Dynamic faceted search for discovery-driven analysis

We propose a dynamic faceted search system for discoverydriven analysis on data with both textual content and structured attributes. From a keyword query, we want to dynamically select a small set of "interesting" attributes and present aggregates on them to a user. Similar to work in OLAP exploration, we define "interestingness" as how surprising an aggregated value is, based on a given expectation. We make two new contributions by proposing a novel"navigational"expectation that's particularly useful in the context of faceted search, and a novel interestingness measure through judicious application of p-values. Through a user survey, we find the new expectation and interestingness metric quite effective. We develop an efficient dynamic faceted search system by improving a popular open source engine, Solr. Our system exploits compressed bitmaps for caching the posting lists in an inverted index, and a novel directory structure called a bitset tree fo...

• Type: Conference
• Year: 2008
• Where: CIKM
• Authors: Debabrata Dash, Jun Rao, Nimrod Megiddo, Anastasia Ailamaki, Guy M. Lohman
• Keywords: CIKM 2008, Dynamic Faceted Search, Faceted Search, Fast Bitset Intersection, Information Management