Self-adapting data migration in the context of schema evolution in NoSQL databases
When NoSQL database systems are used in an agile software development setting, data model changes occur frequently and thus, data is routinely stored in different versions. The management of versioned data leads to an overhead potentially impeding the software development. Several data migration str...
Gespeichert in:
| Veröffentlicht in: | Distributed and parallel databases : an international journal 2022-03, Vol.40 (1), p.5-25 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 25 |
|---|---|
| container_issue | 1 |
| container_start_page | 5 |
| container_title | Distributed and parallel databases : an international journal |
| container_volume | 40 |
| creator | Hillenbrand, Andrea Störl, Uta Nabiyev, Shamil Klettke, Meike |
| description | When NoSQL database systems are used in an agile software development setting, data model changes occur frequently and thus, data is routinely stored in different versions. The management of versioned data leads to an overhead potentially impeding the software development. Several data migration strategies exist that handle legacy data differently during data accesses, each of which can be characterized by certain advantages and disadvantages. Depending on the requirements for the software application, we evaluate and compare different migration strategies through metrics like migration costs and latency as well as precision and recall. Ideally, exactly that strategy should be selected whose characteristics fulfill service-level agreements and match the migration scenario, which depends on the query workload and the changes in the data model which imply an evolution of the database schema. In this paper, we present a methodology of self-adapting data migration, which automatically adjusts migration strategies and their parameters with respect to the migration scenario and service-level agreements, thereby contributing to the self-management of database systems and supporting agile development. |
| doi_str_mv | 10.1007/s10619-021-07334-1 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2637583043</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2637583043</sourcerecordid><originalsourceid>FETCH-LOGICAL-c363t-7a34d116f7113f448838bad70025913124f05a499c909b4ae4979ed7170039783</originalsourceid><addsrcrecordid>eNp9kE1LAzEQQIMoWKt_wFPAc3Rmk90kRyl-QVG0eg7pbtJuaTc1SUX_vaurePM0l_dmhkfIKcI5AsiLhFChZlAgA8m5YLhHRlhKzmQp1T4ZgS4qpqQqDslRSisA0BLliDzN3Noz29htbrsFbWy2dNMuos1t6Gjb0bx0tA5ddu-ZBk9TvXQbS91bWO9-kfswe5x-q3ObXDomB96ukzv5mWPycn31PLll04ebu8nllNW84plJy0WDWHmJyL0QSnE1t40EKEqNHAvhobRC61qDngvrhJbaNf3XAFxLxcfkbNi7jeF151I2q7CLXX_SFBWXpeIgeE8VA1XHkFJ03mxju7HxwyCYr3ZmaGf6dua7ncFe4oOUerhbuPi3-h_rE4Vzb4k</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2637583043</pqid></control><display><type>article</type><title>Self-adapting data migration in the context of schema evolution in NoSQL databases</title><source>SpringerNature Journals</source><creator>Hillenbrand, Andrea ; Störl, Uta ; Nabiyev, Shamil ; Klettke, Meike</creator><creatorcontrib>Hillenbrand, Andrea ; Störl, Uta ; Nabiyev, Shamil ; Klettke, Meike</creatorcontrib><description>When NoSQL database systems are used in an agile software development setting, data model changes occur frequently and thus, data is routinely stored in different versions. The management of versioned data leads to an overhead potentially impeding the software development. Several data migration strategies exist that handle legacy data differently during data accesses, each of which can be characterized by certain advantages and disadvantages. Depending on the requirements for the software application, we evaluate and compare different migration strategies through metrics like migration costs and latency as well as precision and recall. Ideally, exactly that strategy should be selected whose characteristics fulfill service-level agreements and match the migration scenario, which depends on the query workload and the changes in the data model which imply an evolution of the database schema. In this paper, we present a methodology of self-adapting data migration, which automatically adjusts migration strategies and their parameters with respect to the migration scenario and service-level agreements, thereby contributing to the self-management of database systems and supporting agile development.</description><identifier>ISSN: 0926-8782</identifier><identifier>EISSN: 1573-7578</identifier><identifier>DOI: 10.1007/s10619-021-07334-1</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Computer Science ; Data models ; Data Structures ; Database Management ; Evolution ; Information Systems Applications (incl.Internet) ; Memory Structures ; Operating Systems ; Software ; Software development ; Special Issue on Self-Managing and Hardware-Optimized Database Systems</subject><ispartof>Distributed and parallel databases : an international journal, 2022-03, Vol.40 (1), p.5-25</ispartof><rights>The Author(s) 2021</rights><rights>The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c363t-7a34d116f7113f448838bad70025913124f05a499c909b4ae4979ed7170039783</citedby><cites>FETCH-LOGICAL-c363t-7a34d116f7113f448838bad70025913124f05a499c909b4ae4979ed7170039783</cites><orcidid>0000-0003-2771-142X ; 0000-0002-1063-5734 ; 0000-0003-0551-8389</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10619-021-07334-1$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10619-021-07334-1$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Hillenbrand, Andrea</creatorcontrib><creatorcontrib>Störl, Uta</creatorcontrib><creatorcontrib>Nabiyev, Shamil</creatorcontrib><creatorcontrib>Klettke, Meike</creatorcontrib><title>Self-adapting data migration in the context of schema evolution in NoSQL databases</title><title>Distributed and parallel databases : an international journal</title><addtitle>Distrib Parallel Databases</addtitle><description>When NoSQL database systems are used in an agile software development setting, data model changes occur frequently and thus, data is routinely stored in different versions. The management of versioned data leads to an overhead potentially impeding the software development. Several data migration strategies exist that handle legacy data differently during data accesses, each of which can be characterized by certain advantages and disadvantages. Depending on the requirements for the software application, we evaluate and compare different migration strategies through metrics like migration costs and latency as well as precision and recall. Ideally, exactly that strategy should be selected whose characteristics fulfill service-level agreements and match the migration scenario, which depends on the query workload and the changes in the data model which imply an evolution of the database schema. In this paper, we present a methodology of self-adapting data migration, which automatically adjusts migration strategies and their parameters with respect to the migration scenario and service-level agreements, thereby contributing to the self-management of database systems and supporting agile development.</description><subject>Computer Science</subject><subject>Data models</subject><subject>Data Structures</subject><subject>Database Management</subject><subject>Evolution</subject><subject>Information Systems Applications (incl.Internet)</subject><subject>Memory Structures</subject><subject>Operating Systems</subject><subject>Software</subject><subject>Software development</subject><subject>Special Issue on Self-Managing and Hardware-Optimized Database Systems</subject><issn>0926-8782</issn><issn>1573-7578</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><recordid>eNp9kE1LAzEQQIMoWKt_wFPAc3Rmk90kRyl-QVG0eg7pbtJuaTc1SUX_vaurePM0l_dmhkfIKcI5AsiLhFChZlAgA8m5YLhHRlhKzmQp1T4ZgS4qpqQqDslRSisA0BLliDzN3Noz29htbrsFbWy2dNMuos1t6Gjb0bx0tA5ddu-ZBk9TvXQbS91bWO9-kfswe5x-q3ObXDomB96ukzv5mWPycn31PLll04ebu8nllNW84plJy0WDWHmJyL0QSnE1t40EKEqNHAvhobRC61qDngvrhJbaNf3XAFxLxcfkbNi7jeF151I2q7CLXX_SFBWXpeIgeE8VA1XHkFJ03mxju7HxwyCYr3ZmaGf6dua7ncFe4oOUerhbuPi3-h_rE4Vzb4k</recordid><startdate>20220301</startdate><enddate>20220301</enddate><creator>Hillenbrand, Andrea</creator><creator>Störl, Uta</creator><creator>Nabiyev, Shamil</creator><creator>Klettke, Meike</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>C6C</scope><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0003-2771-142X</orcidid><orcidid>https://orcid.org/0000-0002-1063-5734</orcidid><orcidid>https://orcid.org/0000-0003-0551-8389</orcidid></search><sort><creationdate>20220301</creationdate><title>Self-adapting data migration in the context of schema evolution in NoSQL databases</title><author>Hillenbrand, Andrea ; Störl, Uta ; Nabiyev, Shamil ; Klettke, Meike</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c363t-7a34d116f7113f448838bad70025913124f05a499c909b4ae4979ed7170039783</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Computer Science</topic><topic>Data models</topic><topic>Data Structures</topic><topic>Database Management</topic><topic>Evolution</topic><topic>Information Systems Applications (incl.Internet)</topic><topic>Memory Structures</topic><topic>Operating Systems</topic><topic>Software</topic><topic>Software development</topic><topic>Special Issue on Self-Managing and Hardware-Optimized Database Systems</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hillenbrand, Andrea</creatorcontrib><creatorcontrib>Störl, Uta</creatorcontrib><creatorcontrib>Nabiyev, Shamil</creatorcontrib><creatorcontrib>Klettke, Meike</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>CrossRef</collection><jtitle>Distributed and parallel databases : an international journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hillenbrand, Andrea</au><au>Störl, Uta</au><au>Nabiyev, Shamil</au><au>Klettke, Meike</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Self-adapting data migration in the context of schema evolution in NoSQL databases</atitle><jtitle>Distributed and parallel databases : an international journal</jtitle><stitle>Distrib Parallel Databases</stitle><date>2022-03-01</date><risdate>2022</risdate><volume>40</volume><issue>1</issue><spage>5</spage><epage>25</epage><pages>5-25</pages><issn>0926-8782</issn><eissn>1573-7578</eissn><abstract>When NoSQL database systems are used in an agile software development setting, data model changes occur frequently and thus, data is routinely stored in different versions. The management of versioned data leads to an overhead potentially impeding the software development. Several data migration strategies exist that handle legacy data differently during data accesses, each of which can be characterized by certain advantages and disadvantages. Depending on the requirements for the software application, we evaluate and compare different migration strategies through metrics like migration costs and latency as well as precision and recall. Ideally, exactly that strategy should be selected whose characteristics fulfill service-level agreements and match the migration scenario, which depends on the query workload and the changes in the data model which imply an evolution of the database schema. In this paper, we present a methodology of self-adapting data migration, which automatically adjusts migration strategies and their parameters with respect to the migration scenario and service-level agreements, thereby contributing to the self-management of database systems and supporting agile development.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10619-021-07334-1</doi><tpages>21</tpages><orcidid>https://orcid.org/0000-0003-2771-142X</orcidid><orcidid>https://orcid.org/0000-0002-1063-5734</orcidid><orcidid>https://orcid.org/0000-0003-0551-8389</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0926-8782 |
| ispartof | Distributed and parallel databases : an international journal, 2022-03, Vol.40 (1), p.5-25 |
| issn | 0926-8782 1573-7578 |
| language | eng |
| recordid | cdi_proquest_journals_2637583043 |
| source | SpringerNature Journals |
| subjects | Computer Science Data models Data Structures Database Management Evolution Information Systems Applications (incl.Internet) Memory Structures Operating Systems Software Software development Special Issue on Self-Managing and Hardware-Optimized Database Systems |
| title | Self-adapting data migration in the context of schema evolution in NoSQL databases |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-22T13%3A05%3A09IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Self-adapting%20data%20migration%20in%20the%20context%20of%20schema%20evolution%20in%20NoSQL%20databases&rft.jtitle=Distributed%20and%20parallel%20databases%20:%20an%20international%20journal&rft.au=Hillenbrand,%20Andrea&rft.date=2022-03-01&rft.volume=40&rft.issue=1&rft.spage=5&rft.epage=25&rft.pages=5-25&rft.issn=0926-8782&rft.eissn=1573-7578&rft_id=info:doi/10.1007/s10619-021-07334-1&rft_dat=%3Cproquest_cross%3E2637583043%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2637583043&rft_id=info:pmid/&rfr_iscdi=true |