The interplay of big data, WorldCat, and Dewey

Abstract

As the premier example of big data in the bibliographic world, WorldCat has the potential to support knowledge discovery in many arenas. After giving evidence for a big data characterization of WorldCat, the paper explores this knowledge discovery potential from two perspectives related to the Dewey Decimal Classification (DDC) system: (1) how WorldCat data can inform development of the DDC (classification analytics) and (2) how DDC-classified content in WorldCat can shed light on the bibliographic world itself (collection analytics). In the realm of classification analytics, WorldCat data support decisions to modify the DDC by expanding or reducing the number of classes, adding topical coverage, or adding subject access points; data analysis can support recognition of (1) trending topics and (2) the faceted structure of subject domains. In the realm of collection analytics, the paper considers as possible applications the use of the DDC in the topical "fingerprinting" of categorized content in WorldCat or in performing a bibliographic gap analysis.