{"id":{"repo_id":"nps","oai_identifier":"oai:calhoun.nps.edu:10945/10660"},"canonical_url":"https://search.dev.ndltd.org/etd/nps/oai:calhoun.nps.edu:10945/10660","repository":{"repo_id":"nps","name":"Naval Postgraduate School","base_url":"https://calhoun.nps.edu/server/oai/request"},"display":{"title":"Information selection in intelligence processing","abstract":"In many intelligence agencies, the processing of data into usable information ready for analysis poses a significant bottleneck. Typically, much more data is available than what can be processed in the limited time available for processing. We formulate the problem faced by an intelligence collection unit, when processing incoming raw information for delivery to intelligence analysts, as an exploration-exploitation problem: the processor has to choose between exploring for new sources of relevant information and exploiting known sources. To address the exploration-exploitation problem, we develop a mathematical model of the processor's knowledge and examine algorithms that allow the processor to maximize the discovery of relevant data given a time limit. We derive insights on the performance of different algorithms using a simulated case study.","abstract_html":"In many intelligence agencies, the processing of data into usable information ready for analysis poses a significant bottleneck. Typically, much more data is available than what can be processed in the limited time available for processing. We formulate the problem faced by an intelligence collection unit, when processing incoming raw information for delivery to intelligence analysts, as an exploration-exploitation problem: the processor has to choose between exploring for new sources of relevant information and exploiting known sources. To address the exploration-exploitation problem, we develop a mathematical model of the processor's knowledge and examine algorithms that allow the processor to maximize the discovery of relevant data given a time limit. We derive insights on the performance of different algorithms using a simulated case study.","abstract_has_math":false,"creators":["Nevo, Yuval."],"institution":"Monterey, California. Naval Postgraduate School","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Operations Research","school":null,"contributors":[],"advisors":["Kress, Moshe","Dimitrov, Nedialko B."],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-12","date_published":"2011-12","updated_at":"2026-07-27T20:24:57Z","subjects":[],"languages":[],"rights":["This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10945/10660","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Kress, Moshe","Dimitrov, Nedialko B."]},{"key":"dc:contributor.department","label":"Department","values":["Operations Research"]},{"key":"dc:creator","label":"Author","values":["Nevo, Yuval."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["December 2011"]},{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2012-08-22T15:33:08Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2012-08-22T15:33:08Z"]},{"key":"dc:date.issued","label":"Date","values":["2011-12"]},{"key":"dc:publisher","label":"Institution","values":["Monterey, California. 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We formulate the problem faced by an intelligence collection unit, when processing incoming raw information for delivery to intelligence analysts, as an exploration-exploitation problem: the processor has to choose between exploring for new sources of relevant information and exploiting known sources. To address the exploration-exploitation problem, we develop a mathematical model of the processor's knowledge and examine algorithms that allow the processor to maximize the discovery of relevant data given a time limit. We derive insights on the performance of different algorithms using a simulated case study."]},{"key":"dc:title","label":"Title","values":["Information selection in intelligence processing"]}]}],"canonical_facts":{"dc:contributor.advisor":["Kress, Moshe","Dimitrov, Nedialko B."],"dc:contributor.department":["Operations Research"],"dc:creator":["Nevo, Yuval."],"dc:date":["December 2011"],"dc:date.accessioned":["2012-08-22T15:33:08Z"],"dc:date.available":["2012-08-22T15:33:08Z"],"dc:date.issued":["2011-12"],"dc:description.abstract":["In many intelligence agencies, the processing of data into usable information ready for analysis poses a significant bottleneck. Typically, much more data is available than what can be processed in the limited time available for processing. We formulate the problem faced by an intelligence collection unit, when processing incoming raw information for delivery to intelligence analysts, as an exploration-exploitation problem: the processor has to choose between exploring for new sources of relevant information and exploiting known sources. To address the exploration-exploitation problem, we develop a mathematical model of the processor's knowledge and examine algorithms that allow the processor to maximize the discovery of relevant data given a time limit. We derive insights on the performance of different algorithms using a simulated case study."],"dc:identifier.uri":["https://hdl.handle.net/10945/10660"],"dc:publisher":["Monterey, California. Naval Postgraduate School"],"dc:rights":["This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States."],"dc:title":["Information selection in intelligence processing"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T20:24:57Z"}