Ontology-Based Data Mining in Digital Libraries

2009 ◽  
pp. 163-175
Author(s):  
Ana Kovačević
Keyword(s):  
Data Mining ◽  
Digital Libraries

Bibliomining for Library Decision-Making

2011 ◽  
pp. 341-345
Author(s):  
Scott Nicholson ◽  
Jeffrey Stanton
Keyword(s):  
Data Mining ◽  
Digital Libraries ◽  
Large Data ◽  
Data Sets ◽  
Data Mining Techniques ◽  
Governmental Organizations ◽  
Non Governmental Organizations ◽  
The People ◽  
The World ◽  
Use Of The Internet

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-governmental 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 expanded 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 providers 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. With widespread adoption of computerized catalogs and search facilities over the past quarter century, library and information scientists have often used bibliometric methods (e.g., the discovery of patterns in authorship and citation within a field) to explore patterns in bibliographic information. During the same period, various researchers have developed and tested data mining techniques—advanced statistical and visualization methods to locate non-trivial patterns in large data sets. Bibliomining refers to the use of these bibliometric and data mining techniques to explore the enormous quantities of data generated by the typical automated library.

The Application of Data-Mining to Recommender Systems

2011 ◽  
pp. 45-50 ◽  
Author(s):  
J. Ben Schafer
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Electronic Commerce ◽  
Recommender Systems ◽  
Digital Libraries ◽  
Buying Behavior ◽  
Data Mining Techniques ◽  
The Past ◽  
A Site ◽  
Product Description

In a world where the number of choices can be overwhelming, recommender systems help users find and evaluate items of interest. They connect users with items to “consume” (purchase, view, listen to, etc.) by associating the content of recommended items or the opinions of other individuals with the consuming user’s actions or opinions. Such systems have become powerful tools in domains from electronic commerce to digital libraries and knowledge management. For example, a consumer of just about any major online retailer who expresses an interest in an item – either through viewing a product description or by placing the item in his “shopping cart” – will likely receive recommendations for additional products. These products can be recommended based on the top overall sellers on a site, on the demographics of the consumer, or on an analysis of the past buying behavior of the consumer as a prediction for future buying behavior. This paper will address the technology used to generate recommendations, focusing on the application of data mining techniques.

Data Mining Medical Digital Libraries

2011 ◽  
pp. 334-340
Author(s):  
Colleen Cunningham
Keyword(s):  
Data Mining ◽  
Growth Rate ◽  
Text Mining ◽  
Digital Libraries ◽  
Exponential Growth ◽  
Medical Data ◽  
Biomedical Literature ◽  
Exponential Growth Rate ◽  
Biological Functions ◽  
Data Mining Techniques

Given the exponential growth rate of medical data and the accompanying biomedical literature, more than 10,000 documents per week (Leroy et al., 2003), it has become increasingly necessary to apply data mining techniques to medical digital libraries in order to assess a more complete view of genes, their biological functions and diseases. Data mining techniques, as applied to digital libraries, are also known as text mining.

Data Mining Medical Digital Libraries

2011 ◽  
pp. 278-282
Author(s):  
Colleen Cunningham ◽  
Xiaohua Hu
Keyword(s):  
Data Mining ◽  
Growth Rate ◽  
Text Mining ◽  
Digital Libraries ◽  
Exponential Growth ◽  
Medical Data ◽  
Biomedical Literature ◽  
Exponential Growth Rate ◽  
Biological Functions ◽  
Data Mining Techniques

Given the exponential growth rate of medical data and the accompanying biomedical literature, more than 10,000 documents per week (Leroy et al., 2003), it has become increasingly necessary to apply data mining techniques to medical digital libraries in order to assess a more complete view of genes, their biological functions and diseases. Data mining techniques, as applied to digital libraries, are also known as text mining.

Bibliomining for Library Decision-Making

2011 ◽  
pp. 153-159
Author(s):  
Scott Nicholson
Keyword(s):  
Data Mining ◽  
Digital Libraries ◽  
Public Libraries ◽  
Quarter Century ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
The People ◽  
The World ◽  
Commercial Organizations ◽  
Provider Role

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 college campus. However, these notions greatly oversimplify the world of libraries. Most large commercial organizations have dedicated in-house library operations, as do schools; nongovernmental organizations; and local, state, and federal governments. With the increasing use of the World Wide Web, digital libraries have burgeoned, serving a huge variety of different user audiences. With this expanded 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 providers 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. With widespread adoption of computerized catalogs and search facilities over the past quarter century, library and information scientists have often used bibliometric methods (e.g., the discovery of patterns in authorship and citation within a field) to explore patterns in bibliographic information. During the same period, various researchers have developed and tested data-mining techniques, which are advanced statistical and visualization methods to locate nontrivial patterns in large datasets. Bibliomining refers to the use of these bibliometric and data-mining techniques to explore the enormous quantities of data generated by the typical automated library.

Application of Data Mining to Recommender Systems

2011 ◽  
pp. 44-48 ◽  
Author(s):  
J. Ben Schafer
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Electronic Commerce ◽  
Recommender Systems ◽  
Digital Libraries ◽  
Buying Behavior ◽  
Data Mining Techniques ◽  
The Past ◽  
A Site ◽  
Product Description

In a world where the number of choices can be overwhelming, recommender systems help users find and evaluate items of interest. They connect users with items to “consume” (purchase, view, listen to, etc.) by associating the content of recommended items or the opinions of other individuals with the consuming user’s actions or opinions. Such systems have become powerful tools in domains from electronic commerce to digital libraries and knowledge management. For example, a consumer of just about any major online retailer who expresses an interest in an item – either through viewing a product description or by placing the item in his “shopping cart” – will likely receive recommendations for additional products. These products can be recommended based on the top overall sellers on a site, on the demographics of the consumer, or on an analysis of the past buying behavior of the consumer as a prediction for future buying behavior. This paper will address the technology used to generate recommendations, focusing on the application of data mining techniques.

scholarly journals Data mining in personalized service of digital library

2018 ◽  
Vol 7 (1.7) ◽  
pp. 51
Author(s):  
Nivedhitha G ◽  
Rupavathy N
Keyword(s):  
Data Mining ◽  
Digital Library ◽  
Digital Libraries ◽  
Information Resources ◽  
Personalized Service ◽  
Mining Technology ◽  
Information Mining ◽  
Personalized Services ◽  
Information Engineering ◽  
Parliamentary Procedure

With the deepening of information engineering, people face increasing amounts of information resources and also the demand of information is more and more. The digital library is a wealth of information resources. Affording readers a richer layer of personalized service is a new objective for the growth of these digital libraries. The data mining techniques to elicit useful information from a bunch of clutter information, in parliamentary procedure to provide efficient technical support for personalized services of digital libraries. The feasibility of information mining technology in digital libraries is analyzed in this paper. It also discusses the information mining technology in the digital library applications and the feasibility analysis for the data mining applications in digital libraries.

Design of an Integrated Digital Library System Based on Peer-to-Peer Data Mining

2012 ◽  
Vol 2 (3) ◽  
pp. 1-14
Author(s):  
Mohammed Ammari ◽  
Dalila Chiadmi
Keyword(s):  
Data Mining ◽  
Digital Library ◽  
Digital Libraries ◽  
Research Area ◽  
Peer To Peer ◽  
Distributed Data ◽  
Library System ◽  
Digital Library System ◽  
Long Term Preservation

Traditional libraries will evolve to digital libraries which are clearly superior at: Dissemination, sharing, linking, storing, and information variety. Therefore, one can say that electronic libraries have specific needs in terms of content, services and long-term preservation. In contrast, digital libraries suffer from several inherent constraints: storage limitation, performance, relevancy, decentralization, lack of semantic, fault tolerance, scalability. The main intention of this paper is to present a design of an integrated digital library system based on peer-to-peer data mining. This article aims also to prove that peer-to-peer mining, an emerging branch of distributed data mining, is a hot research area well suited to overcome intrinsic problems of digital libraries.

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