Search results

This study aims to overcome the problem of traditional association rules relying almost entirely on expert experience to set relevant interest indexes in mining. Second, this project can effectively solve the problem of four types of rules being present in the database at the same time. The traditional association algorithm can only mine one or two types of rules and cannot fully explore the database knowledge in the decision-making process for library recommendation.

The authors proposed a Markov logic network method to reconstruct association rule-mining tasks for library recommendation and compared the method proposed in this paper to traditional Apriori, FP-Growth, Inverse, Sporadic and UserBasedCF algorithms on two history library data sets and the Chess and Accident data sets.

The method used in this project had two major advantages. First, the authors were able to mine four types of rules in an integrated manner without having to set interest measures. In addition, because it represents the relevance of mining in the network, decision-makers can use network visualization tools to fully understand the results of mining in library recommendation and data sets from other fields.

The time cost of the project is still high for large data sets. The authors will solve this problem by mapping books, items, or attributes to higher granularity to reduce the computational complexity in the future.

The authors believed that knowledge of complex real-world problems can be well captured from a network perspective. This study can help researchers to avoid setting interest metrics and to comprehensively extract frequent, rare, positive, and negative rules in an integrated manner.

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.

Nowadays, database management system has been applied in library management, and a great number of data about readers’ visiting history to resources have been accumulated by libraries. A lot of important information is concealed behind such data. The purpose of this paper is to use a typical data mining (DM) technology named an association rule mining model to find out borrowing rules of readers according to their borrowing records, and to recommend other booklists for them in a personalized way, so as to increase utilization rate of data resources at library.

Association rule mining algorithm is applied to find out borrowing rules of readers according to their borrowing records, and to recommend other booklists for them in a personalized way, so as to increase utilization rate of data resources at library.

Through an analysis on record of book borrowing by readers, library manager can recommend books that may be interested by a reader based on historical borrowing records or current book-borrowing records of the reader.

If many different categories of book-borrowing problems are involved, it will result in large length of encoding as well as giant searching space. Therefore, future research work may be considered in the following aspects: introduce clustering method; and apply association rule mining method to procurement of book resources and layout of books.

The paper provides a helpful inspiration for Big Data mining and software development, which will improve their efficiency and insight on users’ behavior and psychology.

The paper proposes a framework to help users understand others’ behavior, which will aid them better take part in group and community with more contribution and delightedness.

DM technology has been used to discover information concealed behind Big Data in library; the library personalized recommendation problem has been analyzed and formulated deeply; and a method of improved association rules combined with artificial bee colony algorithm has been presented.

The behaviour of a library user is based on his ability to evaluate the consumption of library services based on alternative market services. The purpose of this paper is to study the usage behaviour in a public library building in the context of alternative costs instead of a narrow focus on book circulation data.

By library usage mining, using associative rules, the authors described the behaviour of library users and identified the typical behaviour during the visits. The authors analyse the results in the context of alternative costs assigned to the visits.

The results confirm that some underused services, such as digital services, deliver significantly greater benefit. The frequency of use, the duration of visit and the number of items used are associated with higher alternative costs. There were no significant differences in alternative cost within economic groups (excluding pensioners). This paper identified 41 frequent patterns with different alternative costs.

The limitation of this study is the fact that data for library usage mining were collected using a questionnaire survey.

This may be particularly important for both policy makers and library management. The first beneficiaries are donors and patrons, who can learn about the benefit that libraries bring to society. The proposed system for library usage mining also enables managers to promote specific (effective) services, take steps to avoid readers leaving, and improve the adoption of library services. It can also be used to adapt the location of library services. Librarians, especially those who engage in acquisitions, may also use this information in their evidence-based decisions about the design of services.

So far, there has been no relevant research on the economic aspects of extracted behaviour patterns. Therefore, this study revealed users’ economic preferences using a questionnaire survey that supplemented transaction data. The ability to describe users’ behaviour can provide library management with enough information to realise evidence-based decision making.

Traditional library catalogs have become inefficient and inconvenient in assisting library users. Readers may spend a lot of time searching library materials via printed catalogs. Readers need an intelligent and innovative solution to overcome this problem. The paper seeks to examine data mining technology which is a good approach to fulfill readers' requirements.

Data mining is considered to be the non‐trivial extraction of implicit, previously unknown, and potentially useful information from data. This paper analyzes readers' borrowing records using the techniques of data analysis, building a data warehouse, and data mining.

The paper finds that after mining data, readers can be classified into different groups according to the publications in which they are interested. Some people on the campus also have a greater preference for multimedia data.

The data mining results shows that all readers can be categorized into five clusters, and each cluster has its own characteristics. The frequency with which graduates and associate researchers borrow multimedia data is much higher. This phenomenon shows that these readers have a higher preference for accepting digitized publications. Also, the number of readers borrowing multimedia data has increased over the years. This trend indicates that readers preferences are gradually shifting towards reading digital publications.

This study aims to analyze and predict a user’s behavior and create recommender systems in libraries and information centers, using data mining techniques.

The present study is an analytical survey study of cross-sectional type. The required data for this study were collected from the transactions of the users of libraries and information centers in Hamadan University of Medical Sciences. Using data mining techniques, the existing patterns were investigated, and users’ loan transactions were analyzed.

The findings showed that the association rules with the degree of confidence above 0.50 were able to determine user access patterns. Furthermore, among the decision tree algorithms, the C.05 predicted the loan period, referrals and users’ delay with the highest accuracy (i.e. 90.1). The other findings on feedforward neural network with R = 0.99 showed that the predicted results of neural network computation were very close to the real situation and had a proper estimation of user’s delay prediction. Finally, the clustering technique with the k-means algorithm predicted users’ behavior model regarding their loyalty.

The results of this study can lead to providing effective services and improve the quality of interaction between librarians and users and provide a good opportunity for managers to align supply of information resources with the real needs of users.

The results of the study showed that various data mining techniques are applicable with high efficiency and accuracy in analyzing library and information centers data and can be used to predict a user’s behavior and create recommendation systems.

To further provide some insight into mobile library (m-library) applications (apps) user needs and help libraries or app providers improve the service quality, the purpose of this paper is to explore all the types of user improvement needs and to discover which need is the most important based on user results.

Data were collected from more than 27,000 m-library app users from 16 provinces and autonomous regions in China. Text analysis using latent Dirichlet allocation and Word2Vec was carried out by text preprocessing. Furthermore, a visual presentation was conducted through pyLDAvis and word cloud. Finally, combined with expert opinions, the results were summarized to find the different types of needs.

There are three different types of needs for improvement: needs of function, needs of technology and needs of experience. These types can be further divided into six subtypes: richness of function, feasibility of function, easiness of technology, stableness of technology, optimization of experience and customization of experience. Besides the richness of function, the feasibility of function has received the most attention from users.

Most studies on m-library user needs have only focused on a method of quantitative research based on questionnaire surveys. This study, however, is the first to apply text mining methods for large-scale user opinion texts, which place more focus on user needs and inspire libraries and app providers to further improve their services.

As libraries are developing a larger Web presence, issues regarding the utility, accessibility, and impact of the usage of their networked resources and services are gaining critical importance. The need to assess systematically the networked electronic services and resources is great as increasing amounts of financial resources are dedicated to the Web presence of libraries. This paper aims to address this issue.

This project proposes to measure the impact of networked electronic services, building on MINES for Libraries^®, in a scalable way across libraries and consortia to enhance digital library service quality and impact on learning by enabling the future allocation of resources to areas of user‐identified need. Short, standardized web surveys are placed at the point‐of‐use of networked electronic resources and services through a network assessment infrastructure that uses contemporary mechanisms of authentication and access, such as EZproxy, openURL, Shibboleth, federated searching and others as modules to interface with ARL's StatsQUAL^®. A valid and reliable sampling method is proposed.

Point‐of‐use web surveys hold considerable promise as key tools in the assessment toolkit libraries may deploy to improve the research, teaching, and learning outcomes of their users.

This project enhances and deepens the information gained from vendor‐supplied data.

The developments described will make it easier for libraries to assess the usage of networked electronic resources and services.

This paper, a case study with research implications, analyzes ebook use and users, focusing on ProQuest’s Electronic Book Library (EBL) at the University of Massachusetts Amherst. The purpose of this paper is to understand ebook user attributes and behaviors in the context of print books and other eresources; to examine usage of EBL ebooks and print materials; and to explore differences between users of ebooks and print books.

The methodologies of the study are MINES for Libraries® implemented through EZproxy and an analysis of users and usage of EBL ebooks compared to print books based in part on the set of books available to the UMass community in both formats.

Undergraduates use print more often compared to graduate and faculty users. Among all uses of overlap materials in which materials are available in both formats, EBL and print, we find more print use. In all, 40 percent of EBL users did not check out any print library books, which would indicate that EBL created new users and new circulation that would not have otherwise occurred in print. Further, the findings show little difference by gender and ethnicity in the use of ebooks and print books and little evidence of lagging adoption of ebooks by faculty. When compared to campus-wide demographics, women and students of color use both print and ebooks in higher than expected rates.

Subject analysis of ebooks and print book usage and users by school or college are explored, highlighting differences between the two formats in certain subjects.

This paper aims to develop a system, which would enable efficient management and exploitation of documentation in electronic form, related to mining projects, with information retrieval and information extraction (IE) features, using various language resources and natural language processing.

The system is designed to integrate textual, lexical, semantic and terminological resources, enabling advanced document search and extraction of information. These resources are integrated with a set of Web services and applications, for different user profiles and use-cases.

The use of the system is illustrated by examples demonstrating keyword search supported by Web query expansion services, search based on regular expressions, corpus search based on local grammars, followed by extraction of information based on this search and finally, search with lexical masks using domain and semantic markers.

The presented system is the first software solution for implementation of human language technology in management of documentation from the mining engineering domain, but it is also applicable to other engineering and non-engineering domains. The system is independent of the type of alphabet (Cyrillic and Latin), which makes it applicable to other languages of the Balkan region related to Serbian, and its support for morphological dictionaries can be applied in most morphologically complex languages, such as Slavic languages. Significant search improvements and the efficiency of IE are based on semantic networks and terminology dictionaries, with the support of local grammars.