Abstract
Large amounts of data (“big data”) are readily available and collected daily by global networks worldwide. However, much of the real-time utility of this data is not realized, as data analysis tools for very large datasets, particularly time series data are cumbersome. A methodology for data cleaning and preparation needed to support big data analysis is presented, along with a comparative examination of three widely available data mining tools. This methodology and offered tools are used for analysis of a large-scale time series dataset of environmental data. The case study of environmental data analysis is presented as visualization, providing future direction for data mining on massive data sets gathered from global networks, and an illustration of the use of big data technology for predictive data modeling and assessment.
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Keywords
- Wireless Sensor Network
- Association Rule
- Time Series Data
- Environmental Sustainability
- Exploratory Spatial Data Analysis
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Morreale, P., Goncalves, A., Silva, C. (2015). Analysis and Visualization of Large-Scale Time Series Network Data. In: Xhafa, F., Barolli, L., Barolli, A., Papajorgji, P. (eds) Modeling and Processing for Next-Generation Big-Data Technologies. Modeling and Optimization in Science and Technologies, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-09177-8_9
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DOI: https://doi.org/10.1007/978-3-319-09177-8_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09176-1
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