{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/81830"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/81830","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A Framework for Intelligent Notification Management in Multitasking Domains","abstract":"In this work, we focus on one class of interruption, notifications in the desktop, and present one solution to managing such notifications--- intelligently timing their delivery. Our solution is based on a deep theoretical understanding of how humans process information and what moments during a user's task execution exhibit lower mental workload. We leverage breakpoints, transitions between units of action as potential moments for presenting notifications, as we empirically show these moments to have lower mental workload. Through a series of empirical studies, we demonstrate how presenting interruptions at breakpoints lowers interruption costs and how the cost varies based on the granularity of the breakpoint, how these breakpoints can be detected using known task structures and how breakpoints can be detected without any knowledge of the underlying task. We develop O ASIS, a computational framework that can detect and differentiate three levels of breakpoints with reasonable accuracy without requiring any complex machinery. OASIS is the first system of its kind that can detect breakpoints and schedule notifications. We evaluate how O ASIS picks breakpoints for novel users and novel tasks, and how scheduling notifications at breakpoints reduces interruption costs. This work provides the first empirical evidence that intelligent notification management benefits the end user and contributes new lessons for designing effective notification management systems.","abstract_html":"In this work, we focus on one class of interruption, notifications in the desktop, and present one solution to managing such notifications--- intelligently timing their delivery. Our solution is based on a deep theoretical understanding of how humans process information and what moments during a user's task execution exhibit lower mental workload. We leverage breakpoints, transitions between units of action as potential moments for presenting notifications, as we empirically show these moments to have lower mental workload. Through a series of empirical studies, we demonstrate how presenting interruptions at breakpoints lowers interruption costs and how the cost varies based on the granularity of the breakpoint, how these breakpoints can be detected using known task structures and how breakpoints can be detected without any knowledge of the underlying task. We develop O ASIS, a computational framework that can detect and differentiate three levels of breakpoints with reasonable accuracy without requiring any complex machinery. OASIS is the first system of its kind that can detect breakpoints and schedule notifications. We evaluate how O ASIS picks breakpoints for novel users and novel tasks, and how scheduling notifications at breakpoints reduces interruption costs. This work provides the first empirical evidence that intelligent notification management benefits the end user and contributes new lessons for designing effective notification management systems.","abstract_has_math":false,"creators":["Iqbal, Shamsi T."],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Bailey, Brian P."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T20:20:36Z","date_published":"2015-09-25T20:20:36Z","updated_at":"2026-07-22T22:26:17Z","subjects":["Computer Science"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI3337802"],"render_values":[{"text":"(MiAaPQ)AAI3337802","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/81830","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bailey, Brian P."]},{"key":"dc:creator","label":"Author","values":["Iqbal, Shamsi T."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T20:20:36Z","10000-01-01","2008"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computer Science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/81830","(MiAaPQ)AAI3337802"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In this work, we focus on one class of interruption, notifications in the desktop, and present one solution to managing such notifications--- intelligently timing their delivery. Our solution is based on a deep theoretical understanding of how humans process information and what moments during a user's task execution exhibit lower mental workload. We leverage breakpoints, transitions between units of action as potential moments for presenting notifications, as we empirically show these moments to have lower mental workload. Through a series of empirical studies, we demonstrate how presenting interruptions at breakpoints lowers interruption costs and how the cost varies based on the granularity of the breakpoint, how these breakpoints can be detected using known task structures and how breakpoints can be detected without any knowledge of the underlying task. We develop O ASIS, a computational framework that can detect and differentiate three levels of breakpoints with reasonable accuracy without requiring any complex machinery. OASIS is the first system of its kind that can detect breakpoints and schedule notifications. We evaluate how O ASIS picks breakpoints for novel users and novel tasks, and how scheduling notifications at breakpoints reduces interruption costs. This work provides the first empirical evidence that intelligent notification management benefits the end user and contributes new lessons for designing effective notification management systems.","Made available in DSpace on 2015-09-25T20:20:36Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3337802.pdf: 3923720 bytes, checksum: 6c5f90c0bd84aca43d33b53ef83050ab (MD5) Previous issue date: 2008","Embargo set by: Seth Robbins for item 83111 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","U of I Only","215 p.","Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008."]},{"key":"dc:title","label":"Title","values":["A Framework for Intelligent Notification Management in Multitasking Domains"]}]}],"canonical_facts":{"dc:contributor":["Bailey, Brian P."],"dc:creator":["Iqbal, Shamsi T."],"dc:date":["2015-09-25T20:20:36Z","10000-01-01","2008"],"dc:description":["In this work, we focus on one class of interruption, notifications in the desktop, and present one solution to managing such notifications--- intelligently timing their delivery. Our solution is based on a deep theoretical understanding of how humans process information and what moments during a user's task execution exhibit lower mental workload. We leverage breakpoints, transitions between units of action as potential moments for presenting notifications, as we empirically show these moments to have lower mental workload. Through a series of empirical studies, we demonstrate how presenting interruptions at breakpoints lowers interruption costs and how the cost varies based on the granularity of the breakpoint, how these breakpoints can be detected using known task structures and how breakpoints can be detected without any knowledge of the underlying task. We develop O ASIS, a computational framework that can detect and differentiate three levels of breakpoints with reasonable accuracy without requiring any complex machinery. OASIS is the first system of its kind that can detect breakpoints and schedule notifications. We evaluate how O ASIS picks breakpoints for novel users and novel tasks, and how scheduling notifications at breakpoints reduces interruption costs. This work provides the first empirical evidence that intelligent notification management benefits the end user and contributes new lessons for designing effective notification management systems.","Made available in DSpace on 2015-09-25T20:20:36Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3337802.pdf: 3923720 bytes, checksum: 6c5f90c0bd84aca43d33b53ef83050ab (MD5) Previous issue date: 2008","Embargo set by: Seth Robbins for item 83111 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","U of I Only","215 p.","Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008."],"dc:identifier":["http://hdl.handle.net/2142/81830","(MiAaPQ)AAI3337802"],"dc:language":["eng"],"dc:subject":["Computer Science"],"dc:title":["A Framework for Intelligent Notification Management in Multitasking Domains"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:17Z"}