Zobrazit v EDS

Constructing a Lightweight Key-Value Store Based on the Windows Native Features.

Uloženo v:
Podrobná bibliografie
Název: Constructing a Lightweight Key-Value Store Based on the Windows Native Features.
Autoři: Kwon, Hyuk-Yoon
Zdroj: Applied Sciences (2076-3417); Sep2019, Vol. 9 Issue 18, p3801, 22p
Témata: WINDOWS (Graphical user interfaces), BIG data, RETAIL stores, CLINICAL trial registries
Abstrakt: Featured Application: In this paper, we propose a lightweight key-value store for managing various types of data, which are generated from Big data applications, in a very simple form. The proposed technique can be used in any environments where Windows operating systems are running, which encompass from client environments (e.g., Windows 10) to server environments (e.g., Windows Server 2016), with the minimum effort for the installation. For the other environments without Windows operating systems, we can easily migrate data to the other any environments that support existing key-value stores by using the ETL (Extract-Transform-Load) method proposed by this paper. In this paper, we propose a method to construct a lightweight key-value store based on the Windows native features. The main idea is providing a thin wrapper for the key-value store on top of a built-in storage in Windows, called Windows registry. First, we define a mapping of the components in the key-value store onto the components in the Windows registry. Then, we present a hash-based multi-level registry index so as to distribute the key-value data balanced and to efficiently access them. Third, we implement basic operations of the key-value store (i.e., Get, Put, and Delete) by manipulating the Windows registry using the Windows native APIs. We call the proposed key-value store WR-Store. Finally, we propose an efficient ETL (Extract-Transform-Load) method to migrate data stored in WR-Store into any other environments that support existing key-value stores. Because the performance of the Windows registry has not been studied much, we perform the empirical study to understand the characteristics of WR-Store, and then, tune the performance of WR-Store to find the best parameter setting. Through extensive experiments using synthetic and real data sets, we show that the performance of WR-Store is comparable to or even better than the state-of-the-art systems (i.e., RocksDB, BerkeleyDB, and LevelDB). Especially, we show the scalability of WR-Store. That is, WR-Store becomes much more efficient than the other key-value stores as the size of data set increases. In addition, we show that the performance of WR-Store is maintained even in the case of intensive registry workloads where 1000 processes accessing to the registry actively are concurrently running. [ABSTRACT FROM AUTHOR]
Copyright of Applied Sciences (2076-3417) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Databáze: Complementary Index
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://resolver.ebscohost.com/openurl?sid=EBSCO:edb&genre=article&issn=20763417&ISBN=&volume=9&issue=18&date=20190915&spage=3801&pages=3801-3822&title=Applied Sciences (2076-3417)&atitle=Constructing%20a%20Lightweight%20Key-Value%20Store%20Based%20on%20the%20Windows%20Native%20Features.&aulast=Kwon%2C%20Hyuk-Yoon&id=DOI:10.3390/app9183801
    Name: Full Text Finder
    Category: fullText
    Text: Full Text Finder
    Icon: https://imageserver.ebscohost.com/branding/images/FTF.gif
    MouseOverText: Full Text Finder
  – Url: https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=EBSCO&SrcAuth=EBSCO&DestApp=WOS&ServiceName=TransferToWoS&DestLinkType=GeneralSearchSummary&Func=Links&author=Kwon%20H
    Name: ISI
    Category: fullText
    Text: Nájsť tento článok vo Web of Science
    Icon: https://imagesrvr.epnet.com/ls/20docs.gif
    MouseOverText: Nájsť tento článok vo Web of Science
Header DbId: edb
DbLabel: Complementary Index
An: 139050153
RelevancyScore: 886
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 885.552368164063
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Constructing a Lightweight Key-Value Store Based on the Windows Native Features.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Kwon%2C+Hyuk-Yoon%22">Kwon, Hyuk-Yoon</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: Applied Sciences (2076-3417); Sep2019, Vol. 9 Issue 18, p3801, 22p
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22WINDOWS+%28Graphical+user+interfaces%29%22">WINDOWS (Graphical user interfaces)</searchLink><br /><searchLink fieldCode="DE" term="%22BIG+data%22">BIG data</searchLink><br /><searchLink fieldCode="DE" term="%22RETAIL+stores%22">RETAIL stores</searchLink><br /><searchLink fieldCode="DE" term="%22CLINICAL+trial+registries%22">CLINICAL trial registries</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Featured Application: In this paper, we propose a lightweight key-value store for managing various types of data, which are generated from Big data applications, in a very simple form. The proposed technique can be used in any environments where Windows operating systems are running, which encompass from client environments (e.g., Windows 10) to server environments (e.g., Windows Server 2016), with the minimum effort for the installation. For the other environments without Windows operating systems, we can easily migrate data to the other any environments that support existing key-value stores by using the ETL (Extract-Transform-Load) method proposed by this paper. In this paper, we propose a method to construct a lightweight key-value store based on the Windows native features. The main idea is providing a thin wrapper for the key-value store on top of a built-in storage in Windows, called Windows registry. First, we define a mapping of the components in the key-value store onto the components in the Windows registry. Then, we present a hash-based multi-level registry index so as to distribute the key-value data balanced and to efficiently access them. Third, we implement basic operations of the key-value store (i.e., Get, Put, and Delete) by manipulating the Windows registry using the Windows native APIs. We call the proposed key-value store WR-Store. Finally, we propose an efficient ETL (Extract-Transform-Load) method to migrate data stored in WR-Store into any other environments that support existing key-value stores. Because the performance of the Windows registry has not been studied much, we perform the empirical study to understand the characteristics of WR-Store, and then, tune the performance of WR-Store to find the best parameter setting. Through extensive experiments using synthetic and real data sets, we show that the performance of WR-Store is comparable to or even better than the state-of-the-art systems (i.e., RocksDB, BerkeleyDB, and LevelDB). Especially, we show the scalability of WR-Store. That is, WR-Store becomes much more efficient than the other key-value stores as the size of data set increases. In addition, we show that the performance of WR-Store is maintained even in the case of intensive registry workloads where 1000 processes accessing to the registry actively are concurrently running. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Applied Sciences (2076-3417) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://erproxy.cvtisr.sk/sfx/access?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=139050153
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/app9183801
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 22
        StartPage: 3801
    Subjects:
      – SubjectFull: WINDOWS (Graphical user interfaces)
        Type: general
      – SubjectFull: BIG data
        Type: general
      – SubjectFull: RETAIL stores
        Type: general
      – SubjectFull: CLINICAL trial registries
        Type: general
    Titles:
      – TitleFull: Constructing a Lightweight Key-Value Store Based on the Windows Native Features.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Kwon, Hyuk-Yoon
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 15
              M: 09
              Text: Sep2019
              Type: published
              Y: 2019
          Identifiers:
            – Type: issn-print
              Value: 20763417
          Numbering:
            – Type: volume
              Value: 9
            – Type: issue
              Value: 18
          Titles:
            – TitleFull: Applied Sciences (2076-3417)
              Type: main
ResultId 1