Competency E: Introduction
Design, query, and evaluate information retrieval systems.
While much of library science focuses on making information as available to people as possible at any given time, trying to locate a specific item can be overwhelming, especially to those unaccustomed to using cataloging or other information organization structures. This is where information retrieval systems come in: they allow a user to only view material that matches their specifications, thus preventing “analysis paralysis” or mental burnout.
I say “user” instead of “patron”, because information retrieval systems have been deeply integrated into everyday life, especially in the digital sphere. Every time someone performs a Google search, queries a database, or utilizes social media they interact with one of these systems. Thus, in both the commercial and public spheres, it is deeply prudent to make information retrieval systems both accurate and user-friendly.
Definitions:
Design: Understanding the underlying framework of information retrieval systems and it’s accompanying logic, so that one can create an effective information retrieval system of their own utilizing an independent dataset.
Query: To use a preexisting information retrieval system efficiently. This can involve employing Boolean modifiers, narrowing search results with the appropriate fields, and other methods as are available within the program.
Evaluate: To determine the capability of an information retrieval system based on measurable criteria such as number of results, information accuracy,
In this assignment, I conducted a comparison of three search services: that of one academic library-based database, one public library-based catalog, and one commercial catalog. The first, SJSU’s ACM Digital Library, had excellent filtering capabilities for the search results, as well as the ability to utilize Boolean modifiers. MarinNet’s online library catalog was remarkably similar, with expanded search options including “intended audience” and “reading level”. The last, the search service for the Barnes and Noble online store, was not nearly as robust, which was rather disappointing considering how their main audience is those seeking reading material.
Being able to compare these services directly really showed some of the small differences that can be found between services, and how those differences can affect usability.
An information retrieval system is only as robust as it’s data structure; a lesson our group learned the hard way when designing and testing a small database dedicated to the proper brewing of tea. Between deciding what counted as “herbal” tea not, debating how to format brewing temperatures, and determining the ranges of each attribute, we discovered how information retrieval systems and data structures interact down to the most minute detail. Proofreading became imperative to our success, as did a working knowledge of computer logic. While quite the challenging assignment, it provided valuable insight into a process that underpins much of today’s information processes.
Application and Conclusion
Between working with Workflows, Drupal, Bluecloud, and our online catalog, I interact with many information retrieval systems at my current library, as well as my life in general. After all, Google has become nearly ubiquitous in the digital landscape. However, where my interactions with these systems has always been from the user perspective, my time with the MLIS program has shown me the delicate processes behind a feature that I had previously taken for granted. While I do not know if I will be designing an information retrieval system again anytime soon, integrating this competency will allow me to utilize them more productively in the future.