{"id":{"repo_id":"reykjavik","oai_identifier":"oai:skemman.is:1946/53724"},"canonical_url":"https://search.dev.ndltd.org/etd/reykjavik/oai:skemman.is:1946/53724","repository":{"repo_id":"reykjavik","name":"Reykjavík University","base_url":"https://skemman.is/oai/request"},"display":{"title":"Progressive browsing-state delivery for interactive multimedia exploration","abstract":"Unlike traditional retrieval, dynamic exploration of multimedia collections may require complex aggregation queries whose performance is highly sensitive to dataset size, filters and grouping applied at any time. Such queries often yield unstable response times, thus undermining interactivity. Inspired by the online aggregation approach from the database community, we investigate how progressively refined intermediate results, accompanied by progress estimates, can help users retain control over the aggregation process, even when applied to an extremely large collection. We evaluate this approach within the Multidimensional Media Model. Our study shows that moving deduplication and grouping operators from the database to the server, coupled with a batched streaming strategy, reduces time-to-first-byte by over 90% for heavy queries while maintaining steady improvements in result quality. Our examination of the effect of join order shows that the database query optimiser produces near-optimal plans, with sensitivity compressed to at most 26%. An evaluation of classical selectivity estimation for progress tracking identifies attribute correlation as the primary source of error and evaluates two alternative estimation strategies.","abstract_html":"Unlike traditional retrieval, dynamic exploration of multimedia collections may require complex aggregation queries whose performance is highly sensitive to dataset size, filters and grouping applied at any time. Such queries often yield unstable response times, thus undermining interactivity. Inspired by the online aggregation approach from the database community, we investigate how progressively refined intermediate results, accompanied by progress estimates, can help users retain control over the aggregation process, even when applied to an extremely large collection. We evaluate this approach within the Multidimensional Media Model. Our study shows that moving deduplication and grouping operators from the database to the server, coupled with a batched streaming strategy, reduces time-to-first-byte by over 90% for heavy queries while maintaining steady improvements in result quality. Our examination of the effect of join order shows that the database query optimiser produces near-optimal plans, with sensitivity compressed to at most 26%. An evaluation of classical selectivity estimation for progress tracking identifies attribute correlation as the primary source of error and evaluates two alternative estimation strategies.","abstract_has_math":false,"creators":["Sigurður Þórarinsson 1991-"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Háskólinn í Reykjavík"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-06-10T11:50:57Z","date_published":"2026-06-10T11:50:57Z","updated_at":"2026-07-27T20:36:46Z","subjects":["Meistaraprófsritgerðir","Tölvunarfræði","Gagnagrunnar","Söfnun","Computer science","Databases","Aggregation"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1946/53724","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Háskólinn í Reykjavík"]},{"key":"dc:creator","label":"Author","values":["Sigurður Þórarinsson 1991-"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-06-10T11:50:52Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-06-10T11:50:52Z"]},{"key":"dc:date.issued","label":"Date","values":["2026-06-10T11:50:57Z"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Meistaraprófsritgerðir","Tölvunarfræði","Gagnagrunnar","Söfnun","Computer science","Databases","Aggregation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1946/53724"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Unlike traditional retrieval, dynamic exploration of multimedia collections may require complex aggregation queries whose performance is highly sensitive to dataset size, filters and grouping applied at any time. Such queries often yield unstable response times, thus undermining interactivity. Inspired by the online aggregation approach from the database community, we investigate how progressively refined intermediate results, accompanied by progress estimates, can help users retain control over the aggregation process, even when applied to an extremely large collection. We evaluate this approach within the Multidimensional Media Model. Our study shows that moving deduplication and grouping operators from the database to the server, coupled with a batched streaming strategy, reduces time-to-first-byte by over 90% for heavy queries while maintaining steady improvements in result quality. Our examination of the effect of join order shows that the database query optimiser produces near-optimal plans, with sensitivity compressed to at most 26%. An evaluation of classical selectivity estimation for progress tracking identifies attribute correlation as the primary source of error and evaluates two alternative estimation strategies."]},{"key":"dc:title","label":"Title","values":["Progressive browsing-state delivery for interactive multimedia exploration"]}]}],"canonical_facts":{"dc:contributor":["Háskólinn í Reykjavík"],"dc:creator":["Sigurður Þórarinsson 1991-"],"dc:date.accessioned":["2026-06-10T11:50:52Z"],"dc:date.available":["2026-06-10T11:50:52Z"],"dc:date.issued":["2026-06-10T11:50:57Z"],"dc:description.abstract":["Unlike traditional retrieval, dynamic exploration of multimedia collections may require complex aggregation queries whose performance is highly sensitive to dataset size, filters and grouping applied at any time. Such queries often yield unstable response times, thus undermining interactivity. Inspired by the online aggregation approach from the database community, we investigate how progressively refined intermediate results, accompanied by progress estimates, can help users retain control over the aggregation process, even when applied to an extremely large collection. We evaluate this approach within the Multidimensional Media Model. Our study shows that moving deduplication and grouping operators from the database to the server, coupled with a batched streaming strategy, reduces time-to-first-byte by over 90% for heavy queries while maintaining steady improvements in result quality. Our examination of the effect of join order shows that the database query optimiser produces near-optimal plans, with sensitivity compressed to at most 26%. 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