31:T14d3,Histogram = { const temporal_H_I12 = vl.markBar({size:5}) .data(temporal_H_IA2012) .encode( vl.x().fieldQ('rel_month').scale({domain: [1, 20]}).axis({grid: false, title:null, titleFontSize:"15", labelFontSize: "15", "values": [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20], labelAngle: "90"}), vl.y().fieldQ('count').axis({grid: false,labelColor:"#727272", labelPadding:12, title:"Number of Requests", titleFontSize:"17", labelFontSize: "15"}), vl.color({"value": "#1F618D"}) ) .title({text:"IA 2012", color: "#026bb8", fontSize:16, align:"center"}) .width(300) .height(200) const temporal_H_I15 = vl.markBar({size:5}) .data(temporal_H_IA2015) .encode( vl.x().fieldQ('rel_month').scale({domain: [1, 20]}).axis({grid: false, title:null, titleFontSize:"15", labelFontSize: "15", "values": [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20], labelAngle: "90"}), vl.y().fieldQ('count').axis({grid: false,labelColor:"#727272", labelPadding:12, title:"Number of Requests", titleFontSize:"17", labelFontSize: "15"}), vl.color({"value": "#1F618D"}) ) .title({text:"IA 2015", color: "#026bb8", fontSize:16, align:"center"}) .width(300) .height(200) const temporal_H_I19 = vl.markBar({size:5}) .data(temporal_H_IA2019) .encode( vl.x().fieldQ('rel_month').scale({domain: [1, 24]}).axis({grid: false,title:null, "values": [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24], labelAngle: "90", titleFontSize:"15", labelFontSize: "15"}), vl.y().fieldQ('count').axis({grid: false,ticks: 0, labelColor:"#727272", labelPadding:12, title:"Number of Requests", titleFontSize:"15", labelFontSize: "15"}), vl.color({"value": "#1F618D"}) ) .title({text:"IA 2019", color: "#026bb8", fontSize:16, align:"center"}) .width(300) .height(200) const temporal_H_P19 = vl.markBar({size:5}) .data(temporal_H_PT2019) .encode( vl.x().fieldQ('rel_month').scale({domain: [1, 15]}).axis({grid: false,title:"Number of Years Prior", "values": [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15], labelAngle: "90", titleFontSize:"15", labelFontSize: "15"}), vl.y().fieldQ('count').axis({grid: false,ticks: 0, labelColor:"#727272", labelPadding:12, title:"Number of Requests", titleFontSize:"15", labelFontSize: "15"}), vl.color({"value": "#1F618D"}) ) .title({text:"PT 2019", color: "#026bb8", fontSize:16, align:"center"}) .width(300) .height(200) const temporal_B_I12 = vl.markBar({size:5}) .data(temporal_B_IA2012) .encode( vl.x().fieldQ('rel_month').scale({domain: [1, 20]}).axis({grid: false, title:null, titleFontSize:"17", labelFontSize: "15", "values": [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20], labelAngle: "90"}), vl.y().fieldQ('count').axis({grid: false,labelColor:"#727272", labelPadding:12, title:null, titleFontSize:"17", labelFontSize: "15"}), vl.color({"value": "#D35400"}) ) .title({text:"IA 2012", color: "#026bb8", fontSize:16, align:"center"}) .width(300) .height(200) const temporal_B_I15 = vl.markBar({size:5}) .data(temporal_B_IA2015) .encode( vl.x().fieldQ('rel_month').scale({domain: [1, 20]}).axis({grid: false, title:null, titleFontSize:"17", labelFontSize: "15", "values": [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20], labelAngle: "90"}), vl.y().fieldQ('count').axis({grid: false,labelColor:"#727272", labelPadding:12, title:null, titleFontSize:"17", labelFontSize: "15"}), vl.color({"value": "#D35400"}) ) .title({text:"IA 2015", color: "#026bb8", fontSize:16, align:"center"}) .width(300) .height(200) const temporal_B_I19 = vl.markBar({size:5}) .data(temporal_B_IA2019) .encode( vl.x().fieldQ('rel_month').scale({domain: [1, 24]}).axis({grid: false,title:null, "values": [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24], labelAngle: "90", titleFontSize:"17", labelFontSize: "15"}), vl.y().fieldQ('count').axis({grid: false,ticks: 0, labelColor:"#727272", labelPadding:12, title:null, titleFontSize:"15", labelFontSize: "15"}), vl.color({"value": "#D35400"}) ) .title({text:"IA 2019", color: "#026bb8", fontSize:16, align:"center"}) .width(300) .height(200) const temporal_B_P19 = vl.markBar({size:5}) .data(temporal_B_PT2019) .encode( vl.x().fieldQ('rel_month').scale({domain: [1, 15]}).axis({grid: false,title:"Number of Years Prior", "values": [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15], labelAngle: "90", titleFontSize:"15", labelFontSize: "15"}), vl.y().fieldQ('count').axis({grid: false,ticks: 0, labelColor:"#727272", labelPadding:12, title:null, titleFontSize:"15", labelFontSize: "15"}), vl.color({"value": "#D35400"}) ) .title({text:"PT 2019", color: "#026bb8", fontSize:16, align:"center"}) .width(300) .height(200) const left = vl.vconcat(temporal_H_I12,temporal_H_I15,temporal_H_I19, temporal_H_P19) const right = vl.vconcat(temporal_B_I12,temporal_B_I15,temporal_B_I19,temporal_B_P19) //.const top = vl.hconcat(temporal_H_I12,temporal_H_I15,temporal_H_I19, temporal_H_P19) //.const bottom = vl.hconcat(temporal_B_I12,temporal_B_I15,temporal_B_I19,temporal_B_P19) return vl.hconcat(left,right) //..config({"view": {"stroke":"transparent"}}) //.title({text:"Major Title", color: "#2680c0", fontSize:20, align:"center"}) .render() }32:Tc3e,#### Description ##### Axis - **x-axis:** The number of months prior - Relative The number of months passed with respect to the date-time of the access logs. Example: 1. For IA 2012 February sample, 24 months prior is 2010 February 2. For IA 2015 February sample, 24 months prior is 2013 February 3. For IA 2019 February sample, 24 months prior is 2017 February 4. For PT 2019 February sample, 24 months prior is 2017 February * **y-axis:** Number of requests ##### Interpretation * We can answer the below questions by looking at the above two main charts. (1) Is there a temporal preference for web archive users? It's evident that the majority of the requests are for mementos that are close to the date-time of each access log sample and gradually diminish as we go further back in time. (2) Comparisons between the datasets. 2.1 Is there a difference in temporal preference from 2012 to 2015 to 2019 for web archive users? There is no significant difference in temporal preference from 2012 to 2019. 2.2 Is there a difference in the temporal preference of users from one web archive to other (IA and PT)? There is no significant difference in temporal preference in IA and PT. (3) Comparisons between humans and robots. 3.1 Is there a difference in temporal preference between bots and humans for web archive users? There is no significant difference in temporal preference between bots and humans in IA2012, IA2015, and IA2019 samples. The majority of the requests are for mementos that are close to the date-time of the two access log samples and gradually diminish as we go further back in time. The PT2019 robots follow a similar temporal preference as all other samples, however, in the PT2019 sample, the humans display a different temporal preference. There are two spikes around 4-5 years prior and 7-8 years which implies PT human accesses were mostly for years around 2015-2016 followed by requests for 2012-2013 mementos. 3.2 Is there a difference in temporal preference between bots and humans from 2012 to 2019 for web archive users? There is no significant difference in temporal preference between bots and humans from 2012 to 2019. 3.2 Is there a difference in temporal preference between bots and humans from one web archive to another (IA and PT)? There seems to be no significant difference between the temporal preference of bots between IA and PT. However, as mentioned in 3.1, humans are displaying a different temporal preference between the two archives. This could perhaps be just on the day we choose. Looking at more days would give us more insight into this (future work?) * What are the implications? In terms of temporal preferences, there is an advantage. Web archives can prioritize or store data in memory for the most recent years to speed up disk access. This may assist with hardware optimization. Web archives can store recent WARC files and CDX indexes in their storage, or even divide their indexes into yearly buckets, so they don't have to go through everything when a recent memento is requested. 33:Tbae,#### Description There are four different user access patterns established by AlNoamany et al. (the findings are included in the chart included below) in the prior work. We looked into each of these patterns and identified the prevalence of these patterns in our three datasets. A quick overview of the different patterns is as below: * Dip - The user accesses only one URI (URI-M or URI-T). * Slide - The user accesses the same URI-R at different Memento-Datetimes. * Dive - The user accesses different URI-Rs at nearly the same datetime ( Dives deeply into a memento by browsing hyperlinks of URI-Ms). * Skim - The user accesses different TimeMaps (URI-T). ##### Axis - **x-axis:** Access patterns - **y-axis:** Percentage of number of requests - **color:** Mementos vs. TimeMaps ##### Interpretation * Dips were the most common pattern in both human and robot sessions, according to AlNoamany et al.'s findings based on the 2012 IA sample. However, in our full-day IA2012 and IA2015 samples, "Dive" and "Dip" account for about the same percentage of human sessions but "Skim" is the most common pattern among robot sessions. * Dip is the most common pattern in IA2019, with "Slide, Dive" coming in second for both human and robot sessions. * A High number of Robot "Skim" in IA2012 and IA2015 compared to IA2019. In IA 2012 robot sessions, it is over 90% Skims. **Mixed patterns** We could observe sessions having a hybrid of the patterns. * The below combinations of patterns are there in all three datasets for human and bot sessions. - Dive and Slide - Dive and Skim - Skim and Slide - Dive, Skim, and Slide * PT2019 bot sessions only have a maximum of two patterns whereas all others have a small percentage of hybrid of all three (Dive, Skim, and Slide). **Unknown** For each sample in IA, there is a very small percentage of requests (4.22% in IA2012, 0.97% in IA2019) that do not belong to any of the patterns. There are no requests in PT2019 that are of an "Unknown" pattern. Human vs Bot "Unknown" % Breakdown: IA2012 - Human: 4.02% IA2012 - Bot: 0.2% IA2015 - Human: 3.42% IA2015 - Bot: 0.33% IA2019 - Human: 0.85% IA2019 - Bot: 0.12% The percentage of human requests in IA2012 and IA2015 falling under "Unknown" category is significantly higher compared to all other request percentages (IA2012 robot, IA2015 robot, and IA2019 human and robot). ###### Future work: AlNoamany et al. observed four different user access patterns in 2013, and 0.48% of requests in all our samples did not fall into any of these patterns or their combinations. One may look into if the x percent of requests that fell into the unknown category have any other generally applicable patterns, or if they are completely random. 34:T161d,Final_Chart_Pattern_SameXaxis1 = { const stackedBar1 = vl.markBar({size:19}) .data(pattern_IA2012_H) .encode( vl.x().fieldQ("percentage").title(null).axis({grid: false,titleFontSize:"15", labelFontSize: "13", titlePadding:20}), vl.y().fieldN("Pattern").title("Access Patterns").axis({grid: false,titleFontSize:"18", labelFontSize: "15"}).sort(vl.field('percentage').order('descending')), vl.color({"legend": {title:null, labelFontSize:15}}).fieldN("TM"), vl.order().fieldN("TM"), vl.tooltip([vl.fieldN("Pattern"),"percentage"]) ) .title({text:"IA 2012 Human", color: "#026bb8", fontSize:18, align:"center"}) .width(300) .height(220) const stackedBar2 = vl.markBar({size:19}) .data(pattern_IA2012_R) .encode( vl.x().fieldQ("percentage").title(null).axis({grid: false,titleFontSize:"15", labelFontSize: "12", titlePadding:20}), vl.y().fieldN("Pattern").title(null).axis({grid: false,titleFontSize:"18", labelFontSize: "15"}).sort(vl.field('percentage').order('descending')), vl.color({"legend": {title: "Memento or TimeMap"}}).fieldN("TM"), vl.order().fieldN("TM"), vl.tooltip([vl.fieldN("Pattern"),"percentage"]) ) .title({text:"IA 2012 Robot", color: "#026bb8", fontSize:18, align:"center"}) .width(300) .height(220) const stackedBar3 = vl.markBar({size:19}) .data(pattern_IA2015_H) .encode( vl.x().fieldQ("percentage").title(null).axis({grid: false,titleFontSize:"15", labelFontSize: "13", titlePadding:20}), vl.y().fieldN("Pattern").title("Access Patterns").axis({grid: false,titleFontSize:"18", labelFontSize: "15"}).sort(vl.field('percentage').order('descending')), vl.color({"legend": {title:null, labelFontSize:15}}).fieldN("TM"), vl.order().fieldN("TM"), vl.tooltip([vl.fieldN("Pattern"),"percentage"]) ) .title({text:"IA 2015 Human", color: "#026bb8", fontSize:18, align:"center"}) .width(300) .height(220) const stackedBar4 = vl.markBar({size:19}) .data(pattern_IA2015_R) .encode( vl.x().fieldQ("percentage").title(null).axis({grid: false,titleFontSize:"15", labelFontSize: "13", titlePadding:20}), vl.y().fieldN("Pattern").title(null).axis({grid: false,titleFontSize:"18", labelFontSize: "15"}).sort(vl.field('percentage').order('descending')), vl.color({"legend": {title:null, labelFontSize:15}}).fieldN("TM"), vl.order().fieldN("TM"), vl.tooltip([vl.fieldN("Pattern"),"percentage"]) ) .title({text:"IA 2015 Robot", color: "#026bb8", fontSize:18, align:"center"}) .width(300) .height(220) const stackedBar5 = vl.markBar({size:19}) .data(pattern_IA2019_H) .encode( vl.x().fieldQ("percentage").title(null).axis({grid: false,titleFontSize:"15", labelFontSize: "13", titlePadding:20}), vl.y().fieldN("Pattern").title("Access Patterns").axis({grid: false,titleFontSize:"18", labelFontSize: "15"}).sort(vl.field('percentage').order('descending')), vl.color({"legend": {title: "Memento or TimeMap"}}).fieldN("TM"), vl.order().fieldN("TM"), vl.tooltip([vl.fieldN("Pattern"),"percentage"]) ) .title({text:"IA 2019 Human", color: "#026bb8", fontSize:18, align:"center"}) .width(300) .height(220) const stackedBar6 = vl.markBar({size:19}) .data(pattern_IA2019_R) .encode( vl.x().fieldQ("percentage").title(null).axis({grid: false,titleFontSize:"15", labelFontSize: "13", titlePadding:20}), vl.y().fieldN("Pattern").title(null).axis({grid: false,titleFontSize:"18", labelFontSize: "15"}).sort(vl.field('percentage').order('descending')), vl.color({"legend": {title: "Memento or TimeMap"}}).fieldN("TM"), vl.order().fieldN("TM"), vl.tooltip([vl.fieldN("Pattern"),"percentage"]) ) .title({text:"IA 2019 Robot", color: "#026bb8", fontSize:18, align:"center"}) .width(300) .height(220) const stackedBar7 = vl.markBar({size:19}) .data(pattern_PT2019_H) .encode( vl.x().fieldQ("percentage").title("Percentage of Requests").axis({grid: false,titleFontSize:"15", labelFontSize: "13", titlePadding:20}), vl.y().fieldN("Pattern").title("Access Patterns").axis({grid: false,titleFontSize:"18", labelFontSize: "15"},).sort(vl.field('percentage').order('descending')), vl.color({"legend": {title: "Memento or TimeMap"}}).fieldN("TM"), vl.order().fieldN("TM"), vl.tooltip([vl.fieldN("Pattern"),"percentage"]) ) .title({text:"PT 2019 Human", color: "#026bb8", fontSize:18, align:"center"}) .width(300) .height(180) const stackedBar8 = vl.markBar({size:19}) .data(pattern_PT2019_R) .encode( vl.x().fieldQ("percentage").title("Percentage of Requests").axis({grid: false,titleFontSize:"15", labelFontSize: "13", titlePadding:20}), vl.y().fieldN("Pattern").title(" ").axis({grid: false,titleFontSize:"18", labelFontSize: "15", titlePadding:20}).sort(vl.field('percentage').order('descending')), vl.color({"legend": {title: "Memento or TimeMap"}}).fieldN("TM"), vl.order().fieldN("TM"), vl.tooltip([vl.fieldN("Pattern"),"percentage"]) ) .title({text:"PT 2019 Robot", color: "#026bb8", fontSize:18, align:"center"}) .width(300) .height(179) //const IA2012_final = vl.vconcat(stackedBar1, stackedBar2) //return vl.hconcat(stackedBar1, stackedBar3, stackedBar5) //return vl.vconcat(vl.hconcat(stackedBar1, stackedBar3, stackedBar5),vl.hconcat(stackedBar2, stackedBar4, stackedBar6)) return vl.vconcat(vl.hconcat(stackedBar1,stackedBar2),vl.hconcat(stackedBar3,stackedBar4),vl.hconcat(stackedBar5,stackedBar6),vl.hconcat(stackedBar7,stackedBar8)) // .title({text:"sdfasdgasg", align:"center", color: "#026bb8", fontSize:16, orient:"bottom"}) .config({"view": {"stroke":"transparent"}}) .render() }35:[{"id":"b742381c60a39ddeaeadec1565c2a5986fca4dcdb75da9b5596075a84113b51c48e318ae8b6892cc07d8042b8574850574d33b81291cd0584e29724997b050d7","href":"https://static.observableusercontent.com/files/b742381c60a39ddeaeadec1565c2a5986fca4dcdb75da9b5596075a84113b51c48e318ae8b6892cc07d8042b8574850574d33b81291cd0584e29724997b050d7","name":"temporal_all.csv","type":"text/csv","lastModified":1653749076174.941,"size":32412},{"id":"99939e1350cf913140d81d1f5c1dae0b44bbea24719203c54fbfb77de954c10326c479252932e5ca54b65a5d7ac137706ddfbce47d0a704451a8c44e765a2a7c","href":"https://static.observableusercontent.com/files/99939e1350cf913140d81d1f5c1dae0b44bbea24719203c54fbfb77de954c10326c479252932e5ca54b65a5d7ac137706ddfbce47d0a704451a8c44e765a2a7c","name":"temporal_bot.csv","type":"text/csv","lastModified":1653749076205.436,"size":16992},{"id":"4f32dcef19ddb8c974b085eb1c2307801676c1863a0d5021f1342d41fda8f997aee3ff770cd042ad7709421de45fb46c93fcfe4f2465b1b35a3ce594d77b41c5","href":"https://static.observableusercontent.com/files/4f32dcef19ddb8c974b085eb1c2307801676c1863a0d5021f1342d41fda8f997aee3ff770cd042ad7709421de45fb46c93fcfe4f2465b1b35a3ce594d77b41c5","name":"pattern_all@1.csv","type":"text/csv","lastModified":1653878281781.379,"size":6331},{"id":"6b414f5c92db73407a9ce92b37b042e315fb703b81d27217c7f4baf64abd6779d8684fdba2cabfacdb527d8419dd10ca2e7b59ec8fa6c157ab365a95c9b768e9","href":"https://static.observableusercontent.com/files/6b414f5c92db73407a9ce92b37b042e315fb703b81d27217c7f4baf64abd6779d8684fdba2cabfacdb527d8419dd10ca2e7b59ec8fa6c157ab365a95c9b768e9","name":"pattern_all@2.csv","type":"text/csv","lastModified":1653920840991.605,"size":5685},{"id":"91e1b3c4a8e97c3d1fccc9c41fdef71a4bf474afb000e6b5da0777c94813517a0b1dde5254a2ec6ecd8e8a2813e2134b8853f150d845134d687df4c9b48a70ec","href":"https://static.observableusercontent.com/files/91e1b3c4a8e97c3d1fccc9c41fdef71a4bf474afb000e6b5da0777c94813517a0b1dde5254a2ec6ecd8e8a2813e2134b8853f150d845134d687df4c9b48a70ec","name":"pattern_all@3.csv","type":"text/csv","lastModified":1654097671011.582,"size":6670},{"id":"7226589e831c58570d60d1ba5e3a909fb0ce0009b9945e5efba43be890d53434bac306cd02f64421ca68a83df4062661f1054b42c8143c3a1b280236e3fde39f","href":"https://static.observableusercontent.com/files/7226589e831c58570d60d1ba5e3a909fb0ce0009b9945e5efba43be890d53434bac306cd02f64421ca68a83df4062661f1054b42c8143c3a1b280236e3fde39f","name":"temporal_bot@1.csv","type":"text/csv","lastModified":1668841600281.282,"size":23403},{"id":"556d22c9d0e42afef23171f5fa21c4c5c38c916ed45ef588aa6225ab9594da0ebca231736764792bdd97994f26b1a38820daed2696886f4d6b8cb343961ef479","href":"https://static.observableusercontent.com/files/556d22c9d0e42afef23171f5fa21c4c5c38c916ed45ef588aa6225ab9594da0ebca231736764792bdd97994f26b1a38820daed2696886f4d6b8cb343961ef479","name":"temporal_human@1.csv","type":"text/csv","lastModified":1668841613317.578,"size":21626},{"id":"bb9dca9ebb761f806ce3beb87708da9676a97b06c8a84a672ff943b2403f334c5ff5443be770b4def80c45b94d8280d783133b0d5a1ed42537ce03a99a783f83","href":"https://static.observableusercontent.com/files/bb9dca9ebb761f806ce3beb87708da9676a97b06c8a84a672ff943b2403f334c5ff5443be770b4def80c45b94d8280d783133b0d5a1ed42537ce03a99a783f83","name":"temporal_all@1.csv","type":"text/csv","lastModified":1668839707989.188,"size":44997},{"id":"408af5e6ab3e1c7db255a3b8a06b6a7055b1a68c267876bd6961ef4d5bc5d95ea42c53e0b4636fb9ca86a89903b9d7c02a8d143934600d1f5b7a2ca2feb9bc4f","href":"https://static.observableusercontent.com/files/408af5e6ab3e1c7db255a3b8a06b6a7055b1a68c267876bd6961ef4d5bc5d95ea42c53e0b4636fb9ca86a89903b9d7c02a8d143934600d1f5b7a2ca2feb9bc4f","name":"pattern_all@5.csv","type":"text/csv","lastModified":1669611634518.305,"size":8533},{"id":"a64f0e2f36885465d60aceaea0a84c7a310bcdcb2c08f9569ce4cb296e686aa963eeaa8b6e95033faf9fffc8bc5882ff0f82d9a6e3810db765294634183c421a","href":"https://static.observableusercontent.com/files/a64f0e2f36885465d60aceaea0a84c7a310bcdcb2c08f9569ce4cb296e686aa963eeaa8b6e95033faf9fffc8bc5882ff0f82d9a6e3810db765294634183c421a","name":"pattern_all@6.csv","type":"text/csv","lastModified":1669611786782.025,"size":8405},{"id":"cae7251393deef9c31b69d0a9cb53fd99286403f2196f6212558b34a6d95919a20955109875738a444039344e040866e113533a463ce4b8e5e86a3710f9b428a","href":"https://static.observableusercontent.com/files/cae7251393deef9c31b69d0a9cb53fd99286403f2196f6212558b34a6d95919a20955109875738a444039344e040866e113533a463ce4b8e5e86a3710f9b428a","name":"temporal_human.csv","type":"text/csv","lastModified":1653749076243.82,"size":15452},{"id":"e39bd7db509b2b150f46e956c53dd8584de4b2fda3a1bb4f996d97fb642b4e7f843ffe9409e4e25a78f919dff4c1d8f373d0243339d80e5848be4fd1cdfa8953","href":"https://static.observableusercontent.com/files/e39bd7db509b2b150f46e956c53dd8584de4b2fda3a1bb4f996d97fb642b4e7f843ffe9409e4e25a78f919dff4c1d8f373d0243339d80e5848be4fd1cdfa8953","name":"pattern_all.csv","type":"text/csv","lastModified":1653873712337.534,"size":6318},{"id":"ece357537e18dbaba1206ca9df9d1b4ca56fad6206094408c81f9bc32d35016a9e764da8ee758aba852c83ebbb24c1b9b75ccb7ed817efd6005ebac77ebe266d","href":"https://static.observableusercontent.com/files/ece357537e18dbaba1206ca9df9d1b4ca56fad6206094408c81f9bc32d35016a9e764da8ee758aba852c83ebbb24c1b9b75ccb7ed817efd6005ebac77ebe266d","name":"yasmin_jcdl_13.png","type":"image/png","lastModified":1653891125237.532,"size":42795},{"id":"15a3cb6734f651a6acf7c97687506adc57af564a322be4afa4d902552146d469075941a84aa7949333bbbc4ac14602e6a6e335c2695a27b0a55758fa52818c99","href":"https://static.observableusercontent.com/files/15a3cb6734f651a6acf7c97687506adc57af564a322be4afa4d902552146d469075941a84aa7949333bbbc4ac14602e6a6e335c2695a27b0a55758fa52818c99","name":"pattern_all@4.csv","type":"text/csv","lastModified":1668838772945.608,"size":8841}] 2f:{"title":"Visualizations for Web Archive Access Log Datasets","cells":[{"id":0,"value":"# Visualizations for Web Archive Access Log Datasets\n***Himarsha R. Jayanetti*** \n*Created on:* Wednesday, May 25, 2022 \n*Updated on:* Friday, Nov 18, 2022 (added Internet Archive 2015 data)\n","pinned":false,"hidden":false,"mode":"md"},{"id":10,"value":"toc()","pinned":false,"hidden":false,"mode":"ojs"},{"id":7,"value":"## Temporal Analysis","pinned":false,"hidden":false,"mode":"md"},{"id":48,"value":"### Import Data","pinned":false,"hidden":false,"mode":"md"},{"id":35,"value":"temporal_all = d3.csvParse(await FileAttachment(\"temporal_all@1.csv\").text(), function(d) {\n return {\n data_label: d[\"data\"],\n rel_month: d[\"rel_month\"]/12,\n botVshuman: d[\"bot\"],\n memdate: d3.timeParse(\"%Y%m\")(d[\"memdate\"]),\n count: d[\"count\"]\n };\n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":39,"value":"temporal_human = d3.csvParse(await FileAttachment(\"temporal_human@1.csv\").text(), function(d) {\n return {\n data_label: d[\"data\"],\n rel_month: d[\"rel_month\"]/12,\n memdate: d3.timeParse(\"%Y%m\")(d[\"memdate\"]),\n count: d[\"count\"]\n };\n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":42,"value":"temporal_bot = d3.csvParse(await FileAttachment(\"temporal_bot@1.csv\").text(), function(d) {\n return {\n data_label: d[\"data\"],\n rel_month: d[\"rel_month\"]/12,\n memdate: d3.timeParse(\"%Y%m\")(d[\"memdate\"]),\n count: d[\"count\"]\n };\n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":51,"value":"### Data Manipulation","pinned":false,"hidden":false,"mode":"md"},{"id":122,"value":"#### All","pinned":false,"hidden":false,"mode":"md"},{"id":124,"value":"temporal_map = d3.group(temporal_all, d => d.data_label)","pinned":false,"hidden":false,"mode":"ojs"},{"id":127,"value":"temporal_IA2012 = temporal_map.get(\"IA_2012\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":753,"value":"temporal_IA2015 = temporal_map.get(\"IA_2015\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":129,"value":"temporal_IA2019 = temporal_map.get(\"IA_2019\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":131,"value":"temporal_PT2019 = temporal_map.get(\"PT_2019\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":120,"value":"#### Separately for Bots and Humans","pinned":false,"hidden":false,"mode":"md"},{"id":53,"value":"temporal_human_map = d3.group(temporal_human, d => d.data_label)","pinned":false,"hidden":false,"mode":"ojs"},{"id":61,"value":"temporal_bot_map = d3.group(temporal_bot, d => d.data_label)","pinned":false,"hidden":false,"mode":"ojs"},{"id":65,"value":"temporal_H_IA2012 = temporal_human_map.get(\"IA_2012\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":68,"value":"temporal_B_IA2012 = temporal_bot_map.get(\"IA_2012\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":800,"value":"temporal_H_IA2015 = temporal_human_map.get(\"IA_2015\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":802,"value":"temporal_B_IA2015 = temporal_bot_map.get(\"IA_2015\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":71,"value":"temporal_H_IA2019 = temporal_human_map.get(\"IA_2019\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":77,"value":"temporal_B_IA2019 = temporal_bot_map.get(\"IA_2019\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":74,"value":"temporal_H_PT2019 = temporal_human_map.get(\"PT_2019\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":80,"value":"temporal_B_PT2019 = temporal_bot_map.get(\"PT_2019\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":644,"value":"

\n\n  \n","pinned":false,"hidden":false,"mode":"md"},{"id":55,"value":"### Charts","pinned":false,"hidden":false,"mode":"md"},{"id":110,"value":"#### Separately for Bots and Humans","pinned":false,"hidden":false,"mode":"md"},{"id":185,"value":"viewof symbolLegend = plot({\n  autosize: \"pad\",\n  data: [\n    {\n      name: \"values\",\n      values: [\"Human\", \"Bot\"]\n    }\n  ],\n  scales: [\n    {\n      name: \"symbolColor\",\n      type: \"ordinal\",\n      domain: {data: \"values\", field: \"data\"},\n      range: [\"#1F618D\",\"#D35400\"]\n    },\n    {\n      name: \"symbolSize\",\n      type: \"ordinal\",\n      domain: {data: \"values\", field: \"data\"},\n      range: [200, 200]\n    }\n  ],\n  legends: [Object.assign({offset: width-350}, symbolLegendDef)]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":57,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":240,"value":"\n\n  \n","pinned":false,"hidden":false,"mode":"md"},{"id":181,"value":"$32","pinned":false,"hidden":false,"mode":"md"},{"id":253,"value":"\n\n  \n","pinned":false,"hidden":false,"mode":"md"},{"id":255,"value":"## Access Patterns","pinned":false,"hidden":false,"mode":"md"},{"id":260,"value":"### Import Data","pinned":false,"hidden":false,"mode":"md"},{"id":276,"value":"pattern_all = d3.csvParse(await FileAttachment(\"pattern_all@6.csv\").text(), function(d) {\n  return {\n    dataset:d[\"dataset\"],\n    BotvsHuman: d[\"BotvsHuman\"],\n    Pattern: d[\"Pattern\"],\n    TM: d[\"TM\"],\n    Value: d[\"Value\"],\n    percentage: d[\"Percentage\"]\n  };\n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":263,"value":"### Data Manipulation","pinned":false,"hidden":false,"mode":"md"},{"id":287,"value":"pattern_map = d3.group(pattern_all, d => d.dataset)","pinned":false,"hidden":false,"mode":"ojs"},{"id":289,"value":"pattern_IA2012 = pattern_map.get(\"IA2012\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":667,"value":"pattern_IA2015 = pattern_map.get(\"IA2015\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":292,"value":"pattern_IA2019 = pattern_map.get(\"IA2019\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":294,"value":"pattern_PT2019 = pattern_map.get(\"PT2019\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":326,"value":"pattern_IA2012_map = d3.group(pattern_IA2012, d => d.BotvsHuman)","pinned":true,"hidden":false,"mode":"ojs"},{"id":324,"value":"pattern_IA2012_H = pattern_IA2012_map.get(\"Human\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":337,"value":"pattern_IA2012_R = pattern_IA2012_map.get(\"Robot\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":669,"value":"pattern_IA2015_map = d3.group(pattern_IA2015, d => d.BotvsHuman)","pinned":true,"hidden":false,"mode":"ojs"},{"id":672,"value":"pattern_IA2015_H = pattern_IA2015_map.get(\"Human\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":674,"value":"pattern_IA2015_R = pattern_IA2015_map.get(\"Robot\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":339,"value":"pattern_IA2019_map = d3.group(pattern_IA2019, d => d.BotvsHuman)","pinned":true,"hidden":false,"mode":"ojs"},{"id":341,"value":"pattern_IA2019_H = pattern_IA2019_map.get(\"Human\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":343,"value":"pattern_IA2019_R = pattern_IA2019_map.get(\"Robot\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":345,"value":"pattern_PT2019_map = d3.group(pattern_PT2019, d => d.BotvsHuman)","pinned":true,"hidden":false,"mode":"ojs"},{"id":347,"value":"pattern_PT2019_H = pattern_PT2019_map.get(\"Human\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":349,"value":"pattern_PT2019_R = pattern_PT2019_map.get(\"Robot\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":265,"value":"### Charts","pinned":false,"hidden":false,"mode":"md"},{"id":416,"value":"There are two charts for each dataset: \n\n1. Access patterns for human sessions.\n2. Access patterns for bot sessions.\n\nIn the charts below, the horizontal (x) axis represents the percentage of the number of requests and the verticle (y) axis represents the different patterns or combinations of patterns. The color of the stacked bar distinguishes between Mementos (blue) and TimeMaps (orange).\n\n* x-axis: Number of requests\n* y-axis: Access pattern \n* Color: Mementos(M) & TimeMaps(T)\n\nAll charts are sorted in descending order by the percentage value for each pattern to bring some order into the chart. It also makes it easy to see the most prevalent patterns for each archive. However, they can be a drawback when comparing between different charts for patterns.","pinned":false,"hidden":false,"mode":"md"},{"id":268,"value":"","pinned":false,"hidden":false,"mode":"md"},{"id":428,"value":"$33","pinned":false,"hidden":false,"mode":"md"},{"id":470,"value":"
","pinned":false,"hidden":false,"mode":"md"},{"id":466,"value":"#### AlNoamany et al. - Access Patterns Established in 2013","pinned":false,"hidden":false,"mode":"md"},{"id":432,"value":"$${await FileAttachment(\"yasmin_jcdl_13.png\").image()}","pinned":false,"hidden":false,"mode":"md"},{"id":500,"value":"
","pinned":false,"hidden":false,"mode":"md"},{"id":495,"value":"#### All - 2*3 - (x-axis is not ordered by y-axis values)\n","pinned":false,"hidden":false,"mode":"md"},{"id":497,"value":"","pinned":false,"hidden":false,"mode":"md"},{"id":484,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":12,"value":"## Imports\n","pinned":false,"hidden":false,"mode":"md"},{"id":14,"value":"d3 = require(\"d3@6\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":16,"value":"import { vl } from \"@vega/vega-lite-api\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":19,"value":"vegaEmbed = require(\"vega-embed@5\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":21,"value":"import {toc} from \"@harrystevens/toc\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":24,"value":"import {printTable} from '@uwdata/data-utilities'","pinned":false,"hidden":false,"mode":"ojs"},{"id":189,"value":"import {plot} from '@vega/vega-plot';","pinned":true,"hidden":false,"mode":"ojs"},{"id":195,"value":"import {slider, menu, checkbox} from '@jheer/dom-utilities'","pinned":true,"hidden":false,"mode":"ojs"},{"id":273,"value":"// The Vega legend definition.\n// https://observablehq.com/@vega/a-guide-to-guides-axes-legends-in-vega\nsymbolLegendDef = {\n  return {\n    type: \"symbol\",\n    title: \"\",\n    fill: \"symbolColor\",\n    size: checkbox('Sized Symbols', true) ? \"symbolSize\" : undefined,\n    direction: \"vertical\",\n    columns: +['', 'Default', true] || undefined,\n    rowPadding: 7,\n    columnPadding: 0,\n    clipHeight: +[0, 'None'],\n    titleOrient: \"left\",\n    symbolStrokeColor: '#fff',\n    symbolStrokeWidth: 1\n  };\n}","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W35","stdlib":"1","titling":"auto"}