Evaluating sliding and sticky target policies by measuring temporal drift in acyclic walks through a web archive
When viewing an archived page using the archive’s user interface (UI), the user selects a datetime to view from a list. The archived web page, if available, is then displayed. From this display, the web archive UI attempts to simulate the web browsing experience by smoothly transitioning between arc...
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Veröffentlicht in: | International journal on digital libraries 2015-06, Vol.16 (2), p.129-144 |
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description | When viewing an archived page using the archive’s user interface (UI), the user selects a datetime to view from a list. The archived web page, if available, is then displayed. From this display, the web archive UI attempts to simulate the web browsing experience by smoothly transitioning between archived pages. During this process, the target datetime changes with each link followed, potentially drifting away from the datetime originally selected. For sparsely archived resources, this almost transparent drift can be many years in just a few clicks. We conducted 200,000 acyclic walks of archived pages, following up to 50 links per walk, comparing the results of two target datetime policies. The Sliding Target policy allows the target datetime to change as it does in archive UIs such as the Internet Archive’s Wayback Machine. The Sticky Target policy, represented by the Memento API, keeps the target datetime the same throughout the walk. We found that the Sliding Target policy drift increases with the number of walk steps, number of domains visited, and choice (number of links available). However, the Sticky Target policy controls temporal drift, holding it to
<
30 days on average regardless of walk length or number of domains visited. The Sticky Target policy shows some increase as choice increases, but this may be caused by other factors. We conclude that based on walk length, the Sticky Target policy generally produces at least 30 days less drift than the Sliding Target policy. |
doi_str_mv | 10.1007/s00799-014-0120-4 |
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<
30 days on average regardless of walk length or number of domains visited. The Sticky Target policy shows some increase as choice increases, but this may be caused by other factors. We conclude that based on walk length, the Sticky Target policy generally produces at least 30 days less drift than the Sliding Target policy.</description><identifier>ISSN: 1432-5012</identifier><identifier>EISSN: 1432-1300</identifier><identifier>DOI: 10.1007/s00799-014-0120-4</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Computer Science ; Database Management ; Digital archives ; Digital libraries ; Information Systems and Communication Service ; Temporal logic</subject><ispartof>International journal on digital libraries, 2015-06, Vol.16 (2), p.129-144</ispartof><rights>Springer-Verlag Berlin Heidelberg 2014</rights><rights>Springer-Verlag Berlin Heidelberg 2015</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c386t-ed487eab1388257f7b06499378052054d1c04a39a27e95b3b20cd13ef436e2ce3</citedby><cites>FETCH-LOGICAL-c386t-ed487eab1388257f7b06499378052054d1c04a39a27e95b3b20cd13ef436e2ce3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00799-014-0120-4$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00799-014-0120-4$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51298</link.rule.ids></links><search><creatorcontrib>Ainsworth, Scott G.</creatorcontrib><creatorcontrib>Nelson, Michael L.</creatorcontrib><title>Evaluating sliding and sticky target policies by measuring temporal drift in acyclic walks through a web archive</title><title>International journal on digital libraries</title><addtitle>Int J Digit Libr</addtitle><description>When viewing an archived page using the archive’s user interface (UI), the user selects a datetime to view from a list. The archived web page, if available, is then displayed. From this display, the web archive UI attempts to simulate the web browsing experience by smoothly transitioning between archived pages. During this process, the target datetime changes with each link followed, potentially drifting away from the datetime originally selected. For sparsely archived resources, this almost transparent drift can be many years in just a few clicks. We conducted 200,000 acyclic walks of archived pages, following up to 50 links per walk, comparing the results of two target datetime policies. The Sliding Target policy allows the target datetime to change as it does in archive UIs such as the Internet Archive’s Wayback Machine. The Sticky Target policy, represented by the Memento API, keeps the target datetime the same throughout the walk. We found that the Sliding Target policy drift increases with the number of walk steps, number of domains visited, and choice (number of links available). However, the Sticky Target policy controls temporal drift, holding it to
<
30 days on average regardless of walk length or number of domains visited. The Sticky Target policy shows some increase as choice increases, but this may be caused by other factors. We conclude that based on walk length, the Sticky Target policy generally produces at least 30 days less drift than the Sliding Target policy.</description><subject>Computer Science</subject><subject>Database Management</subject><subject>Digital archives</subject><subject>Digital libraries</subject><subject>Information Systems and Communication Service</subject><subject>Temporal logic</subject><issn>1432-5012</issn><issn>1432-1300</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp1kElrwzAQhU1poenyA3oT9Ox2tNiWjyWkCwR6ac9ClseJEsd2JTnB_742zqGXHmaBee8NfFH0QOGJAmTPfmx5HgMVYzGIxUW0oIKzmHKAy_OejKfr6Mb7HQBQSbNF1K2Ouu51sM2G-NqW09RNSXywZj-QoN0GA-na2hqLnhQDOaD2vZt0AQ9d63RNSmerQGxDtBnMqCQnXe89CVvX9pst0eSEBdHObO0R76KrStce78_zNvp-XX0t3-P159vH8mUdGy7TEGMpZIa6oFxKlmRVVkAq8pxnEhIGiSipAaF5rlmGeVLwgoEpKcdK8BSZQX4bPc65nWt_evRB7dreNeNLRVNJZcJyKUYVnVXGtd47rFTn7EG7QVFQE1g1g1UjWDWBVZOHzR7fTRjQ_Un-1_QLPeJ72Q</recordid><startdate>20150601</startdate><enddate>20150601</enddate><creator>Ainsworth, Scott G.</creator><creator>Nelson, Michael L.</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7XB</scope><scope>88I</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ALSLI</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CNYFK</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0N</scope><scope>M1O</scope><scope>M2O</scope><scope>M2P</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PADUT</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope></search><sort><creationdate>20150601</creationdate><title>Evaluating sliding and sticky target policies by measuring temporal drift in acyclic walks through a web archive</title><author>Ainsworth, Scott G. ; Nelson, Michael L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c386t-ed487eab1388257f7b06499378052054d1c04a39a27e95b3b20cd13ef436e2ce3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Computer Science</topic><topic>Database Management</topic><topic>Digital archives</topic><topic>Digital libraries</topic><topic>Information Systems and Communication Service</topic><topic>Temporal logic</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ainsworth, Scott G.</creatorcontrib><creatorcontrib>Nelson, Michael L.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Social Science Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>Advanced Technologies & Aerospace Database (1962 - current)</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Library & Information Science Collection</collection><collection>ProQuest Central</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Computing Database</collection><collection>Library Science Database</collection><collection>ProQuest Research Library</collection><collection>Science Database (ProQuest)</collection><collection>Research Library (Corporate)</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Research Library China</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><jtitle>International journal on digital libraries</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ainsworth, Scott G.</au><au>Nelson, Michael L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluating sliding and sticky target policies by measuring temporal drift in acyclic walks through a web archive</atitle><jtitle>International journal on digital libraries</jtitle><stitle>Int J Digit Libr</stitle><date>2015-06-01</date><risdate>2015</risdate><volume>16</volume><issue>2</issue><spage>129</spage><epage>144</epage><pages>129-144</pages><issn>1432-5012</issn><eissn>1432-1300</eissn><abstract>When viewing an archived page using the archive’s user interface (UI), the user selects a datetime to view from a list. The archived web page, if available, is then displayed. From this display, the web archive UI attempts to simulate the web browsing experience by smoothly transitioning between archived pages. During this process, the target datetime changes with each link followed, potentially drifting away from the datetime originally selected. For sparsely archived resources, this almost transparent drift can be many years in just a few clicks. We conducted 200,000 acyclic walks of archived pages, following up to 50 links per walk, comparing the results of two target datetime policies. The Sliding Target policy allows the target datetime to change as it does in archive UIs such as the Internet Archive’s Wayback Machine. The Sticky Target policy, represented by the Memento API, keeps the target datetime the same throughout the walk. We found that the Sliding Target policy drift increases with the number of walk steps, number of domains visited, and choice (number of links available). However, the Sticky Target policy controls temporal drift, holding it to
<
30 days on average regardless of walk length or number of domains visited. The Sticky Target policy shows some increase as choice increases, but this may be caused by other factors. We conclude that based on walk length, the Sticky Target policy generally produces at least 30 days less drift than the Sliding Target policy.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00799-014-0120-4</doi><tpages>16</tpages></addata></record> |
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title | Evaluating sliding and sticky target policies by measuring temporal drift in acyclic walks through a web archive |
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