Reference number : GC-2017-Q1-03268, amendment number : , agreement type : G, recipient legal name : Hoeber, Orland (University of Regina), recipient country : CA, recipient province : nan, recipient city : Saskatchewan, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : This research addresses a problem that is becoming increasingly important in our modern information-centric lives. As we have become adept at searching the web for factual information, we are increasingly discovering the need to address complex information seeking problems within specialized knowledge repositories. While web search engines support finding single-page answers to focused questions using simple search interfaces, support for complex information seeking tasks is limited. Resolving such problems requires more than just viewing the top few documents in the search results list; the searcher may need to explore among and make sense of a potentially large number of search results, refine the query as the information seeking goal becomes more precise, and synthesize information gleaned from multiple queries and potentially over multiple information seeking sessions. 
 
The goal of this research is to study how visual analytics can support interactive information retrieval activities within complex search settings. The resulting &ldquo;visual search analytics&rdquo; research will address four problems: (1) analyze and understand the difficulties of undertaking complex search tasks; (2) design visual analytics approaches that support interactive information retrieval and the resolution of complex information seeking problems; (3) develop and iteratively refine prototype systems that implement these designs; and (4) study the benefits and limitations of such approaches. 
 
Consider the difficulty of searching within a digital library when one has limited knowledge of their search topic. In such situations, it is common to start with a vague query, and develop knowledge about the topic incrementally as documents are examined. A visual search analytics approach could use machine learning to extract key topics within the search results, and present this information in a visual format so that their relationships could be seen. This would enable the searcher to easily identify a specific interest, interactively refine the query to match this interest, and produce subsequent visualizations that enable the searcher to compare and make sense of the search results in relation to the topics. 
 
Building upon and extending the results of my previous research on using visualization to support human-centred search, this research will undertake a systematic study of visual search analytics within three specialized online knowledge repositories: digital libraries, social media, and online encyclopedias. These repositories each feature important differences in textual data and associated metadata, the types of information seeking behaviours searchers employ, and the criteria for considering the search a success. Studying visual search analytics over multiple knowledge repositories and various searcher behaviours will enable the development of a generalized framework for visual search analytics., program purpose french : This research addresses a problem that is becoming increasingly important in our modern information-centric lives. As we have become adept at searching the web for factual information, we are increasingly discovering the need to address complex information seeking problems within specialized knowledge repositories. While web search engines support finding single-page answers to focused questions using simple search interfaces, support for complex information seeking tasks is limited. Resolving such problems requires more than just viewing the top few documents in the search results list; the searcher may need to explore among and make sense of a potentially large number of search results, refine the query as the information seeking goal becomes more precise, and synthesize information gleaned from multiple queries and potentially over multiple information seeking sessions. 
 
The goal of this research is to study how visual analytics can support interactive information retrieval activities within complex search settings. The resulting &ldquo;visual search analytics&rdquo; research will address four problems: (1) analyze and understand the difficulties of undertaking complex search tasks; (2) design visual analytics approaches that support interactive information retrieval and the resolution of complex information seeking problems; (3) develop and iteratively refine prototype systems that implement these designs; and (4) study the benefits and limitations of such approaches. 
 
Consider the difficulty of searching within a digital library when one has limited knowledge of their search topic. In such situations, it is common to start with a vague query, and develop knowledge about the topic incrementally as documents are examined. A visual search analytics approach could use machine learning to extract key topics within the search results, and present this information in a visual format so that their relationships could be seen. This would enable the searcher to easily identify a specific interest, interactively refine the query to match this interest, and produce subsequent visualizations that enable the searcher to compare and make sense of the search results in relation to the topics. 
 
Building upon and extending the results of my previous research on using visualization to support human-centred search, this research will undertake a systematic study of visual search analytics within three specialized online knowledge repositories: digital libraries, social media, and online encyclopedias. These repositories each feature important differences in textual data and associated metadata, the types of information seeking behaviours searchers employ, and the criteria for considering the search a success. Studying visual search analytics over multiple knowledge repositories and various searcher behaviours will enable the development of a generalized framework for visual search analytics., agreement title english : Studying Visual Analytics Support for Interactive Information Retrieval within Complex Search Settings, agreement title french : Studying Visual Analytics Support for Interactive Information Retrieval within Complex Search Settings, agreement number : RGPIN, agreement value : 100000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada