{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/31293"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/31293","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Solving Intelligence Analysis Problems using Biclusters","abstract":"Analysts must filter through an ever-growing amount of data to obtain information relevant to their investigations. Looking at every piece of information individually is in many cases not feasible; there is hence a growing need for new filtering tools and techniques to improve the analyst process with large datasets. We present MineVis — an analytics system that integrates biclustering algorithms and visual analytics tools in one seamless environment. The combination of biclusters and visual data glyphs in a visual analytics spatial environment enables a novel type of filtering. This design allows for rapid exploration and navigation across connected documents. Through a user study we conclude that our system has the potential to help analysts filter data by allowing them to i) form hypotheses before reading documents and subsequently ii) validating them by reading a reduced and focused set of documents.","abstract_html":"Analysts must filter through an ever-growing amount of data to obtain information relevant to their investigations. Looking at every piece of information individually is in many cases not feasible; there is hence a growing need for new filtering tools and techniques to improve the analyst process with large datasets. We present MineVis — an analytics system that integrates biclustering algorithms and visual analytics tools in one seamless environment. The combination of biclusters and visual data glyphs in a visual analytics spatial environment enables a novel type of filtering. This design allows for rapid exploration and navigation across connected documents. Through a user study we conclude that our system has the potential to help analysts filter data by allowing them to i) form hypotheses before reading documents and subsequently ii) validating them by reading a reduced and focused set of documents.","abstract_has_math":false,"creators":["Fiaux, Patrick O."],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Computer Science and Applications","degree_department":"Computer Science and Applications","school":null,"contributors":[],"advisors":[],"committee_chairs":["Ramakrishnan, Naren","North, Christopher L."],"committee_members":["Pérez-Quiñones, Manuel A."],"year":2012,"date_issued":"2012-01-27","date_published":"2012-01-27","updated_at":"2026-07-22T22:20:21Z","subjects":["Visual Analytics","Biclustering"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-02202012-084450"],"render_values":[{"text":"etd-02202012-084450","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/31293","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Ramakrishnan, Naren","North, Christopher L."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Pérez-Quiñones, Manuel A."]},{"key":"dc:contributor.department","label":"Department","values":["Computer Science and Applications"]},{"key":"dc:creator","label":"Author","values":["Fiaux, Patrick O."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-03-14T20:32:01Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-03-14T20:32:01Z","2012-03-09"]},{"key":"dc:date.issued","label":"Date","values":["2012-01-27"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science and Applications"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Visual Analytics","Biclustering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-02202012-084450"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/31293"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Analysts must filter through an ever-growing amount of data to obtain information relevant to their investigations. Looking at every piece of information individually is in many cases not feasible; there is hence a growing need for new filtering tools and techniques to improve the analyst process with large datasets. We present MineVis — an analytics system that integrates biclustering algorithms and visual analytics tools in one seamless environment. The combination of biclusters and visual data glyphs in a visual analytics spatial environment enables a novel type of filtering. This design allows for rapid exploration and navigation across connected documents. Through a user study we conclude that our system has the potential to help analysts filter data by allowing them to i) form hypotheses before reading documents and subsequently ii) validating them by reading a reduced and focused set of documents."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:title","label":"Title","values":["Solving Intelligence Analysis Problems using Biclusters"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Ramakrishnan, Naren","North, Christopher L."],"dc:contributor.committeemember":["Pérez-Quiñones, Manuel A."],"dc:contributor.department":["Computer Science and Applications"],"dc:creator":["Fiaux, Patrick O."],"dc:date.accessioned":["2014-03-14T20:32:01Z"],"dc:date.available":["2014-03-14T20:32:01Z","2012-03-09"],"dc:date.issued":["2012-01-27"],"dc:description.abstract":["Analysts must filter through an ever-growing amount of data to obtain information relevant to their investigations. Looking at every piece of information individually is in many cases not feasible; there is hence a growing need for new filtering tools and techniques to improve the analyst process with large datasets. We present MineVis — an analytics system that integrates biclustering algorithms and visual analytics tools in one seamless environment. The combination of biclusters and visual data glyphs in a visual analytics spatial environment enables a novel type of filtering. This design allows for rapid exploration and navigation across connected documents. Through a user study we conclude that our system has the potential to help analysts filter data by allowing them to i) form hypotheses before reading documents and subsequently ii) validating them by reading a reduced and focused set of documents."],"dc:description.degree":["Master of Science"],"dc:identifier.other":["etd-02202012-084450"],"dc:identifier.uri":["http://hdl.handle.net/10919/31293"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Visual Analytics","Biclustering"],"dc:title":["Solving Intelligence Analysis Problems using Biclusters"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science and Applications"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:20:21Z"}