDB (Senselet) to MongoDB (Clowder), allowing them to store environmental information with experimental data. Thus, this fusion framework allows researchers to visualize, correlate and migrate data seamlessly between platforms enabling them to improve and streamline their research process.

Type of Resource

text

Language

eng

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