Performance Overhead of Lossless Data Compression and Decompression Algorithms: A Qualitative Fundamental Research Study
With the development of big data, the Internet of Things (IoT), and social media, the volume of data is being collected and stored exponentially. Some of the influx of the huge data storage also comes from sensors in automobiles, household appliances, medical equipment, and many other devices. Today...
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| Médium: | Dissertation |
| Jazyk: | angličtina |
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ProQuest Dissertations & Theses
01.01.2022
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| ISBN: | 9798837540622 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | With the development of big data, the Internet of Things (IoT), and social media, the volume of data is being collected and stored exponentially. Some of the influx of the huge data storage also comes from sensors in automobiles, household appliances, medical equipment, and many other devices. Today data collection is outgrowing the capability to store this vast amount of data. Therefore, there is a need to compress data to alleviate this data storage problem. Several different data compression algorithms use different methods of data compression. For example, Run Length Encoding (RLE) removes consecutive repeating strings and combines them by coding the string once with the number of occurrences. Several data compression algorithms, such as Huffman coding, rely on a dictionary representing characters or a string of characters with codewords that contains fewer bits. Unfortunately, these dictionaries are unique to a document, so the metadata must be passed to the decoder for decompression. This research develops a compression and decompression model that eliminates the overhead of the encoder having to create and maintain a dictionary and eliminates passing this metadata to the decoder. Compression is achieved by reducing the number of bits for each alphanumeric character from 8 bits to 6 bits. This compression method comes with some overhead in compressing non-alphanumeric characters; however, non-alphanumeric characters are used infrequently. This document shows a lossless 6-bit data compression model that provides up to 25% data compression. This data compression model could save businesses the cost of investing in data servers and cloud data storage. |
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| Bibliografie: | SourceType-Dissertations & Theses-1 ObjectType-Dissertation/Thesis-1 content type line 12 |
| ISBN: | 9798837540622 |