logseq/assets/icse2023-paper9_1666614163725_0.edn

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"}, :properties {:color "green"}} {:id #uuid "635edcad-de36-4ae4-8f8c-b0d47bbf3085", :page 1, :position {:bounding {:x1 159.9901123046875, :y1 1117.1315460205078, :x2 465.6258850097656, :y2 1136.8495635986328, :width 1060.8, :height 1372.8}, :rects ({:x1 159.9901123046875, :y1 1117.1315460205078, :x2 465.6258850097656, :y2 1136.8495635986328, :width 1060.8, :height 1372.8}), :page 1}, :content {:text " their effectiveness is of critical importance"}, :properties {:color "green"}} {:id #uuid "635edd26-35d9-49c5-b037-97c60e6f1902", :page 1, :position {:bounding {:x1 0, :y1 293.56219482421875, :x2 975.7518310546875, :y2 865.7268676757812, :width 1060.8, :height 1372.8}, :rects ({:x1 625.168701171875, :y1 293.56219482421875, :x2 975.7518310546875, :y2 313.28021240234375, :width 1060.8, :height 1372.8} {:x1 540.7562255859375, :y1 314.281494140625, :x2 847.38671875, :y2 333.99951171875, :width 1060.8, :height 1372.8} {:x1 0, :y1 846.5565795898438, :x2 0, :y2 865.7268676757812, :width 1060.8, :height 1372.8}), :page 1}, :content {:text "Typically, software engineers create and customize KPI dashboards to aid their daily operations"}, :properties {:color "green"}} {:id #uuid "635edd55-67ea-466e-b350-47680c4e64df", :page 1, :position {:bounding {:x1 0, :y1 314.281494140625, :x2 976.0368041992188, :y2 897.7344360351562, :width 1060.8, :height 1372.8}, :rects ({:x1 879.986572265625, :y1 314.281494140625, :x2 974.2818603515625, :y2 333.99951171875, :width 1060.8, :height 1372.8} {:x1 540.7562255859375, :y1 335.0008239746094, :x2 976.0368041992188, :y2 354.7188415527344, :width 1060.8, :height 1372.8} {:x1 540.7562255859375, :y1 355.72869873046875, :x2 662.970703125, :y2 375.44671630859375, :width 1060.8, :height 1372.8} {:x1 0, :y1 862.5603637695312, :x2 0, :y2 881.7306518554688, :width 1060.8, :height 1372.8} {:x1 0, :y1 878.5641479492188, :x2 0, :y2 897.7344360351562, :width 1060.8, :height 1372.8}), :page 1}, :content {:text "PI dashboard is a group of selected KPIs that are organized together in a Web-hosted site."}, :properties {:color "green"}} {:id #uuid "635edd8b-eff0-441f-b12a-0c6424cc558f", :page 1, :position {:bounding {:x1 0, :y1 531.8643188476562, :x2 975.7302856445312, :y2 1041.768310546875, :width 1060.8, :height 1372.8}, :rects ({:x1 893.1273193359375, :y1 531.8643188476562, :x2 975.7302856445312, :y2 551.5823364257812, :width 1060.8, :height 1372.8} {:x1 575.4595947265625, :y1 552.5836181640625, :x2 621.407470703125, :y2 572.3016357421875, :width 1060.8, :height 1372.8} {:x1 0, :y1 1022.5980834960938, :x2 0, :y2 1041.768310546875, :width 1060.8, :height 1372.8}), :page 1}, :content {:text "over 500TB a day."}, :properties {:color "green"}} {:id #uuid "635edd99-4dfc-4770-8832-fb87fed32dcb", :page 1, :position {:bounding {:x1 0, :y1 594.0308227539062, :x2 976.0196533203125, :y2 1089.7796630859375, :width 1060.8, :height 1372.8}, :rects ({:x1 622.0073852539062, :y1 594.0308227539062, :x2 976.0196533203125, :y2 613.7488403320312, :width 1060.8, 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change as the system evolves."}, :properties {:color "purple"}} {:id #uuid "635eddd9-ad48-444a-8109-895c62ce1b6f", :page 1, :position {:bounding {:x1 0, :y1 739.0831298828125, :x2 976.0172119140625, :y2 1217.809814453125, :width 1060.8, :height 1372.8}, :rects ({:x1 843.6411743164062, :y1 739.0831298828125, :x2 975.7365112304688, :y2 758.8011474609375, :width 1060.8, :height 1372.8} {:x1 575.4595947265625, :y1 759.81103515625, :x2 976.0172119140625, :y2 779.529052734375, :width 1060.8, :height 1372.8} {:x1 575.4595947265625, :y1 780.5303344726562, :x2 729.4849853515625, :y2 800.2483520507812, :width 1060.8, :height 1372.8} {:x1 0, :y1 1182.6357421875, :x2 0, :y2 1201.8060302734375, :width 1060.8, :height 1372.8} {:x1 0, :y1 1198.6395263671875, :x2 0, :y2 1217.809814453125, :width 1060.8, :height 1372.8}), :page 1}, :content {:text "software engineers to frequently update their KPI dashboards, which can be especially exhausting."}, :properties {:color "purple"}} {:id #uuid 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Baradari, M. Shoar, N. Nezafati, and M. Motadel, “A new approach for kpi ranking and selection in itil processes: Using simultaneous evaluation of criteria and alternatives (seca),” Journal of Industrial Engineering and Management Studies, vol. 8, no. 1, pp. 152–179, 2021."}, :properties {:color "green"}} {:id #uuid "635ede5b-0f4a-446c-a664-869b4f516f10", :page 11, :position {:bounding {:x1 0, :y1 1134.9667358398438, :x2 976.0303344726562, :y2 2274.05859375, :width 1060.8, :height 1372.8}, :rects ({:x1 544.5870971679688, :y1 1134.9667358398438, :x2 975.3265380859375, :y2 1150.302978515625, :width 1060.8, :height 1372.8} {:x1 572.404296875, :y1 1150.5083618164062, :x2 976.0303344726562, :y2 1165.8446044921875, :width 1060.8, :height 1372.8} {:x1 572.404296875, :y1 1166.0499877929688, :x2 975.9852905273438, :y2 1181.38623046875, :width 1060.8, :height 1372.8} {:x1 572.404296875, :y1 1181.5916137695312, :x2 881.67236328125, :y2 1196.9278564453125, :width 1060.8, :height 1372.8} {:x1 0, :y1 2222.8807373046875, :x2 0, :y2 2242.051025390625, :width 1060.8, :height 1372.8} {:x1 0, :y1 2238.884521484375, :x2 0, :y2 2258.0548095703125, :width 1060.8, :height 1372.8} {:x1 0, :y1 2254.8883056640625, :x2 0, :y2 2274.05859375, :width 1060.8, :height 1372.8}), :page 11}, :content {:text "30] I. Baradari, M. Shoar, N. Nezafati, and M. Motadel, “A new approach for kpi ranking and selection in itil processes: Using simultaneous evaluation of criteria and alternatives (seca),” Journal of Industrial Engineering and Management Studies, vol. 8, no. 1, pp. 152–179, 2021"}, :properties {:color "green"}} {:id #uuid "635ede60-f316-4105-aa0d-cd67e1f7fef3", :page 11, :position {:bounding {:x1 0, :y1 1134.9667358398438, :x2 976.0303344726562, :y2 2274.05859375, :width 1060.8, :height 1372.8}, :rects ({:x1 575.9403686523438, :y1 1134.9667358398438, :x2 975.3265380859375, :y2 1150.302978515625, :width 1060.8, :height 1372.8} {:x1 572.404296875, :y1 1150.5083618164062, :x2 976.0303344726562, :y2 1165.8446044921875, :width 1060.8, :height 1372.8} {:x1 572.404296875, :y1 1166.0499877929688, :x2 975.9852905273438, :y2 1181.38623046875, :width 1060.8, :height 1372.8} {:x1 572.404296875, :y1 1181.5916137695312, :x2 881.67236328125, :y2 1196.9278564453125, :width 1060.8, :height 1372.8} {:x1 0, :y1 2222.8807373046875, :x2 0, :y2 2242.051025390625, :width 1060.8, :height 1372.8} {:x1 0, :y1 2238.884521484375, :x2 0, :y2 2258.0548095703125, :width 1060.8, :height 1372.8} {:x1 0, :y1 2254.8883056640625, :x2 0, :y2 2274.05859375, :width 1060.8, :height 1372.8}), :page 11}, :content {:text ". Baradari, M. Shoar, N. Nezafati, and M. Motadel, “A new approach for kpi ranking and selection in itil processes: Using simultaneous evaluation of criteria and alternatives (seca),” Journal of Industrial Engineering and Management Studies, vol. 8, no. 1, pp. 152–179, 2021"}, :properties {:color "green"}} {:id #uuid "635ede84-bf11-45d1-8ea7-3a3138d8274b", :page 1, :position {:bounding {:x1 544.438232421875, :y1 1226.0598754882812, :x2 976.0333251953125, :y2 1245.7778930664062, :width 1060.8, :height 1372.8}, :rects ({:x1 544.438232421875, :y1 1226.0598754882812, :x2 976.0333251953125, :y2 1245.7778930664062, :width 1060.8, :height 1372.8}), :page 1}, :content {:text "atency, traffic pressure, error count and saturation as KPIs."}, :properties {:color "green"}} {:id #uuid "635ede9e-ae82-4419-8f38-9434dd4c2084", :page 2, :position {:bounding {:x1 0, :y1 30.364349365234375, :x2 520.1382446289062, :y2 168.21926879882812, :width 1060.8, :height 1372.8}, :rects ({:x1 0, :y1 30.364349365234375, :x2 0, :y2 49.534637451171875, :width 1060.8, :height 1372.8} {:x1 395.14019775390625, :y1 127.78195190429688, :x2 520.1382446289062, :y2 147.49996948242188, :width 1060.8, :height 1372.8} {:x1 84.8627700805664, :y1 148.50125122070312, :x2 154.70045471191406, :y2 168.21926879882812, :width 1060.8, :height 1372.8}), :page 2}, :content {:text "recommendations are static"}, :properties {:color "purple"}} {:id #uuid "635edef5-50be-4055-b226-367d68808acf", :page 2, :position {:bounding {:x1 0, :y1 206.40582275390625, :x2 520.142951965332, :y2 438.6744689941406, :width 1060.8, :height 1372.8}, :rects ({:x1 0, :y1 206.40582275390625, :x2 0, :y2 225.57611083984375, :width 1060.8, :height 1372.8} {:x1 0, :y1 222.40960693359375, :x2 0, :y2 241.57989501953125, :width 1060.8, :height 1372.8} {:x1 0, :y1 238.41336059570312, :x2 0, :y2 257.5836486816406, :width 1060.8, :height 1372.8} {:x1 197.2010498046875, :y1 356.7898864746094, :x2 520.1378173828125, :y2 376.5079040527344, :width 1060.8, :height 1372.8} {:x1 84.8627700805664, :y1 377.5092468261719, :x2 520.1398391723633, :y2 397.2272644042969, :width 1060.8, :height 1372.8} {:x1 84.8627700805664, :y1 398.2370910644531, :x2 520.142951965332, :y2 417.9551086425781, :width 1060.8, :height 1372.8} {:x1 84.8627700805664, :y1 418.9564514160156, :x2 218.20191192626953, :y2 438.6744689941406, :width 1060.8, :height 1372.8}), :page 2}, :content {:text "require continuous labelling of the anomaly time to supervisedly learn the correlation between metrics and real faults, which is still very labor-intensive and difficult to apply in practice. "}, :properties {:color "green"}} {:id #uuid "635edf0a-9d82-4d81-9e7a-710eba058112", :page 2, :position {:bounding {:x1 0, :y1 302.4284439086914, :x2 520.141487121582, :y2 543.3493423461914, :width 1060.8, :height 1372.8}, :rects ({:x1 0, :y1 302.4284439086914, :x2 0, :y2 321.5987319946289, :width 1060.8, :height 1372.8} {:x1 0, :y1 318.4322280883789, :x2 0, :y2 337.6025161743164, :width 1060.8, :height 1372.8} {:x1 447.7079772949219, :y1 482.1926956176758, :x2 520.1387329101562, :y2 501.9107131958008, :width 1060.8, :height 1372.8} {:x1 84.8627700805664, :y1 502.91202545166016, :x2 520.141487121582, :y2 522.6300430297852, :width 1060.8, :height 1372.8} {:x1 84.8627700805664, :y1 523.6313552856445, :x2 363.6885757446289, :y2 543.3493423461914, :width 1060.8, :height 1372.8}), :page 2}, :content {:text "this paper proposes to automatically recommend metrics against actual conditions with minimal human effort. "}, :properties {:color "green"}} {:id #uuid "635edf9e-8117-45de-9587-6ab7d166bd66", :page 2, :position {:bounding {:x1 0, :y1 350.43975830078125, :x2 520.0458984375, :y2 584.7965393066406, :width 1060.8, :height 1372.8}, :rects ({:x1 0, :y1 350.43975830078125, :x2 0, :y2 369.61004638671875, :width 1060.8, :height 1372.8} {:x1 460.55767822265625, :y1 544.3592224121094, :x2 520.0458984375, :y2 564.0772399902344, :width 1060.8, :height 1372.8} {:x1 84.8627700805664, :y1 565.0785522460938, :x2 287.4201126098633, :y2 584.7965393066406, :width 1060.8, :height 1372.8}), :page 2}, :content {:text "anomaly detection and fault diagnosis"}, :properties {:color "purple"}} {:id #uuid "635edfb5-19b7-4927-add1-fb11d172a3f7", :page 2, :position {:bounding {:x1 0, :y1 398.45106506347656, :x2 520.1360549926758, :y2 667.6824035644531, :width 1060.8, :height 1372.8}, :rects ({:x1 0, :y1 398.45106506347656, :x2 0, :y2 417.621337890625, :width 1060.8, :height 1372.8} {:x1 0, :y1 414.454833984375, :x2 0, 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Using Metrics for Failure Management"}, :properties {:color "purple"}} {:id #uuid "635ee30d-4573-4246-8535-090fb007b673", :page 3, :position {:bounding {:x1 220.84909057617188, :y1 537.9149322509766, :x2 366.5909118652344, :y2 557.6329345703125, :width 1060.8, :height 1372.8}, :rects ({:x1 220.84909057617188, :y1 537.9149322509766, :x2 366.5909118652344, :y2 557.6329345703125, :width 1060.8, :height 1372.8}), :page 3}, :content {:text "Anomaly Detection"}, :properties {:color "purple"}} {:id #uuid "635ee328-943c-406c-bbbd-de0d4da90474", :page 3, :position {:bounding {:x1 0, :y1 590.4963073730469, :x2 520.1372680664062, :y2 932.0697631835938, :width 1060.8, :height 1372.8}, :rects ({:x1 0, :y1 590.4963073730469, :x2 0, :y2 609.6665954589844, :width 1060.8, :height 1372.8} {:x1 166.91677856445312, :y1 891.6239013671875, :x2 520.1372680664062, :y2 911.3419189453125, :width 1060.8, :height 1372.8} {:x1 84.8627700805664, :y1 912.3517456054688, :x2 337.07447052001953, :y2 932.0697631835938, :width 1060.8, :height 1372.8}), :page 3}, :content {:text "selecting useful metrics to assess the symptom of a fault needs to be conducted first"}, :properties {:color "green"}} {:id #uuid "635ee456-82bb-4a80-8e10-99a250bc2e18", :page 3, :position {:bounding {:x1 143.95021057128906, :y1 1205.340576171875, :x2 356.8972473144531, :y2 1225.05859375, :width 1060.8, :height 1372.8}, :rects ({:x1 143.95021057128906, :y1 1205.340576171875, :x2 356.8972473144531, :y2 1225.05859375, :width 1060.8, :height 1372.8}), :page 3}, :content {:text "intelligent anomaly detection"}, :properties {:color "green"}} {:id #uuid "635ee459-2d0a-4d72-86e2-8bcd5316a14d", :page 3, :position {:bounding {:x1 394.4225158691406, :y1 1205.340576171875, :x2 502.31390380859375, :y2 1225.05859375, :width 1060.8, :height 1372.8}, :rects ({:x1 394.4225158691406, :y1 1205.340576171875, :x2 502.31390380859375, :y2 1225.05859375, :width 1060.8, :height 1372.8}), :page 3}, :content {:text "fault diagnosis"}, :properties {:color "green"}} {:id #uuid "635ee48d-203a-40e8-9723-ede05a6a4159", :page 3, :position {:bounding {:x1 0, :y1 793.9324493408203, :x2 976.0372314453125, :y2 1537.8851470947266, :width 1060.8, :height 1372.8}, :rects ({:x1 593.5186157226562, :y1 793.9324493408203, :x2 976.0372314453125, :y2 813.6504669189453, :width 1060.8, :height 1372.8} {:x1 540.7562255859375, :y1 814.6602935791016, :x2 842.714111328125, :y2 834.3783111572266, :width 1060.8, :height 1372.8} {:x1 0, :y1 1518.714859008789, :x2 0, :y2 1537.8851470947266, :width 1060.8, :height 1372.8}), :page 3}, :content {:text "It can be observed that more than three quarters of KPI dashboards have less than 25 metrics."}, :properties {:color "green"}} {:id #uuid "635ee4c0-ddcc-48a2-aa82-b00ab11e27ce", :page 3, :position {:bounding {:x1 0, :y1 1039.5603332519531, :x2 975.7517700195312, :y2 1713.9266357421875, :width 1060.8, :height 1372.8}, :rects ({:x1 815.3583984375, :y1 1039.5603332519531, :x2 975.7517700195312, :y2 1059.2783203125, :width 1060.8, :height 1372.8} {:x1 540.7562255859375, :y1 1060.2796325683594, :x2 789.0729370117188, :y2 1079.9976501464844, :width 1060.8, :height 1372.8} {:x1 0, :y1 1694.75634765625, :x2 0, :y2 1713.9266357421875, :width 1060.8, :height 1372.8}), :page 3}, :content {:text "many metrics are noisy or contain redundant information. "}, :properties {:color "green"}} {:id #uuid "635ee4e1-92fa-470d-8879-db295d39eae7", :page 3, :position {:bounding {:x1 0, :y1 1163.8933410644531, :x2 976.0341186523438, :y2 1841.956787109375, :width 1060.8, :height 1372.8}, :rects ({:x1 623.0674438476562, :y1 1163.8933410644531, :x2 976.0341186523438, :y2 1183.6113586425781, :width 1060.8, :height 1372.8} {:x1 540.7562255859375, :y1 1184.6126708984375, :x2 976.0317993164062, :y2 1204.3306884765625, :width 1060.8, :height 1372.8} {:x1 540.7562255859375, :y1 1205.3405456542969, :x2 976.0336303710938, :y2 1225.0585632324219, :width 1060.8, :height 1372.8} {:x1 540.7562255859375, :y1 1226.0598449707031, :x2 684.3494262695312, :y2 1245.7778625488281, :width 1060.8, :height 1372.8} {:x1 0, :y1 1790.7789306640625, :x2 0, :y2 1809.94921875, :width 1060.8, :height 1372.8} {:x1 0, :y1 1806.78271484375, :x2 0, :y2 1825.9530029296875, :width 1060.8, :height 1372.8} {:x1 0, :y1 1822.7864990234375, :x2 0, :y2 1841.956787109375, :width 1060.8, :height 1372.8}), :page 3}, :content {:text "uch a practice is not automatic, and there is still no a unified standard for selecting KPIs, rendering it difficult to release the full potential of these anomaly detection methods in different systems."}, :properties {:color "purple"}} {:id #uuid "635ee522-7f76-4ee3-9a0e-00f6639dcb88", :page 4, :position {:bounding {:x1 0, :y1 206.40582275390625, :x2 520.135009765625, :y2 338.38128662109375, :width 1060.8, :height 1372.8}, :rects ({:x1 0, :y1 206.40582275390625, :x2 0, :y2 225.57611083984375, :width 1060.8, :height 1372.8} {:x1 458.4952392578125, :y1 297.9439697265625, :x2 520.135009765625, :y2 317.6619873046875, :width 1060.8, :height 1372.8} {:x1 84.8627700805664, :y1 318.66326904296875, :x2 378.59244537353516, :y2 338.38128662109375, :width 1060.8, :height 1372.8}), :page 4}, :content {:text "ngineers tend to use more metrics for analysis. 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"}, :properties {:color "yellow"}} {:id #uuid "635ee61c-6414-4ac3-83f2-0df2fcbdfe75", :page 4, :position {:bounding {:x1 102.13314819335938, :y1 873.4634628295898, :x2 418.43450927734375, :y2 893.1814804077148, :width 1060.8, :height 1372.8}, :rects ({:x1 102.13314819335938, :y1 873.4634628295898, :x2 418.43450927734375, :y2 893.1814804077148, :width 1060.8, :height 1372.8}), :page 4}, :content {:text "Metric Selection for Anomaly Detection."}, :properties {:color "green"}} {:id #uuid "635ee62c-4cc7-4fc7-9eb2-ab3f558c2a60", :page 4, :position {:bounding {:x1 0, :y1 702.5226669311523, :x2 520.1367874145508, :y2 1058.9616928100586, :width 1060.8, :height 1372.8}, :rects ({:x1 0, :y1 702.5226669311523, :x2 0, :y2 721.6929550170898, :width 1060.8, :height 1372.8} {:x1 0, :y1 718.5264511108398, :x2 0, :y2 737.6967391967773, :width 1060.8, :height 1372.8} {:x1 492.4586181640625, :y1 997.7964706420898, :x2 520.0453491210938, :y2 1017.5144882202148, :width 1060.8, :height 1372.8} {:x1 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"}, :properties {:color "green"}} {:id #uuid "635ee706-faef-4fff-bc32-af9f2b4fe1d5", :page 4, :position {:bounding {:x1 752.3237915039062, :y1 1163.9874877929688, :x2 844.6102905273438, :y2 1183.7055053710938, :width 1060.8, :height 1372.8}, :rects ({:x1 752.3237915039062, :y1 1163.9874877929688, :x2 844.6102905273438, :y2 1183.7055053710938, :width 1060.8, :height 1372.8}), :page 4}, :content {:text "redundancy "}, :properties {:color "purple"}} {:id #uuid "635ee70d-8ab9-42db-831c-497f3bcc102d", :page 4, :position {:bounding {:x1 567.6128540039062, :y1 1184.7153930664062, :x2 681.29833984375, :y2 1204.4334106445312, :width 1060.8, :height 1372.8}, :rects ({:x1 567.6128540039062, :y1 1184.7153930664062, :x2 681.29833984375, :y2 1204.4334106445312, :width 1060.8, :height 1372.8}), :page 4}, :content {:text "pattern diversity"}, :properties {:color "purple"}} {:id #uuid "635ee87e-a503-4548-8663-ea43f8e18ab5", :page 5, :position {:bounding {:x1 0, :y1 110.38321304321289, :x2 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unmonitored anomaly-related metric retrieval for fault diagnosis."}, :properties {:color "purple"}} {:id #uuid "635ee8aa-f201-4ecc-8247-6a34e5145510", :page 5, :position {:bounding {:x1 0, :y1 318.4322052001953, :x2 520.1358642578125, :y2 1162.8920288085938, :width 1060.8, :height 1372.8}, :rects ({:x1 0, :y1 318.4322052001953, :x2 0, :y2 337.6024971008301, :width 1060.8, :height 1372.8} {:x1 344.89080810546875, :y1 1122.4461669921875, :x2 520.1358642578125, :y2 1142.1641845703125, :width 1060.8, :height 1372.8} {:x1 84.8627700805664, :y1 1143.1740112304688, :x2 428.64759063720703, :y2 1162.8920288085938, :width 1060.8, :height 1372.8}), :page 5}, :content {:text "dopt anomaly detection techniques to identify anomalies in these metrics"}, :properties {:color "green"}} {:id #uuid "635ee8c8-9b94-4a0f-912b-519ee19122dd", :page 5, :position {:bounding {:x1 0, :y1 334.4359893798828, :x2 976.03515625, :y2 1245.7778625488281, :width 1060.8, :height 1372.8}, :rects ({:x1 0, :y1 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However, it is difficult to ensure that metrics can be selected completely and accurately. The engineers also need a service to assist them in automatically selecting metrics that can characterize the availability of their systems. Hence, we first design a metric selection method for monitoring and detecting anomalies. In addition, the unmonitored metrics can also assist in revealing and diagnosing anomalies. There are far more unmonitored metrics than monitored metrics in an industrial environment. Adopting only monitored metrics may make it difficult to diagnose a fault. Therefore, we design an anomaly-related unmonitored metric retrieval method for fault diagnosis."}, :properties {:color "red"}} {:id #uuid "635ee94e-82f2-49b4-ba57-f5de2b167576", :page 5, :position {:bounding {:x1 0, :y1 622.503849029541, :x2 975.7405395507812, :y2 1017.8311805725098, :width 1060.8, :height 1372.8}, :rects ({:x1 0, :y1 622.503849029541, :x2 0, :y2 641.6741371154785, :width 1060.8, :height 1372.8} {:x1 656.9187622070312, :y1 977.3938026428223, :x2 975.7405395507812, :y2 997.1118202209473, :width 1060.8, :height 1372.8} {:x1 540.7562255859375, :y1 998.1131629943848, :x2 840.2804565429688, :y2 1017.8311805725098, :width 1060.8, :height 1372.8}), :page 5}, :content {:text " we adopt the metric selection service to recommend KPIs which should be monitored. "}, :properties {:color "red"}} {:id #uuid "635ee991-28fb-4310-9e08-4d88344cbde4", :page 5, :position {:bounding {:x1 0, :y1 782.5415344238281, :x2 976.0338134765625, :y2 1245.7778930664062, :width 1060.8, :height 1372.8}, :rects ({:x1 0, :y1 782.5415344238281, :x2 0, :y2 801.7117919921875, :width 1060.8, :height 1372.8} {:x1 0, :y1 798.5452880859375, :x2 0, :y2 817.715576171875, :width 1060.8, :height 1372.8} {:x1 842.8043212890625, :y1 1184.6126708984375, :x2 976.0338134765625, :y2 1204.3306884765625, :width 1060.8, :height 1372.8} {:x1 540.7562255859375, :y1 1205.340576171875, :x2 976.033203125, :y2 1225.05859375, :width 1060.8, :height 1372.8} {:x1 540.7562255859375, :y1 1226.0598754882812, :x2 601.8865966796875, :y2 1245.7778930664062, :width 1060.8, :height 1372.8}), :page 5}, :content {:text "we use the metric retrieval service to identify the unmonitored anomaly-related metrics. "}, :properties {:color "green"}} {:id #uuid "635ee99e-49d2-45cf-95a2-0f77f791b335", :page 6, :position {:bounding {:x1 112.12602996826172, :y1 86.33477783203125, :x2 373.84200286865234, :y2 106.05279541015625, :width 1060.8, :height 1372.8}, :rects ({:x1 112.12602996826172, :y1 86.33477783203125, :x2 373.84200286865234, :y2 106.05279541015625, :width 1060.8, :height 1372.8}), :page 6}, :content {:text "unmonitored anomaly-related metric"}, :properties {:color "yellow"}} {:id #uuid "635eea9b-fff2-4965-8947-0c66c0d48ec2", :page 6, :position {:bounding {:x1 0, :y1 110.38320922851562, :x2 520.139778137207, :y2 312.4585876464844, :width 1060.8, :height 1372.8}, :rects ({:x1 0, :y1 110.38320922851562, :x2 0, :y2 129.55349731445312, :width 1060.8, :height 1372.8} {:x1 0, :y1 126.38699340820312, :x2 0, :y2 145.55728149414062, :width 1060.8, :height 1372.8} {:x1 327.447509765625, :y1 251.29342651367188, :x2 520.1370849609375, :y2 271.0114440917969, :width 1060.8, :height 1372.8} {:x1 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design a new unsupervised solution in practice."}, :properties {:color "purple"}} {:id #uuid "635eead5-a422-4861-ac27-f6d8ab79453f", :page 6, :position {:bounding {:x1 0, :y1 446.4623622894287, :x2 520.1377639770508, :y2 747.8552761077881, :width 1060.8, :height 1372.8}, :rects ({:x1 0, :y1 446.4623622894287, :x2 0, :y2 465.6326503753662, :width 1060.8, :height 1372.8} {:x1 0, :y1 462.4661464691162, :x2 0, :y2 481.6364345550537, :width 1060.8, :height 1372.8} {:x1 218.441162109375, :y1 686.6985988616943, :x2 520.1372680664062, :y2 706.4166164398193, :width 1060.8, :height 1372.8} {:x1 84.8627700805664, :y1 707.4179592132568, :x2 520.1377639770508, :y2 727.1359767913818, :width 1060.8, :height 1372.8} {:x1 84.8627700805664, :y1 728.1372585296631, :x2 357.9180374145508, :y2 747.8552761077881, :width 1060.8, :height 1372.8}), :page 6}, :content {:text "Eventually, we can construct the anomaly graph of metrics based on the metric anomaly relationships and select the KPIs via graph learning"}, :properties {:color "purple"}} {:id #uuid "635eeb08-6cba-4796-b180-567563da78f5", :page 6, :position {:bounding {:x1 0, :y1 558.4887390136719, :x2 520.142463684082, :y2 893.1472473144531, :width 1060.8, :height 1372.8}, :rects ({:x1 0, :y1 558.4887390136719, :x2 0, :y2 577.6590270996094, :width 1060.8, :height 1372.8} {:x1 0, :y1 574.4925231933594, :x2 0, :y2 593.6628112792969, :width 1060.8, :height 1372.8} {:x1 426.41192626953125, :y1 831.9905700683594, :x2 519.841796875, :y2 851.7085876464844, :width 1060.8, :height 1372.8} {:x1 84.8627700805664, :y1 852.7099304199219, :x2 520.142463684082, :y2 872.4279479980469, :width 1060.8, :height 1372.8} {:x1 84.8627700805664, :y1 873.4292297363281, :x2 405.7341995239258, :y2 893.1472473144531, :width 1060.8, :height 1372.8}), :page 6}, :content {:text "we adopt the detection results to construct the graph which can represent the topological relationships between metrics"}, :properties {:color "purple"}} {:id #uuid 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"635eebbd-2cd0-4687-a14e-993d0b1b4e27", :page 6, :position {:bounding {:x1 0, :y1 584.5483551025391, :x2 976.031494140625, :y2 1441.862564086914, :width 1060.8, :height 1372.8}, :rects ({:x1 736.9261474609375, :y1 584.5483551025391, :x2 975.7555541992188, :y2 604.2663726806641, :width 1060.8, :height 1372.8} {:x1 540.7562255859375, :y1 605.2762298583984, :x2 976.031494140625, :y2 624.9942779541016, :width 1060.8, :height 1372.8} {:x1 540.7562255859375, :y1 625.9955596923828, :x2 745.6054077148438, :y2 645.7135772705078, :width 1060.8, :height 1372.8} {:x1 0, :y1 1406.688491821289, :x2 0, :y2 1425.8587799072266, :width 1060.8, :height 1372.8} {:x1 0, :y1 1422.6922760009766, :x2 0, :y2 1441.862564086914, :width 1060.8, :height 1372.8}), :page 6}, :content {:text "we prune the anomaly graph based on the similarity between metrics and remove edges between metrics with similar shapes. "}, :properties {:color "green"}} {:id #uuid "635eebc9-d73d-498d-86d2-e8c3eb5b76d7", :page 6, :position {:bounding {:x1 0, :y1 625.9955596923828, :x2 976.0353393554688, :y2 1521.881362915039, :width 1060.8, :height 1372.8}, :rects ({:x1 855.8026123046875, :y1 625.9955596923828, :x2 976.0303344726562, :y2 645.7135772705078, :width 1060.8, :height 1372.8} {:x1 540.7562255859375, :y1 646.7148590087891, :x2 976.0353393554688, :y2 666.4328765869141, :width 1060.8, :height 1372.8} {:x1 540.7562255859375, :y1 667.4427642822266, :x2 976.0308227539062, :y2 687.1607818603516, :width 1060.8, :height 1372.8} {:x1 540.7562255859375, :y1 688.1620635986328, :x2 976.03369140625, :y2 707.8800811767578, :width 1060.8, :height 1372.8} {:x1 540.7562255859375, :y1 708.8813629150391, :x2 976.0313110351562, :y2 728.5993804931641, :width 1060.8, :height 1372.8} {:x1 540.7562255859375, :y1 729.6092681884766, :x2 817.7477416992188, :y2 749.3272857666016, :width 1060.8, :height 1372.8} {:x1 0, :y1 1438.696060180664, :x2 0, :y2 1457.8663482666016, :width 1060.8, :height 1372.8} {:x1 0, :y1 1454.6998443603516, :x2 0, :y2 1473.870132446289, :width 1060.8, :height 1372.8} {:x1 0, :y1 1470.703628540039, :x2 0, :y2 1489.8739166259766, :width 1060.8, :height 1372.8} {:x1 0, :y1 1486.7074127197266, :x2 0, :y2 1505.877700805664, :width 1060.8, :height 1372.8} {:x1 0, :y1 1502.7110748291016, :x2 0, :y2 1521.881362915039, :width 1060.8, :height 1372.8}), :page 6}, :content {:text "e adopt dynamic time warping (DTW) [38] algorithm to compute the similarity. If the DTW distance of two metrics is lower than a relevant threshold, we will remove the edge between them. Eventually, we will obtain a pruned anomaly graph which can characterize the anomaly relations between metrics."}, :properties {:color "purple"}} {:id #uuid "635eebdb-e77d-456a-bea3-98efcd8b74a3", :page 6, :position {:bounding {:x1 558.026611328125, :y1 751.7663726806641, :x2 806.8482666015625, :y2 771.4843902587891, :width 1060.8, :height 1372.8}, :rects ({:x1 558.026611328125, :y1 751.7663726806641, :x2 806.8482666015625, :y2 771.4843902587891, :width 1060.8, :height 1372.8}), :page 6}, :content {:text "3) Random walk to select metrics:"}, :properties {:color "purple"}} {:id #uuid "635eec25-14cf-45c1-b304-2e40f302a2bc", :page 7, :position {:bounding {:x1 0, :y1 78.37568664550781, :x2 520.1435623168945, :y2 257.3012237548828, :width 1060.8, :height 1372.8}, :rects ({:x1 0, :y1 78.37568664550781, :x2 0, :y2 97.54597473144531, :width 1060.8, :height 1372.8} {:x1 0, :y1 94.37944030761719, :x2 0, :y2 113.54972839355469, :width 1060.8, :height 1372.8} {:x1 389.48822021484375, :y1 196.1360321044922, :x2 520.1366577148438, :y2 215.8540496826172, :width 1060.8, :height 1372.8} {:x1 84.8627700805664, :y1 216.85536193847656, :x2 520.1435623168945, :y2 236.57334899902344, :width 1060.8, :height 1372.8} {:x1 84.8627700805664, :y1 237.58323669433594, :x2 491.06290435791016, :y2 257.3012237548828, :width 1060.8, :height 1372.8}), :page 7}, :content {:text "We group metrics with similar implications into one category, and then adopt relevant classification approach to classify the metrics. "}, :properties {:color "green"}} {:id #uuid "635eec66-8037-4c71-95a8-ba843d570dea", :page 7, :position {:bounding {:x1 779.20703125, :y1 1042.6583709716797, :x2 819.0790405273438, :y2 1062.3763885498047, :width 1060.8, :height 1372.8}, :rects ({:x1 779.20703125, :y1 1042.6583709716797, :x2 819.0790405273438, :y2 1062.3763885498047, :width 1060.8, :height 1372.8}), :page 7}, :content {:text "MSG "}, :properties {:color "purple"}} {:id #uuid "635eec6f-f65f-4a9d-a7fd-74d258f5bdae", :page 7, :position {:bounding {:x1 936.0484008789062, :y1 1063.3777313232422, :x2 976.031982421875, :y2 1083.0957489013672, :width 1060.8, :height 1372.8}, :rects ({:x1 936.0484008789062, :y1 1063.3777313232422, :x2 976.031982421875, :y2 1083.0957489013672, :width 1060.8, :height 1372.8}), :page 7}, :content {:text "MRG"}, :properties {:color "purple"}} {:id #uuid "635eecb3-fe18-4027-ba1f-c54bd8c5812e", :page 8, :position {:bounding {:x1 0, :y1 366.4435272216797, :x2 520.13818359375, :y2 537.7437591552734, :width 1060.8, :height 1372.8}, :rects ({:x1 0, :y1 366.4435272216797, :x2 0, :y2 385.6138153076172, :width 1060.8, :height 1372.8} {:x1 303.8603210449219, :y1 497.3064422607422, :x2 520.13818359375, :y2 517.0244598388672, :width 1060.8, :height 1372.8} {:x1 84.8627700805664, :y1 518.0257415771484, :x2 313.6087112426758, :y2 537.7437591552734, :width 1060.8, :height 1372.8}), :page 8}, :content {:text " there is no unified evaluation standard for selecting metrics."}, :properties {:color "green"}} {:id #uuid "635eecdc-e156-4703-807c-78a9651c70b7", :page 8, :position {:bounding {:x1 0, :y1 622.5038299560547, :x2 520.1283721923828, :y2 890.2289276123047, :width 1060.8, :height 1372.8}, :rects ({:x1 0, :y1 622.5038299560547, :x2 0, :y2 641.6741180419922, :width 1060.8, :height 1372.8} {:x1 0, :y1 638.5076141357422, :x2 0, :y2 657.6779022216797, :width 1060.8, :height 1372.8} {:x1 262.4261474609375, :y1 829.0637054443359, :x2 520.051513671875, :y2 848.7817230224609, :width 1060.8, :height 1372.8} {:x1 84.8627700805664, :y1 849.7916107177734, :x2 520.1283721923828, :y2 869.5095672607422, :width 1060.8, :height 1372.8} {:x1 84.8627700805664, :y1 870.5109100341797, :x2 356.62842559814453, :y2 890.2289276123047, :width 1060.8, :height 1372.8}), :page 8}, :content {:text "the exporter has collected 56 metrics from 9 perspectives which includes disk usage, i/o usage, memory usage, CPU usage, and so on. "}, :properties {:color "green"}} {:id #uuid "635eed00-350e-4a2d-bdf0-6c9d964c9b54", :page 8, :position {:bounding {:x1 0, :y1 846.5565795898438, :x2 520.141357421875, :y2 1100.426025390625, :width 1060.8, :height 1372.8}, :rects ({:x1 0, :y1 846.5565795898438, :x2 0, :y2 865.7269287109375, :width 1060.8, :height 1372.8} {:x1 362.9403991699219, :y1 1059.9801635742188, :x2 520.141357421875, :y2 1079.6981201171875, :width 1060.8, :height 1372.8} {:x1 84.8627700805664, :y1 1080.7080078125, :x2 205.1991729736328, :y2 1100.426025390625, :width 1060.8, :height 1372.8}), :page 8}, :content {:text "effectiveness of metric selection method"}, :properties {:color "green"}} {:id #uuid "635eed17-81e4-47e2-b7e5-03cccb34d9e7", :page 8, :position {:bounding {:x1 394.9757080078125, :y1 1226.0598754882812, :x2 466.63543701171875, :y2 1245.77783203125, :width 1060.8, :height 1372.8}, :rects ({:x1 394.9757080078125, :y1 1226.0598754882812, :x2 466.63543701171875, :y2 1245.77783203125, :width 1060.8, :height 1372.8}), :page 8}, :content {:text "e counted"}, :properties {:color "red"}} {:id #uuid "635eed3a-e7c3-4b85-bd08-fb3a41810567", :page 8, :position {:bounding {:x1 0, :y1 579.5333099365234, :x2 976.0314331054688, :y2 1409.8550872802734, :width 1060.8, :height 1372.8}, :rects ({:x1 583.2667236328125, :y1 579.5333099365234, :x2 976.0314331054688, :y2 599.2512664794922, :width 1060.8, :height 1372.8} {:x1 540.7562255859375, :y1 600.2526092529297, :x2 707.847900390625, :y2 619.9706268310547, :width 1060.8, :height 1372.8} {:x1 0, :y1 1390.684799194336, :x2 0, :y2 1409.8550872802734, :width 1060.8, :height 1372.8}), :page 8}, :content {:text "we use the selected metrics to train model and detect anomalies respectively."}, :properties {:color "purple"}} {:id #uuid "635eed44-7179-4d1d-895b-98db9462f4d6", :page 8, :position {:bounding {:x1 783.8322143554688, :y1 641.6998138427734, :x2 826.3352661132812, :y2 661.4178314208984, :width 1060.8, :height 1372.8}, :rects ({:x1 783.8322143554688, :y1 641.6998138427734, :x2 826.3352661132812, :y2 661.4178314208984, :width 1060.8, :height 1372.8}), :page 8}, :content {:text "MSG "}, :properties {:color "purple"}} {:id #uuid "635eed8e-9f85-40ca-bb78-08c8727ad481", :page 8, :position {:bounding {:x1 0, :y1 911.6842193603516, :x2 976.0314331054688, :y2 1681.9191284179688, :width 1060.8, :height 1372.8}, :rects ({:x1 783.0370483398438, :y1 911.6842193603516, :x2 976.0314331054688, :y2 931.4022369384766, :width 1060.8, :height 1372.8} {:x1 540.7562255859375, :y1 932.4120941162109, :x2 976.0314331054688, :y2 952.1301116943359, :width 1060.8, :height 1372.8} {:x1 540.7562255859375, :y1 953.1314239501953, :x2 663.2180786132812, :y2 972.8494262695312, :width 1060.8, :height 1372.8} {:x1 0, :y1 1646.7450561523438, :x2 0, :y2 1665.9153442382812, :width 1060.8, :height 1372.8} {:x1 0, :y1 1662.748779296875, :x2 0, :y2 1681.9191284179688, :width 1060.8, :height 1372.8}), :page 8}, :content {:text "using more metrics to detect anomalies will result in better performance when randomly selecting metrics."}, :properties {:color "red"}} {:id #uuid "635eedca-2184-439b-b5bb-abe4fc30253f", :page 8, :position {:bounding {:x1 0, :y1 1036.0172424316406, :x2 976.032958984375, :y2 1761.9379272460938, :width 1060.8, :height 1372.8}, :rects ({:x1 793.2960815429688, :y1 1036.0172424316406, :x2 976.032958984375, :y2 1055.7352600097656, :width 1060.8, :height 1372.8} {:x1 540.7562255859375, :y1 1056.7451171875, :x2 777.6907958984375, :y2 1076.463134765625, :width 1060.8, :height 1372.8} {:x1 0, :y1 1742.7676391601562, :x2 0, :y2 1761.9379272460938, :width 1060.8, :height 1372.8}), :page 8}, :content {:text ". In addition, we also find that MSG outperforms baselines"}, :properties {:color "red"}} {:id #uuid "635eede7-f7b1-4726-99c3-b6329312b60b", :page 8, :position {:bounding {:x1 0, :y1 1205.3405151367188, :x2 976.0329923629761, :y2 1873.96435546875, :width 1060.8, :height 1372.8}, :rects ({:x1 634.4397306442261, :y1 1205.3405151367188, :x2 976.0329923629761, :y2 1225.0585327148438, :width 1060.8, :height 1372.8} {:x1 540.7562589645386, :y1 1226.0598449707031, :x2 868.1275358200073, :y2 1245.7778625488281, :width 1060.8, :height 1372.8} {:x1 0, :y1 1854.7940063476562, :x2 0, :y2 1873.96435546875, :width 1060.8, :height 1372.8}), :page 8}, :content {:text "unmonitored metrics will be classified into the same category as those detected as anomalie"}, :properties {:color "green"}} {:id #uuid "635eee45-f0bf-4bda-977a-a277196c0aca", :page 9, :position {:bounding {:x1 188.63968181610107, :y1 891.9747619628906, :x2 294.22632122039795, :y2 911.6927795410156, :width 1060.8, :height 1372.8}, :rects 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1372.8} {:x1 242.3624448776245, :y1 912.6941223144531, :x2 520.1408262252808, :y2 932.4120788574219, :width 1060.8, :height 1372.8} {:x1 84.86276531219482, :y1 933.4219665527344, :x2 520.1432828903198, :y2 953.1399841308594, :width 1060.8, :height 1372.8} {:x1 84.86276531219482, :y1 954.1412658691406, :x2 194.70260906219482, :y2 973.8592834472656, :width 1060.8, :height 1372.8}), :page 9}, :content {:text "If metrics have the same normal and anomalous patterns, we consider that they may belong to the same category"}, :properties {:color "red"}} {:id #uuid "635eee71-263d-4b96-9d86-e84786902f45", :page 9, :position {:bounding {:x1 84.86276531219482, :y1 1226.0598754882812, :x2 394.7682828903198, :y2 1245.7778930664062, :width 1060.8, :height 1372.8}, :rects ({:x1 84.86276531219482, :y1 1226.0598754882812, :x2 394.7682828903198, :y2 1245.7778930664062, :width 1060.8, :height 1372.8}), :page 9}, :content {:text "feature extraction and classification model"}, :properties {:color "purple"}} {:id #uuid "635ef68f-73a0-4e93-96dd-5a79ce6013f2", :page 10, :position {:bounding {:x1 0, :y1 763.1543731689453, :x2 825.7764129638672, :y2 1345.3663330078125, :width 897.6, :height 1161.6000000000001}, :rects ({:x1 472.17677307128906, :y1 763.1543731689453, :x2 825.7764129638672, :y2 779.2209014892578, :width 897.6, :height 1161.6000000000001} {:x1 457.5651397705078, :y1 780.6872100830078, :x2 734.2999420166016, :y2 796.7537231445312, :width 897.6, :height 1161.6000000000001} {:x1 0, :y1 1326.0134887695312, :x2 0, :y2 1345.3663330078125, :width 897.6, :height 1161.6000000000001}), :page 10}, :content {:text "This paper proposes a metric recommendation service for online systems on the basis of graph learning."}, :properties {:color "green"}} {:id #uuid "635ef6a2-2238-4ffa-ad6c-d2c0317e15d5", :page 10, :position {:bounding {:x1 0, :y1 815.7528686523438, :x2 825.7815399169922, :y2 1425.3566284179688, :width 897.6, :height 1161.6000000000001}, :rects ({:x1 656.4214935302734, :y1 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diagnosis"}, :properties {:color "green"}} {:id #uuid "63603b06-e72b-4986-87c4-8a117e1a9e30", :page 3, :position {:bounding {:x1 102.13884735107422, :y1 767.8385314941406, :x2 222.17965698242188, :y2 787.19140625, :width 1060.8, :height 1372.8}, :rects ({:x1 102.13884735107422, :y1 767.8385314941406, :x2 222.17965698242188, :y2 787.19140625, :width 1060.8, :height 1372.8}), :page 3}, :content {:text "Fault Diagnosis."}, :properties {:color "purple"}} {:id #uuid "63603b24-4cd8-4ae4-a371-fccc69aeda56", :page 3, :position {:bounding {:x1 0, :y1 574.1045074462891, :x2 520.039794921875, :y2 911.5301513671875, :width 1060.8, :height 1372.8}, :rects ({:x1 0, :y1 574.1045074462891, :x2 0, :y2 593.4573669433594, :width 1060.8, :height 1372.8} {:x1 233.0440216064453, :y1 871.4551086425781, :x2 520.039794921875, :y2 890.8079528808594, :width 1060.8, :height 1372.8} {:x1 84.86846923828125, :y1 892.1772766113281, :x2 158.0281219482422, :y2 911.5301513671875, :width 1060.8, :height 1372.8}), :page 3}, :content {:text "However, no matter how this task is performed"}, :properties {:color "yellow"}} {:id #uuid "63603c11-1a15-43da-a024-39cceebc546f", :page 3, :position {:bounding {:x1 0, :y1 362.3127555847168, :x2 976.1263427734375, :y2 1233.37992477417, :width 1060.8, :height 1372.8}, :rects ({:x1 673.27294921875, :y1 362.3127555847168, :x2 976.122314453125, :y2 381.6656303405762, :width 1060.8, :height 1372.8} {:x1 540.7561950683594, :y1 383.0349235534668, :x2 976.1263427734375, :y2 402.3877983093262, :width 1060.8, :height 1372.8} {:x1 540.7561950683594, :y1 403.7570915222168, :x2 648.062255859375, :y2 423.1099662780762, :width 1060.8, :height 1372.8} {:x1 0, :y1 1198.0289726257324, :x2 0, :y2 1217.38187789917, :width 1060.8, :height 1372.8} {:x1 0, :y1 1214.0270195007324, :x2 0, :y2 1233.37992477417, :width 1060.8, :height 1372.8}), :page 3}, :content {:text "which is representative enough to reveal the practice of engineers in selecting metrics on KPI dashboards for monitoring."}, :properties {:color "yellow"}} {:id #uuid "63603c2e-9725-4f25-b6bd-9f5a19636baa", :page 3, :position {:bounding {:x1 705.3140563964844, :y1 445.2527847290039, :x2 868.146728515625, :y2 464.6056594848633, :width 1060.8, :height 1372.8}, :rects ({:x1 705.3140563964844, :y1 445.2527847290039, :x2 868.146728515625, :y2 464.6056594848633, :width 1060.8, :height 1372.8}), :page 3}, :content {:text " intelligent anomaly det"}, :properties {:color "green"}} {:id #uuid "63603c3a-b07b-4f13-b1d4-6b972ad9046d", :page 3, :position {:bounding {:x1 0, :y1 445.2527847290039, :x2 976.125, :y2 1281.374122619629, :width 1060.8, :height 1372.8}, :rects ({:x1 868.1414794921875, :y1 445.2527847290039, :x2 976.125, :y2 464.6056594848633, :width 1060.8, :height 1372.8} {:x1 540.7561950683594, :y1 465.9749526977539, :x2 733.7019348144531, :y2 485.3278274536133, :width 1060.8, :height 1372.8} {:x1 0, :y1 1262.0212173461914, :x2 0, :y2 1281.374122619629, :width 1060.8, :height 1372.8}), :page 3}, :content {:text "ection and fault diagnosis for online system"}, :properties {:color "green"}} {:id #uuid "63604237-cd0c-42d7-b5dc-96ac01306521", :page 8, :position {:bounding {:x1 0, :y1 894.0657501220703, :x2 520.2372398376465, :y2 1183.4971923828125, :width 1060.8, :height 1372.8}, :rects ({:x1 0, :y1 894.0657501220703, :x2 0, :y2 913.4186096191406, :width 1060.8, :height 1372.8} {:x1 0, :y1 910.0638122558594, :x2 0, :y2 929.4166870117188, :width 1060.8, :height 1372.8} {:x1 146.6662254333496, :y1 1122.6999816894531, :x2 520.2347984313965, :y2 1142.0528564453125, :width 1060.8, :height 1372.8} {:x1 84.86846542358398, :y1 1143.4221496582031, :x2 520.2372398376465, :y2 1162.7750244140625, :width 1060.8, :height 1372.8} {:x1 84.86846542358398, :y1 1164.1443176269531, :x2 403.3705406188965, :y2 1183.4971923828125, :width 1060.8, :height 1372.8}), :page 8}, :content {:text "or baseline group, we consider the expert experience and random selection. Besides, we uniformly adopt SLA-VAE[3] as anomaly detection model in this paper"}, :properties {:color "purple"}}]}