Approaching “Big Data” in Biological Research Imaging Spectroscopy with Novel Compression
| dc.contributor.author | Chen, Yixuan | |
| dc.contributor.examiningcommittee | Paliwal, Jitendra (Biosystems Engineering) Leung, Carson Kai-Sang (Computer Science) | en_US |
| dc.contributor.supervisor | Morrison, Jason (Biosystems Engineering) | en_US |
| dc.date.accessioned | 2014-04-10T16:28:08Z | |
| dc.date.available | 2014-04-10T16:28:08Z | |
| dc.date.issued | 2014-04-10 | |
| dc.degree.discipline | Biosystems Engineering | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.abstract | This research focuses on providing a fast and space efficient compression method to answer information queries on spectroscopic data. Our primary hypothesis was whether a conversion from decimal data to character/integer space could be done in a manner that enables use of succinct structures and provides good compression. This compression algorithm is motivated to handle queries on spectroscopic data that approaches limits of main computer memory. The primary hypothesis is supported in that the new compression method can save 79.20% - 94.07% computer space on the average. The average of maximum error rates is also acceptable, being 0.05% - 1.36% depending on the subject that the data was collected from. Additionally, the data’s compression rate and entropy are negatively correlated; while compression rate and maximum error were positively correlated when the max error rates were performed on a natural logarithm transformation. The effects of different types of data sources on compression rate have been studied as well. Fungus datasets achieved highest compression rates, while mouse brain datasets obtained the lowest compression rates among four types of data sources. Finally, the effect of the studied compression algorithm and method on integrating spectral bands has been investigated in this study. The spectral integration for determining lipid, CH2 and dense core plaque obtained good image quality and the errors can be considered inconsequential except the case of determining creatine deposits. Despite the fact that creatine deposits are still recognizable in the reconstructed image, the image quality was reduced. | en_US |
| dc.description.note | May 2014 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/23434 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | Image Compression | en_US |
| dc.subject | Big Data | en_US |
| dc.title | Approaching “Big Data” in Biological Research Imaging Spectroscopy with Novel Compression | en_US |
| dc.type | master thesis | en_US |