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For library service, bibliomining is concisely defined as the data mining techniques used to extract patterns of behavior‐based artifacts from library systems. The bibliomining process includes identifying topics, creating a data warehouse, refining data, exploring data and evaluating results. The cases of practical implementations and applications in different areas have proved that the properly enough and consolidated data warehouse is the critical promise to successful data mining applications. However, the data warehouse creation in the processing of various data sources obviously hampers librarians to apply bibliomining to improve their services and operations. Moreover, most market data mining tools are even more complex for librarians to adopt bibliomining. The purpose of this paper is to propose a practical application model for librarian bibliomining, then develop its corresponding data processing prototype system to guarantee the success of applying data mining in libraries.

The rapid prototyping software development method was applied to design a prototype bibliomining system. In order to evaluate the effectiveness of the system, there was a comparison experiment of accomplishing an assigned task for 15 librarians.

With the results of system usability scale (SUS) comparison and turn‐around time analysis, it was established that the proposed model and the developed prototype system can really help librarians handle bibliomining applications better.

The proposed novel application bibliomining model and its developed integration system are proved to be effective and efficient in bibliomining by the task‐oriented experiment and SUS to 15 librarians. Comparing turn‐around time to accomplish the assigned task, about 35 per cent in terms of time was saved. Librarians really require an appropriate integration tool to assist them in successful bibliomining applications.

The present study aims, by adopting bibliomining, to analyse the borrowing and collection records in self-service libraries at mass rapid transit stations in northern Taiwan to discover reader borrowing preferences and patterns.

The current study used data mining to analyse two years of book-borrowing information from self-service library stations; it made use of an association rule mining model and the bibliomining process to identify readers’ preferred books and to explore reader borrowing behaviours. In addition, the librarians’ perceptions of the proposed approach were also investigated.

The findings indicated that readers often borrowed books in the bibliographical classifications of Home economics; Medical sciences; Psychology; Commerce: administration and management; and Education in the self-service library stations. Based on the bibliomining results, 23 reader borrowing patterns as well as potential books favoured by readers were uncovered. In addition, the challenges of bibliomining and data mining applied to library operations are reported.

Among the studies on the application of self-service technologies in libraries, most examined the integration of the self-service system and investigated users’ opinions. The present study used borrowing records and collection records in self-service library stations to conduct bibliomining and to explore reader borrowing preferences and behaviours as references for collection development and book recommendation services.

The purpose of this work is to present an alternative way of considering evidence‐based librarianship (EBL) through an examination of the data that makes up studies used for EBL.

This piece starts with the standard evidence‐based librarianship definition and deconstructs it down to the level of the individual user, and that data is considered in a different context.

The bibliomining process, or the combination of data warehousing, data mining, and bibliometrics, is used as a framework to build a different path to EBL. Bibliomining‐based evidence‐based librarianship is not appropriate for all topics; however, when the artifacts of library use can be gathered and explored, this method can provide a different path to reach the goals of EBL.

As the quantity of studies needed for traditional EBL are not currently available, this alternate method provides a way to achieve the goals of EBL through data already in the library systems.

The paper aims to understand the book subscription characteristics of the students at each college and help the library administrators to conduct efficient library management plans for books in the library. Unlike the traditional association rule mining (ARM) techniques which mine patterns from a single data set, this paper proposes a model, recency-frequency-college (RFC) model, to analyse book subscription characteristics of library users and then discovers interesting association rules from equivalence-class RFC segments.

A framework which integrates the RFC model and ARM technique is proposed to analyse book subscription characteristics of library users. First, the author applies the RFC model to determine library users’ RFC values. After that, the author clusters library users’ transactions into several RFC segments by their RFC values. Finally, the author discovers RFC association rules and analyses book subscription characteristics of RFC segments (library users).

The paper provides experimental results from the survey data. It shows that the precision of the frequent itemsets discovered by the proposed RFC model outperforms the traditional approach in predicting library user subscription itemsets in the following time periods. Besides, the proposed approach can discover interesting and valuable patterns from library book circulation transactions.

Because RFC thresholds were assigned based on expert opinion in this paper, it is an acquisition bottleneck. Therefore, researchers are encouraged to automatically infer the RFC thresholds from the library book circulation transactions.

The paper includes implications for the library administrators in conducting library book management plans for different library users.

This paper proposes a model, the RFC model, to analyse book subscription characteristics of library users.

Library data are often hard to analyze because these data come from unconnected sources, and the data sets can be very large. Furthermore, the desire to protect user privacy has prevented the retention of data that could be used to correlate library data to non-library data. The research team used data mining to determine library use patterns and to determine whether library use correlated to students’ grade point average.

A research team collected and analyzed data from the libraries, registrar and human resources. All data sets were uploaded into a single, secure data warehouse, allowing them to be analyzed and correlated.

The analysis revealed patterns of library use by academic department, patterns of book use over 20 years and correlations between library use and grade point average.

Analysis of more narrowly defined user populations and collections will help develop targeted outreach efforts and manage the print collections. The data used are from one university; therefore, similar research is needed at other institutions to determine whether these findings are generalizable.

The unexpected use of the central library by those affiliated with law resulted in cross-education of law and central library staff. Management of the print collections and user outreach efforts will reflect more nuanced selection of subject areas and departments.

A model is suggested for campus partnerships that enables data mining of sensitive library and campus information.

This conceptual piece presents a framework to aid libraries in gaining a more thorough and holistic understanding of their users and services. Through a presentation of the history of library evaluation, a multidimensional matrix of measures is developed that demonstrates the relationship between the topics and perspectives of measurement. These measurements are then combined through evaluation criteria, and then different participants in the library system view those criteria for decision making. By implementing this framework for holistic measurement and cumulative evaluation, library evaluators can gain a more holistic knowledge of the library system and library administrators can be better informed for their decision‐making processes.

This study aims to map the scholarly literature on human resource management (HRM) publishing intensity in journals listed in Web of Science (WOS) under the subject category “Information Science and Library Science,” between 1989 and 2022.

The current study used a “bibliometric research design,” which is a quantitative approach. Ten selected bibliometric indicators were used to measure the scientific literature: publication-related metrics, citation-related metrics, citation analysis, co-citation analysis, bibliographic coupling, co-word analysis, co-authorship analysis, network metrics, clustering and visualization. Moreover, Louvain’s clustering algorithm was used for network metrics.

The paper gives empirical insights into the scholarly literature on HRM. The results were analyzed for the 65 sources and 1,412 authors from 60 countries who contributed the most during this period. Moreover, the study highlights a glimpse of funding sources, open-access publishing patterns and venues of publishing.

The study would be very beneficial to researchers and practitioners across disciplines.

This study illustrates that HRM is a multidisciplinary field that is appealing to academics from various disciplines because of its unique emphasis on management, and as such, it necessitates the pooling and integration of people, information, expertise and strategies. The study investigates numerous quantitative indicators such as research trends and collaboration frameworks.

The purpose of this paper is to discuss the emerging and innovative technologies which integrate together to form smart libraries. Smart libraries are the new generation libraries, which work with the amalgamation of smart technologies, smart users and smart services.

An extensive review of literature on “smart libraries” was carried to ascertain the emerging technologies in the smart library domain. Clarivate Analytic’s Web of Science and Sciverse Scopus were explored initially to ascertain the extent of literature published on Smart Libraries and their varied aspects. Literature was searched against various keywords like smart libraries, smart technologies, Internet of Things (IoT), Electronic resource management (ERM), Data mining, Artificial intelligence (AI), Ambient intelligence, Blockchain Technology and Augmented Reality. Later on, the works citing the literature on Smart Libraries were also explored to visualize a broad spectrum of emerging concepts about this growing trend in libraries.

The study confirms that smart libraries are becoming smarter with the emerging smart technologies, which enhances their working capabilities and satisfies the users associated with them. Implementing the smart technologies in the libraries has bridged the gap between the services offered by the libraries and the rapidly changing and competing needs of the humans.

The paper highlights the emerging smart technologies in smart libraries and how they influence the efficiency of libraries in terms of users, services and technological integration.

The paper tries to highlight the current technologies in the smart library set-ups for the efficient working of library set-ups.

Information users of all categories find it difficult to search information especially in this electronic age. To access information, the user has to know the necessary commands to use when searching for any information electronically. The study discussed the concept of digital literacy, Classification of Understanding the information user in electronic Age, Studies dealing with Information need and challenges of the information user in the electronic age. Suggestions on how to reach out efficiently and effectively to users were made.

Aims to report on the 2004 Annual Conference of the American Society for Information Science and Technology (ASIST), which had the theme of Managing and Enhancing Information: Cultures and Conflicts. The conference was held in November in Providence, Rhode Island.

Reviews some of the events of the conference.

Finds that the hot topics of the conference were: Google, blogs, wikis, virtual reference and the Semantic Web.

A report that will be of interest to library and information management professionals.