Tech Note: Developing Faceted Search Using Apache Solr, Part 1

Searching and sifting through large amount of information is a challenging task and a norm confronting web users today. There is a need for resource discovery services capable of dealing with large search resultsets effectively. Previous research indicates a tendency for users to prefer interfaces that incorporate some form of categorisation and grouping of results. These types of UIs tend to enhance the efficiency of information seeking and provide greater user satisfaction. This post describes the development of faceted search, a resource discovery approach based on a type of category system which has become prevalent in digital libraries. A remit of the UX2.0 project involves enhancing an existing digital library featuring faceted search through user-centred design (UCD). We are also evaluating AquaBrowser a leading library product facilitating faceted search.

This post relates to my recent work on two systems. It describes the development and setting up of a faceted search infrastructure - Apache Solr for Blacklight, a Ruby-on-Rails and open source resource discovery UI. To provide concentration user experience, the infrastructure provides data aggregated from multiple and heterogeneous sources.

Part 1 of this account (this post) describes the general setup of Solr for multi-sourced data and the experience of importing the CERN book dataset, using the Data Import Hanlder (DIH) of Solr. UX2 is incorporating the book data in combinant with digital library objects, for low-fi content-rich UI prototypes testing.

A forthcoming post (Part 2) describes programmatic development of Solr, to enable indexing of Dublin Core metadata and binary documents in multiple formats (PDF, PowerPoints etc) held in an existing Fedora Commons digital repository.