REM: a ray exploration model that caters to the search needs of multi-attribute data

User recall patterns related to multi-attribute data match paradigms more suited to facet browsing than keyword-based search. Canonical keyword-based search methodologies put an undue cognitive load on the user in requiring that the user phrase his/her thoughts into keywords to be used as input for such approaches. Facet classification has been an active paradigm in recent times in enabling exploration of huge catalogs. But even after a considerable effort has gone into building sophisticated and usable models, catalog exploration has been a distant dream of the novice user. We present here a novel approach called Ray Exploration Model (REM) for creating such an interface. REM draws the best from and builds upon connected graphs and facet navigation, thus overcoming the inherent limitations in current interfaces using facet navigation. It conceptualizes the depth of each attribute in the data and breadth across the attributes in an easily navigable sun model with its rays. Together suns and rays form a network using relations between the values of attributes. REM is demonstrated using a prototype to show that it caters to search needs of a user without the limitations of the popular keyword based search approaches.