Improving the Analysis of Large Digital Collections: A Topic-Based Visualization Model for Better Information Access and Retrieval

Your Name and Title: Michelle Chen, Assistant Professor

Library, School, or Organization Name: School of Information, San Jose State University

Co-Presenter Name(s): N/A

Area of the World from Which You Will Present: California, USA

Language in Which You Will Present: English

Target Audience(s): Librarians, Information Professionals, Archivists

Short Session Description (one line): A topic-based visualization approach to exploring and understanding digital collections would be presented and demonstrated.

Full Session Description (as long as you would like):

Today, researchers, analysts, scholars, and the general public are grappling with the information explosion. The ongoing and dramatic increase in available information presents major challenges as we strive to analyze trends and retrieve key information from the “big data” pool. The phenomenon has motivated information professionals to seek better ways to organize and present information so people can find and retrieve what they need from large data sets. One approach for helping users find and retrieve information from large digital collections is information visualization. It refers to the creation of graphical presentations of data and better user interfaces for manipulating large data sets to help users make discoveries, decisions, or find new explanations for patterns (e.g., trends, gaps, outliers). Information visualization techniques can help organize and manage large digital collections to present big data in a way that helps more users find, retrieve, and analyze large-scale information effectively. In this project, the researcher developed, tested, and evaluated a new information visualization model with data from the Illinois Digital Archives. That model offers new options to information professionals for curating big data sets and also expands the ways in which users can retrieve and understand documents from large digital collections. The new model allows users to view digital documents at a semantic level through topic modeling, while at the same time being able to visualize the relationships between those documents more clearly.

Websites / URLs Associated with Your Session: N/A

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