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Apresenta uma metodologia operacional integrada, utilizando técnicas de repositórios de dados (data warehousing) e mineração de dados (data mining), e combinando ferramentas estatísticas com reconhecimento de padrões. Tendo em vista a utilização de recursos, o objetivo principal é chegar a um entendimento melhor do comportamento de comunidades de usuários. O anonimato dos usuários fica inteiramente assegurado na aplicação do modelo, pelo qual são processados os dados oriundos de arquivos transacionais da biblioteca. O próprio processo da bibliomineração consiste basicamente em coletar, depurar e anonimizar os dados, visando, precipuamente, a descoberta de padrões subjacentes. Os resultados podem ser combinados com diferentes variáveis, antes de sua utilização na tomada de decisão pela Administração da biblioteca. In order to get a better understanding of library user communities and to improve resource utilization, the author proposes an integrated operational application of data warehousing and datamining, combining statistical and pattern recognition tools, both integrated under a new concept named bibliomining. With the application of this model to transactional records of the library, patron privacy is fully respected through the creation of an anonymized version of the operational data, before reformatting them for bibliomining analysis. The bibliomining process itself consists mainly of collecting, cleaning and anonymizing the data, in order to discover underlying behavior patterns. The results can be combined with several variables, before being used to improve decision-making by the library administration.

Over the past few years, data mining has moved from corporations to other organizations. This paper looks at the integration of data mining in digital library services. First, bibliomining, or the combination of bibliometrics and data mining techniques to understand library services, is defined and the concept explored. Second, the conceptual frameworks for bibliomining from the viewpoint of the library decision-maker and the library researcher are presented and compared. Finally, a research agenda to resolve many of the common bibliomining issues and to move the field forward in a mindful manner is developed. The result is not only a roadmap for understanding the integration of data mining in digital library services, but also a template for other cross-discipline data mining researchers to follow for systematic exploration in their own subject domains.

Library and information services in corporations, schools, universities and communities capture information about their users, circulation history, resources in the collection and search patterns (Koenig, 1985). Unfortunately, few libraries have taken advantage of these data as a way to improve customer service, manage acquisition budgets or influence strategic decision making about uses of information in their organizations. In this chapter, we present a global view of the data generated in libraries, and the variety of decisions that those data can inform. We describe ways in which library and information managers can use data mining in their libraries, i.e., bibliomining, to understand patterns of behavior among library users and staff members and patterns of information resource use throughout the institution. The chapter examines data sources and possible applications of data mining techniques in the library.

While libraries have employed policies to protect the data about use of their services, these policies are rarely specific or standardized. Since 1996 the U.S. healthcare system has been grappling with the Health Insurance Portability and Accountability Act (HIPAA), which is designed to provide those handling personal health information with standardized, definitive instructions as to the protection of data. In this work, the authors briefly discuss the present situation of privacy policies about library use data, outline the HIPAA guidelines to understand parallels between the two, and finally propose methods to create a de-identified library data warehouse based on HIPAA for the protection of user privacy.

The Digital Reference Electronic Warehouse (DREW) project is a collection of digital reference transactions from different services and different communication channels that live in a single space. Reference services work with DREW to submit transactions using the DREW schema, which is conceptually similar to the MARC record format for bibliographic materials. Researchers can then receive records from DREW to improve our knowledge of digital reference. These researchers then use the results of their research to create tools, reports, and models based on the DREW schema, and place those items into a management information system (MIS). The services can then access the MIS and apply those tools to their own archives. The result is that services can benefit directly and rapidly from research, and are then more likely to continue their involvement with the project. This infrastructure creates a collaborative space where researchers and practitioners can benefit from the work of each other and aid us in advancing the field of digital reference.

Archeologists use artifacts to make statements about occupants of a physical space. Users of information resources leave behind data–based artifacts when they interact with a digital library or other Web–based information space. One process for examining these patterns is bibliomining, or the combination of data warehousing, data mining and bibliometrics to understand connections and patterns between works. The purpose of this paper is to use a research framework from archeology to structure exploration of these data artifacts through bibliomining to aid managers of digital libraries and other Web–based information resources.

Most people think of a library as the little brick building in the heart of their community or the big brick building in the center of a campus. These notions greatly oversimplify the world of libraries, however. Most large commercial organizations have dedicated in-house library operations, as do schools, non-gov-ernmental organizations, as well as local, state, and federal governments. With the increasing use of the Internet and the World Wide Web, digital libraries have burgeoned, and these serve a huge variety of different user audiences. With this ex-panded view of libraries, two key insights arise. First, libraries are typically embedded within larger institutions. Corporate libraries serve their corporations, academic libraries serve their universities, and public libraries serve taxpaying communities who elect overseeing representatives. Second, libraries play a pivotal role within their institutions as repositories and pro-viders of information resources. In the provider role, libraries represent in microcosm the intellectual and learning activities of the people who comprise the institution. This fact provides the basis for the strategic importance of library data mining: By ascertaining what users are seeking, bibliomining can reveal insights that have meaning in the context of the library’s host institution.Use of data mining to examine library data might be aptly termed

Bibliomining is the combination of data mining, bibliometrics, statistics, and reporting tools used to extract patterns of behavior-based artifacts and item-based metadata from library systems. The bibliomining process involves the identification of problem areas, the collecting and anonymizing of data into a data warehouse, the exploration of the data with data mining tools, and the analysis, validation, and implementation of the results. This panel will introduce the topic of bibliomining and present ways in which data warehousing and data mining are currently being used in library settings.