Saturday, November 3rd, 2007

Searching Across Databases: Google Scholar, Meta Search or Federated Search?

As analysts/consultants/students/marketers/startup wannabes, the one thing you often rely on for reports that are truly worth evaluating and reading is a credible source of database. Many companies and academic institutions subscribe to these important databases such as Economist, ThomsonOne and Factiva.

However, a common problem associated with these collection of journals and data repository is in finding the right one itself. It would be inefficient for that individual to search for the required information in each of these database. Shuli and I examine the different possibilities in facilitating an effective search across academic databases in a paper, which is available at http://www.keizng.com/docs/Search%20engine%20comparision.pdf using SMU as the base for the case study. The following is an excerpt from the article.

The library at Singapore Management University (SMU) subscribes to a number of academic
databases which contain collections of journal articles, conference proceedings and working papers
among other documents. These are frequently accessed by students and faculty who require the
documents in their course of their daily research and work.

 

Currently, the process of searching and retrieving articles is tedious as it is not possible to query
different databases at the same time. For example, documents pertinent to the topic of “Search
Engine Indexing Technologies” are present in numerous journals by different publishers who each
maintain their own database, so these relevant articles could be spread across various databases
like JSTOR, ScienceDirect and EBSCOHost. In order for a user to obtain articles from a variety of
sources, he has to access each database individually from the SMU library website and then perform
his search repeatedly, across every database that might contain relevant documents.
This method is laborious and time-consuming, and often results in users restricting their search to a
small subset of the available databases, as it would be too troublesome to repeat the search for all
the known databases.

 

As such, there is a high possibility that documents relevant to a user’s
research are neglected because they are found in some of the smaller or lesser known databases.
Hence the need for an integrated searching experience. After speaking to the librarians at SMU
about this problem, we understand that they are looking to alternatives to the current search process,
and have short listed a number of approaches that could address the above problem. This
document aims to describe and evaluate these approaches in order to determine the one that can
best address the needs of SMU

Quite obviously as seen in the article, the approaches being evaluated are 1)In-house search engine method (which can also be considered as Federated Search, but not for this report), 2) Google Scholar method and 3) Meta Search Method (which for the sake of the report is also considered as Federated search, though some people would consider them as radically different styles). I hope this article will somehow find its way to the desktop of the librarians, and that students will find it useful in a tip or two on Google Scholar within the report (: