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...
Uloženo v:
| Hlavní autor: | |
|---|---|
| Médium: | Dissertation |
| Jazyk: | angličtina |
| Vydáno: |
ProQuest Dissertations & Theses
01.01.2022
|
| Témata: | |
| ISBN: | 9798837540622 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | 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. |
|---|---|
| AbstractList | 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. |
| Author | Williams, David Michael |
| Author_xml | – sequence: 1 givenname: David surname: Williams middlename: Michael fullname: Williams, David Michael |
| BookMark | eNpNjc1KAzEYRQMqqLXvEHBdyOR34q60VoWCf92XbyZf2pGZpCaZom9vQReuLucszr0m5yEGPCNTa2xdC6Mk05xfkmnOXcMYs0Iwya_I1wsmH9MAoUX6fMS0R3A0erqOOfeYM11CAbqIwyGdqIuBQnB0ie0_M-93MXVlP-Q7OqevI_RdgdIdka7G4GDAUKCnb5gRUrun72V03zfkwkOfcfq3E7JZ3W8Wj7P188PTYr6e7TQXMwvKVUwKrqxUyOvKaSN5g8KA0-CgcaKtZQu1qnyj0VjQVisljTfWO8_EhNz-Zg8pfo6Yy_YjjimcHrfcMFVXgnEhfgAPJFxl |
| ContentType | Dissertation |
| Copyright | Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works. |
| Copyright_xml | – notice: Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works. |
| DBID | 04Z 050 053 054 0BH 0IZ AAFGM ABUWG ADZZV AFKRA AGAJT AMEAF AQTIP AZQEC BENPR CBPLH CCPQU DWQXO EU9 G20 M8- P6D PHGZM PHGZT PIMPY PKEHL PQCXX PQEST PQQKQ PQUKI PRINS |
| DatabaseName | Dissertations & Theses Europe Full Text: Business Dissertations & Theses Europe Full Text: Health & Medicine Dissertations & Theses Europe Full Text: Science & Technology Dissertations & Theses Europe Full Text: Social Sciences ProQuest Dissertations and Theses Professional Dissertations & Theses @ Northcentral University ProQuest Central Korea - hybrid linking ProQuest Central (Alumni) ProQuest Central (Alumni) - hybrid linking ProQuest Central UK/Ireland ProQuest Central Essentials - hybrid linking ProQuest Dissertations & Theses Global: The Humanities and Social Sciences Collection ProQuest Women's & Gender Studies - hybrid linking ProQuest Central Essentials ProQuest Central ProQuest Dissertations & Theses Global: The Sciences and Engineering Collection ProQuest One ProQuest Central Korea ProQuest Dissertations & Theses A&I ProQuest Dissertations & Theses Global ProQuest Dissertations and Theses A&I: The Sciences and Engineering Collection ProQuest Dissertations and Theses A&I: The Humanities and Social Sciences Collection ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central - hybrid linking ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China |
| DatabaseTitle | Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest Dissertations & Theses: Open ProQuest Central China ProQuest Dissertations and Theses A&I: The Sciences and Engineering Collection ProQuest Central ProQuest Central Korea Dissertations & Theses Europe Full Text: Business ProQuest Central (New) Dissertations & Theses Europe Full Text: Health & Medicine ProQuest Dissertations & Theses Global: The Humanities and Social Sciences Collection ProQuest One Academic Eastern Edition ProQuest Dissertations & Theses Global: The Sciences and Engineering Collection ProQuest Dissertations and Theses Professional ProQuest Dissertations & Theses Global Dissertations & Theses Europe Full Text: Science & Technology Dissertations & Theses Europe Full Text: Social Sciences ProQuest One Academic UKI Edition ProQuest Dissertations and Theses A&I: The Humanities and Social Sciences Collection Dissertations & Theses @ Northcentral University ProQuest One Academic ProQuest Dissertations & Theses A&I ProQuest One Academic (New) |
| DatabaseTitleList | Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: PIMPY name: ProQuest Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| Genre | Dissertation/Thesis |
| GroupedDBID | 04Z 050 053 054 0BH 0IZ 8R4 8R5 ABUWG AFKRA AMEAF AZQEC BENPR CBPLH CCPQU DWQXO EU9 G20 M8- P6D PHGZM PHGZT PIMPY PKEHL PQEST PQQKQ PQUKI PRINS Q2X |
| ID | FETCH-LOGICAL-g623-9a5d104325945e281d6742be37ad6adabd3c84ca851fb6e79a6965547f79fdf03 |
| IEDL.DBID | PIMPY |
| ISBN | 9798837540622 |
| IngestDate | Mon Jun 30 02:33:54 EDT 2025 |
| IsOpenAccess | true |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-g623-9a5d104325945e281d6742be37ad6adabd3c84ca851fb6e79a6965547f79fdf03 |
| Notes | SourceType-Dissertations & Theses-1 ObjectType-Dissertation/Thesis-1 content type line 12 |
| OpenAccessLink | https://www.proquest.com/publiccontent/docview/2705813023?pq-origsite=%requestingapplication% |
| PQID | 2705813023 |
| PQPubID | 18750 |
| ParticipantIDs | proquest_journals_2705813023 |
| PublicationCentury | 2000 |
| PublicationDate | 20220101 |
| PublicationDateYYYYMMDD | 2022-01-01 |
| PublicationDate_xml | – month: 01 year: 2022 text: 20220101 day: 01 |
| PublicationDecade | 2020 |
| PublicationYear | 2022 |
| Publisher | ProQuest Dissertations & Theses |
| Publisher_xml | – name: ProQuest Dissertations & Theses |
| SSID | ssib000933042 |
| Score | 1.933503 |
| Snippet | 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... |
| SourceID | proquest |
| SourceType | Aggregation Database |
| SubjectTerms | Applied Mathematics Computer Engineering Computer science Information science Information Technology |
| Title | Performance Overhead of Lossless Data Compression and Decompression Algorithms: A Qualitative Fundamental Research Study |
| URI | https://www.proquest.com/docview/2705813023 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LSwMxEB5s60EU6hMfteTgdek-k40XqbZVodZFeqinks2jCnWr3VX035vsbmlR8ORxs4SEkHyZycx8H8CZskPGiXAsxhxXOyjSt_S3smLfdZUnQylUrlrSJ4NBOBrRqCyPTsu0ygUm5kBdsD2bvG0Nwi0x4-bFvOUSOwhNzM27eH2zjIaUibWWghoVqBniLbsKtej2Lnr87b1vUUPTZdRfbWxkc3_AcH639Or_O6tt2OyshNh3YE0mu1BfiDeg8izvwWe0LBlA93pDa1QWaKZQX1-bU41_qMMyhkzHIlU2QSwRqCP5Skt7OtHjZ08v6Tlqo4KPI2cSRz1TYVIIB6BFdh8ySYtf-zDsdYdXN1Ypw2BNtG1kURYIxxD3BdQPpKvtW6zd6Vh6hAnMBIuFx0OfM226qRhLQhmmWBspRBGqhLK9A6gms0QeAlLc1w3apqCS-NiW1AkUFir2HG6I4YIjaCxWeVwepXS8XNTjv3-fwIZrahPy95EGVLP5uzyFdf6RPafzJtQuu4PooQmVa9duljvjG1IjzN4 |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LT8JAEJ4gmGg0wWd8oO5Bj419t2tiDLESCAU5cMAT2XZ30QSLAj74Uf5HZwsNRBNvHDx2mzaZzOw3M7sz8wGcS91nsccNjTHDxARF2Bo-Sy2yTVNawhdcpqwlodds-p0ObeXgK-uFUWWVGSamQM0HsTojvzQ93fHVLZt18_KqKdYodbuaUWhMzaIuJh-Yso2uawHq98I0K3ft26o2YxXQeujqNcocbqg5dA61HWFiuOZidhgJy2PcZZxF3Ip9O2YYicjIFR5lLnXR53rSo5JL3cLfrkDBRlvX81Bo1Rqth9_HA5tUzQFT9LK6q3h5f-B86rwqxX8m9hZsBAtFAtuQE8kOFDP6CTJDo134bM2bHsg9bkn0K5wMJAnR8fcRwUnAxoyoD6fFvglhCSeBiBdWyv0eijt-fB5dkTKZThRJZ6GTiuqRmVIfkKw-kaiyy8ketJch-z7kk0EiDoDI2MYFjIqo8GxXF9RwpMtlZBmxGm3nHEIpU2N3Bgaj7lyHR3-_PoO1arsRdsNas34M66bqtEhPe0qQHw_fxAmsxu_jp9HwdGZ4BLpL1vk3FlcaoA |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LS8NAEB5qKyIKvvFRdQ96DM17s4JINS2WllrEg7eyye5Woaba1kd_mv_O2TTBouCtB4_ZkMBmNt_M7H4zH8CJMgMeU2EZnFs2JijSNfBaGZFr28qRgRQqVS1p0XY7uL9nnQJ85rUwmlaZY2IK1GIQ6z3yik1NL9CnbE5FZbSITli_eH4xtIKUPmnN5TSmS6QpJ--Yvo3OGyHa-tS267W7q2sjUxgweuj2DcY9YemedB5zPWlj6OZjphhJh3Lhc8Ej4cSBG3OMSlTkS8q4z3z0v1RRpoQyHXztApSogzlPEUqXtXbn9vdWwSrTPcG01Kzpa43eH5ifOrL62j_-BOuwEs6QBzagIJNNWMtlKUiGUlvw0fkuhiA3-KuivxFkoEgLA4I-IjsJ-ZgT_eCUBJwQnggSynhmpNrv4XTHD0-jM1Il004jaY90Ute1M1NJBJLzFommY0624W4ec9-BYjJI5C4QFbs4gNESk9T1TcksT_lCRY4V65Z33h6Uc5N2M5AYdb_tuf_37WNYQkN3W4128wCWbV2AkW4ClaE4Hr7KQ1iM38aPo-FRtgYJdOds8i_QcyM6 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adissertation&rft.genre=dissertation&rft.title=Performance+Overhead+of+Lossless+Data+Compression+and+Decompression+Algorithms%3A+A+Qualitative+Fundamental+Research+Study&rft.DBID=04Z%3B050%3B053%3B054%3B0BH%3B0IZ%3BAAFGM%3BABUWG%3BADZZV%3BAFKRA%3BAGAJT%3BAMEAF%3BAQTIP%3BAZQEC%3BBENPR%3BCBPLH%3BCCPQU%3BDWQXO%3BEU9%3BG20%3BM8-%3BP6D%3BPHGZM%3BPHGZT%3BPIMPY%3BPKEHL%3BPQCXX%3BPQEST%3BPQQKQ%3BPQUKI%3BPRINS&rft.PQPubID=18750&rft.au=Williams%2C+David+Michael&rft.date=2022-01-01&rft.pub=ProQuest+Dissertations+%26+Theses&rft.isbn=9798837540622&rft.externalDBID=HAS_PDF_LINK |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9798837540622/lc.gif&client=summon&freeimage=true |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9798837540622/mc.gif&client=summon&freeimage=true |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9798837540622/sc.gif&client=summon&freeimage=true |