The emerging large datasets have made efficient data processing a much more difficult task for the traditional methodologies. Invariably, datasets continue to increase rapidly in size with time. The purpose of this research is to give an overview of some of the tools and techniques that can be utilized to manage and analyze large datasets. We propose a faster way to catalogue and retrieve data by creating a directory file—more specifically, an improved method that would allow file retrieval based on its time and date. This method eliminates the process of searching the entire content of files and reduces the time it takes to locate the selected data. We also implement the nearest search algorithm in an event where the searched query is not found. The algorithm sorts through data to find the closest points that are within close proximity to the searched query.
We also offer an efficient data reduction method that effectively condenses the amount of data. The algorithm enables users to store the desired amount of data in a file and decrease the time in which observations are retrieved for processing. This is achieved by using a reduced standard deviation range to minimize the original data and keeping the dataset to a significant smaller dataset size.
|Commitee:||Jackson, Lethia, Linares, Irving, Yan, Jie, Yang, Bo|
|School:||Bowie State University|
|School Location:||United States -- Maryland|
|Source:||DAI-B 74/08(E), Dissertation Abstracts International|
|Subjects:||Information Technology, Information science, Computer science|
|Keywords:||Algorithm sorts, Big data, Data reduction, Large complex data, Large datasets, Spacecraft data|
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