Vertical Data Structures and Computation of Sliding Window Averages in Two-Dimensional Data
A vertical-style data structure and operations on data in that structure are explored and tested in the domain of sliding window average algorithms for geographical information systems (GIS) data. The approach allows working with data of arbitrary precision, which is centrally important for very lar...
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North Dakota State University
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ndltd-ndsu.edu-oai-library.ndsu.edu-10365-318232021-09-28T17:11:34Z Vertical Data Structures and Computation of Sliding Window Averages in Two-Dimensional Data Helsene, Adam Paul aggregation binary vector gis image data sliding window vertical data A vertical-style data structure and operations on data in that structure are explored and tested in the domain of sliding window average algorithms for geographical information systems (GIS) data. The approach allows working with data of arbitrary precision, which is centrally important for very large GIS data sets. The novel data structure can be constructed from existing multi-channel image data, and data in the structure can be converted back to image data. While in the new structure, operations such as addition, division, and bit-level shifting can be performed in a parallelized manner. It is shown that the computation of averages for sliding windows on this data structure can be performed faster than using traditional computation techniques, and the approach scales to larger sliding window sizes. 2021-03-30T17:14:03Z 2021-03-30T17:14:03Z 2020 text/thesis https://hdl.handle.net/10365/31823 NDSU policy 190.6.2 https://www.ndsu.edu/fileadmin/policy/190.pdf application/pdf North Dakota State University |
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aggregation binary vector gis image data sliding window vertical data |
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aggregation binary vector gis image data sliding window vertical data Helsene, Adam Paul Vertical Data Structures and Computation of Sliding Window Averages in Two-Dimensional Data |
description |
A vertical-style data structure and operations on data in that structure are explored and tested in the domain of sliding window average algorithms for geographical information systems (GIS) data. The approach allows working with data of arbitrary precision, which is centrally important for very large GIS data sets.
The novel data structure can be constructed from existing multi-channel image data, and data in the structure can be converted back to image data. While in the new structure, operations such as addition, division, and bit-level shifting can be performed in a parallelized manner. It is shown that the computation of averages for sliding windows on this data structure can be performed faster than using traditional computation techniques, and the approach scales to larger sliding window sizes. |
author |
Helsene, Adam Paul |
author_facet |
Helsene, Adam Paul |
author_sort |
Helsene, Adam Paul |
title |
Vertical Data Structures and Computation of Sliding Window Averages in Two-Dimensional Data |
title_short |
Vertical Data Structures and Computation of Sliding Window Averages in Two-Dimensional Data |
title_full |
Vertical Data Structures and Computation of Sliding Window Averages in Two-Dimensional Data |
title_fullStr |
Vertical Data Structures and Computation of Sliding Window Averages in Two-Dimensional Data |
title_full_unstemmed |
Vertical Data Structures and Computation of Sliding Window Averages in Two-Dimensional Data |
title_sort |
vertical data structures and computation of sliding window averages in two-dimensional data |
publisher |
North Dakota State University |
publishDate |
2021 |
url |
https://hdl.handle.net/10365/31823 |
work_keys_str_mv |
AT helseneadampaul verticaldatastructuresandcomputationofslidingwindowaveragesintwodimensionaldata |
_version_ |
1719485795651289088 |