Beyond basic faceted search

TLDR

Faceted search is being extended to bring OLAP‑style analytics to free‑text queries over metadata‑rich content. The study aims to broaden faceted search by adding dynamic business‑intelligence aggregations and enabling efficient support for correlated facets where facet values are interdependent. The authors implement flexible, dynamic aggregations and demonstrate an efficient tree‑indexing approach that allows correlated facets to be indexed and retrieved quickly. Reducing the correlated‑facet problem to a tree‑indexing scenario enables efficient indexing and retrieval of data with correlated facets.

Abstract

This paper extends traditional faceted search to support richer information discovery tasks over more complex data models. Our first extension adds exible, dynamic business intelligence aggregations to the faceted application, enabling users to gain insight into their data that is far richer than just knowing the quantities of documents belonging to each facet. We see this capability as a step toward bringing OLAP capabilities, traditionally supported by databases over relational data, to the domain of free-text queries over metadata-rich content. Our second extension shows how one can efficiently extend a faceted search engine to support correlated facets - a more complex information model in which the values associated with a document across multiple facets are not independent. We show that by reducing the problem to a recently solved tree-indexing scenario, data with correlated facets can be efficiently indexed and retrieved