International Information & Library Review

Big Data Analytics and Web Scale Library Services for Next Generation Information Management

The integration of big data analytics with web scale library services has brought changes in current information management by converting traditional library information systems into very scalable digital library systems based on discovery and delivery processes. The development process of such library information systems starts from manual cataloguing systems to computerized library databases and digital repositories which facilitate easy storage, retrieval and management of scholarly information. Due to rapid development in digital information resources, libraries use distributed computing technologies and library cloud-based information systems for managing their huge, heterogeneous information assets. They provide support to sophisticated library search engines, semantic cataloguing and automated classifications that promote academic information discovery and access processes. In addition, modern library information systems employ analysis-based tools to observe usage behaviour, enrich their catalogues and improve their service delivery process. Benefits of such systems include better accessibility, quick retrieval and effective management of institutional information resources. The issues associated with these library information systems include security, complexities in integration, dependence on sophisticated infrastructure, standardization of metadata and interoperable library databases.

Web scale library services emphasize the importance of creating a library discovery platform that combines institutional repositories, digital archives and subscription-based academic databases as a single search and access system that is specifically designed for the purposes of research and learning. Further, the library information systems in universities and other higher education institutions are developing in such a way that they would facilitate scholarly communication and citation indexing, as well as provide resource categorization through subjects rather than by making use of generic digital environments or information networks. Intelligent library services are also becoming popular because they are based on analytical recommendation systems and metadata improvement technologies that are embedded in the processes of library cataloguing, circulation and digital archiving.

In this research, the use of big data analytics with web scale library services forms an ideal approach for achieving the information management of the next-generation, by making traditional library systems scalable and intelligent digital systems. Such a move facilitates improved discoverability, accessibility and organization of academic content using cloud and distributed systems. Although such progress leads to more efficient information searches and delivery, there are several security, metadata consistency and interoperability concerns associated with it. Thus, consistent efforts aimed at solving these problems are crucial in order to ensure safe and sustainable library-based information systems in the future.

Topics of interest include (but not limited to):
• Machine Learning Approaches for Intelligent Library Cataloguing and Classification
• Scalable Digital Repository Management Using Big Data Analytics Techniques
• Web Scale Library Information Retrieval Using Advanced Search Algorithms
• Metadata Standardization Frameworks for Interoperable Library Database Systems
• AI Enabled Recommendation Systems for Academic Library Resource Discovery
• Distributed Computing Models for Large Scale Library Information Management
• Big Data Integration Techniques for Institutional Repository Management Systems
• Semantic Web Technologies for Enhanced Library Information Retrieval Systems
• Automated Indexing and Classification Methods in Digital Library Systems
• Data Driven Library Service Optimization Using Analytics Based Models
• Interoperability Solutions for Heterogeneous Web Scale Library Databases
• User Centric Information Access Models in Intelligent Library Platforms