Analyzing Environmental Data with the Titan Platform

Tsatia Tsida, Cedric (2020) Analyzing Environmental Data with the Titan Platform (Master thesis), Kiel University, Kiel, 84 pp

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Abstract

The natural environment is one of our biggest treasures — if not our biggest treasure — and it must be preserved. Therefore, understanding our natural environment is becoming increasingly important as we struggle to respond to the implications of climate change and new diseases. Climate change is the greatest challenge faced by almost every species. With the help of environmental data, we can understand most of the complex interrelationships present in the climate and consequently take preventive measures. However, with the rise of new technologies, the collection of environmental data has become very rapid and complex, and therefore, it is difficult or even impossible to process them using traditional methods. To overcome this problem, it is important to extract pieces of information from the data as soon as they are produced. We used Titan, a software that applies stream processing, to analyze the environmental data collected across Germany.
Titan comes with a set of solutions that facilitate the processing of huge amounts of data and allow software components to consume and supply data. In this work, three data sources have been connected directly to the Titan platform to collect diverse types of data. Once these connections were established, we analyzed the incoming data on the Titan platform using a set of data flows that consist of modular components connected to each other. Each modular component is responsible for a single task, such as data collection, data filtering, or data transformation. Moreover, we connect an external database to the platform to save the results of our analysis. Finally, we used a visualization tool to, create a dashboard for each flow implemented on the platform.
We evaluate the scalability of the Titan platform based on the number of incoming records. To do so, we continuously increase the amount of workload into the Titan platform and determine the minimum number of component instances that are necessary to process data without reaching high traffic of data across the platform.

Document Type: Thesis (Master thesis)
Thesis Advisors: Hasselbring, Wilhelm and Henning, Sören
Keywords: Environmental Data, Big Data, Scalability, Titan, Flow-based Programming
Research affiliation: Kiel University > Software Engineering
Kiel University
Related URLs:
Projects: TITAN
Date Deposited: 17 Nov 2020 17:27
Last Modified: 17 Nov 2020 17:32
URI: http://eprints.uni-kiel.de/id/eprint/51036

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