Defense Event

Effective Faceted Browsing

Manish Singh

Monday, July 14, 2014
3:00pm - 5:00pm
3725 Beyster Bldg.

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About the Event

ABSTRACT: Faceted browsing is a popular paradigm for end-user data access. It is, at present, the defacto standard for almost all e-commerce. A typical faceted interface has two main component panels: a query panel and a result panel. Faceted browsing is primarily designed to help users quickly get to a specific item if they know the characteristics they are looking for. However, limitations in the query and the result panel deter effective faceted browsing, especially for users unfamiliar with the data. In this dissertation, we highlight two such limitations, one each in the query and the result panel. We propose add-on extensions to address each of these limitations. In a faceted interface, users progressively select a sequence of facet values to get to their desired result set, which is called an exploration path. If the dataset is high-dimensional, the query panel can only show a few of those dimensions as queriable facets. Users cannot see in the query panel the overall space of available exploration paths, and thus end up choosing an inferior exploration path. Many users have difficulty in selecting or understanding an exploration path when there are many non-queriable facets and query panel has very limited information of interaction between facets. We address this limitation by showing users an integrated summary of facet interaction that summarizes their chosen exploration path, and by presenting a two-phased faceted interface that provides users a facetwise way to compare the available exploration paths. The result panel that is normally used for presenting relational tuples, including faceted interface, cannot support fast browsing. When a user scrolls fast through data having alphanumeric values, then everything seems like a fast changing blur. To help the user get a quick sense of data, we propose a novel variable-speed scrolling interface, which provides the user a good impression of the data through selected representative tuples that are chosen based on the user’s scrolling speed and browsing history.

Additional Information

Sponsor(s): H.V. Jagadish

Open to: Public