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Jim Hahn

Publications and source records attributed to Jim Hahn.

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Publisher References in Bibliographic Entity Descriptions

This paper describes a method for improved access to publisher references in linked data RDF editors using data mining techniques and a large set of library metadata encoded in the MARC21 standard. The corpus is comprised of clustered sets of publishers and publisher locations from the library MARC21 records found in the POD Data Lake, an Ivy+ Library Consortium metadata sharing initiative. The POD Data Lake contains seventy million MARC21 records, forty million of which are unique. The discovery of publisher entity sets described forms the basis for the streamlined description of BIBFRAME Instance entities. This study resulted in two major outputs: 1) A prediction database and 2) sets of publisher location and name association rules. The association rules are the basis of a prototype autosuggestion feature of BIBFRAME Instance entity description properties designed specifically to support the autopopulation of publisher entities in linked data RDF editors.

cs.DL

The Bibliotelemetry of Information and Environment: an Evaluation of IoT-Powered Recommender Systems

Internet of Things (IoT) infrastructure within the physical library environment is the basis for an integrative, hybrid approach to digital resource recommenders. The IoT infrastructure provides mobile, dynamic wayfinding support for items in the collection, which includes features for location-based recommendations. A modular evaluation and analysis herein clarified the nature of users' requests for recommendations based on their location and describes subject areas of the library for which users request recommendations. The modular mobile design allowed for deep exploration of users' bibliographic identifiers throughout the global module system, serving to provide context to the browsing data that are the focus of this study. Bibliotelemetry is introduced as an evaluation method for IoT middleware within library collections.

cs.DL