CiteSeerX is a digital library search engine that provides access to more than 4 million academic documents with nearly a million users and millions of hits per day. Artificial intelligence (AI) technologies are used in many components of CiteSeerX e.g. to accurately extract metadata, intelligently crawl the web, and ingest documents. We present key AI technologies used in the following components: document classification and deduplication, document and citation clustering, automatic metadata extraction and indexing, and author disambiguation. These AI technologies have been developed by CiteSeerX group members over the past 5–6 years. We also show the usage status, payoff, development challenges, main design concepts, and deployment and maintenance requirements. While it is challenging to rebuild a system like CiteSeerX from scratch, many of these AI technologies are transferable to other digital libraries and/or search engines.

A private cloud deployment of an infrastructure as a service (IaaS) clusteris a cost effective solution to many small and intermediate digital libraries and maybe companies. As a working online digital library search engine, the physical infrastructure of CiteSeerX represents many of the clusters for a typical digital library in terms of size and functionalities. CiteSeerX used to run on a cluster consisting of eighteen loosely coupled physical machines. In this work we share the experiences and lessons learned through migrating CiteSeerX into a private cloud environment using virtualization technique. We also discuss alternative solutions including a public cloud deployment using Amazon EC2 and EBS services. We found that the private cloud via virtualization is a better model for a digital library system like CiteSeerX. We also report system status, activities and proposed variations after the new system has been running for over half a year.