Date of Award
Summer 2014
Degree Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Engineering
First Advisor
John A. Springer
Committee Member 1
Eric J. Dietz
Committee Member 2
Eric T. Matson
Abstract
n the recent times, advances in scientific research related to electric vehicles led to generation of large amounts of data. This data is majorly logger data collected from various sensors in the vehicle. It is predominantly unstructured and non-relational in nature, also called Big Data. Analysis of such data needs a high performance information technology infrastructure that provides superior computational efficiency and storage capacity. It should be scalable to accommodate the growing data and ensure its security over a network. This research proposes an architecture built over Hadoop to effectively support distributed data management over a network for real-time data collection and storage, parallel processing, and faster random read access for information retrieval for decision-making.
Once imported into a database, the system can support efficient analysis and visualization of data as per user needs. These analytics can help understand correlations between data parameters under various circumstances. This system provides scalability to support data accumulation in the future and still perform analytics with less overhead. Overall, these open problems in EV data analytics are taken into consideration and a low-cost architecture for data management is researched.
Recommended Citation
Bolly, Vamshi Krishna, "Systems For Delivering Electric Vehicle Data Analytics" (2014). Open Access Theses. 406.
https://docs.lib.purdue.edu/open_access_theses/406