Search (115 results, page 1 of 6)

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Footnote

A special issue of selected papers from the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'97), held Singapore, 22-23 Feb 1997

Source

Knowledge-based systems. 10(1998) no.7, S.401-470

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Date

5. 4.1996 15:29:15

Source

Information systems. 22(1997) nos.5/6, S.333-347

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Date

5. 4.1996 15:29:15

Source

Information systems. 22(1997) nos.5/6, S.349-385

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Date

29. 3.2002 17:31:17

Source

Knowledge-based systems. 14(2001) nos.1/2, S.37-53

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Abstract

The present challenge faced by scientists working with Big Data comes in the overwhelming volume and level of detail provided by current data sets. Exceeding traditional empirical approaches, Big Data opens a new perspective on scientific work in which data comes to play a role in the development of the scientific problematic to be developed. Addressing this reconfiguration of our relationship with data through readings of Wittgenstein, Macherey, and Popper, we propose a picture of science that encourages scientists to engage with the data in a direct way, using the data itself as an instrument for scientific investigation. Using GIS as a theme, we develop the concept of cyber-human systems of thought and understanding to bridge the divide between representative (theoretical) thinking and (non-theoretical) data-driven science. At the foundation of these systems, we invoke the concept of the "semantic pixel" to establish a logical and virtual space linking data and the work of scientists. It is with this discussion of the relationship between analysts in their pursuit of knowledge and the rise of Big Data that this present discussion of the philosophical foundations of Big Data addresses the central questions raised by social informatics research.

Date

7. 3.2019 16:32:22

Source

Journal of the Association for Information Science and Technology. 70(2019) no.4, S.402-411

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Abstract

20th century massification of higher education and research in academia is said to have produced structurally stratified higher education systems in many countries. Most manifestly, the research mission of universities appears to be divisive. Authors have claimed that the Swedish system, while formally unified, has developed into a binary state, and statistics seem to support this conclusion. This article makes use of a comprehensive statistical data source on Swedish higher education institutions to illustrate stratification, and uses literature on Swedish research policy history to contextualize the statistics. Highlighting the opportunities as well as constraints of the data, the article argues that there is great merit in combining statistics with a qualitative analysis when studying the structural characteristics of national higher education systems. Not least the article shows that it is an over-simplification to describe the Swedish system as binary; the stratification is more complex. On basis of the analysis, the article also argues that while global trends certainly influence national developments, higher education systems have country-specific features that may enrich the understanding of how systems evolve and therefore should be analyzed as part of a broader study of the increasingly globalized academic system.

Date

22. 3.2013 19:43:01

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.3, S.574-586

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Abstract

Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned. Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on Text Embedded with Heterogeneous and Multimedia Data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases. Mining Text Data simplifies the content, so that advanced-level students, practitioners and researchers in computer science can benefit from this book. Academic and corporate libraries, as well as ACM, IEEE, and Management Science focused on information security, electronic commerce, databases, data mining, machine learning, and statistics are the primary buyers for this reference book.

LCSH

Computer science
Multimedia systems

Subject

Computer science
Multimedia systems

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Abstract

Text mining is about making serendipity more likely. Serendipity, the chance discovery of interesting ideas, has been responsible for many discoveries in science. Text mining systems strive to explore large text collections, separate the potentially meaningfull connections from a vast and mostly noisy background of random associations. In this paper we provide a summary of our text mining approach and also illustrate briefly some of the experiments we have conducted with this approach. In particular we use a profile-based text mining method. We have used these profiles to explore the global distribution of disease research, replicate discoveries made by others and propose new hypotheses. Text mining holds much potential that has yet to be tapped.

Date

29. 2.2008 17:14:09

Source

Knowledge organization, information systems and other essays: Professor A. Neelameghan Festschrift. Ed. by K.S. Raghavan and K.N. Prasad

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Date

2. 4.2000 18:01:22

Source

Library trends. 48(1999) no.1, S.182-208

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Date

23. 3.2008 19:10:22

Footnote

Rez. in: JASIST 54(2003) no.9, S.905-906 (C.A. Badurek): "Visual approaches for knowledge discovery in very large databases are a prime research need for information scientists focused an extracting meaningful information from the ever growing stores of data from a variety of domains, including business, the geosciences, and satellite and medical imagery. This work presents a summary of research efforts in the fields of data mining, knowledge discovery, and data visualization with the goal of aiding the integration of research approaches and techniques from these major fields. The editors, leading computer scientists from academia and industry, present a collection of 32 papers from contributors who are incorporating visualization and data mining techniques through academic research as well application development in industry and government agencies. Information Visualization focuses upon techniques to enhance the natural abilities of humans to visually understand data, in particular, large-scale data sets. It is primarily concerned with developing interactive graphical representations to enable users to more intuitively make sense of multidimensional data as part of the data exploration process. It includes research from computer science, psychology, human-computer interaction, statistics, and information science. Knowledge Discovery in Databases (KDD) most often refers to the process of mining databases for previously unknown patterns and trends in data. Data mining refers to the particular computational methods or algorithms used in this process. The data mining research field is most related to computational advances in database theory, artificial intelligence and machine learning. This work compiles research summaries from these main research areas in order to provide "a reference work containing the collection of thoughts and ideas of noted researchers from the fields of data mining and data visualization" (p. 8). It addresses these areas in three main sections: the first an data visualization, the second an KDD and model visualization, and the last an using visualization in the knowledge discovery process. The seven chapters of Part One focus upon methodologies and successful techniques from the field of Data Visualization. Hoffman and Grinstein (Chapter 2) give a particularly good overview of the field of data visualization and its potential application to data mining. An introduction to the terminology of data visualization, relation to perceptual and cognitive science, and discussion of the major visualization display techniques are presented. Discussion and illustration explain the usefulness and proper context of such data visualization techniques as scatter plots, 2D and 3D isosurfaces, glyphs, parallel coordinates, and radial coordinate visualizations. Remaining chapters present the need for standardization of visualization methods, discussion of user requirements in the development of tools, and examples of using information visualization in addressing research problems.
In 13 chapters, Part Two provides an introduction to KDD, an overview of data mining techniques, and examples of the usefulness of data model visualizations. The importance of visualization throughout the KDD process is stressed in many of the chapters. In particular, the need for measures of visualization effectiveness, benchmarking for identifying best practices, and the use of standardized sample data sets is convincingly presented. Many of the important data mining approaches are discussed in this complementary context. Cluster and outlier detection, classification techniques, and rule discovery algorithms are presented as the basic techniques common to the KDD process. The potential effectiveness of using visualization in the data modeling process are illustrated in chapters focused an using visualization for helping users understand the KDD process, ask questions and form hypotheses about their data, and evaluate the accuracy and veracity of their results. The 11 chapters of Part Three provide an overview of the KDD process and successful approaches to integrating KDD, data mining, and visualization in complementary domains. Rhodes (Chapter 21) begins this section with an excellent overview of the relation between the KDD process and data mining techniques. He states that the "primary goals of data mining are to describe the existing data and to predict the behavior or characteristics of future data of the same type" (p. 281). These goals are met by data mining tasks such as classification, regression, clustering, summarization, dependency modeling, and change or deviation detection. Subsequent chapters demonstrate how visualization can aid users in the interactive process of knowledge discovery by graphically representing the results from these iterative tasks. Finally, examples of the usefulness of integrating visualization and data mining tools in the domain of business, imagery and text mining, and massive data sets are provided. This text concludes with a thorough and useful 17-page index and lengthy yet integrating 17-page summary of the academic and industrial backgrounds of the contributing authors. A 16-page set of color inserts provide a better representation of the visualizations discussed, and a URL provided suggests that readers may view all the book's figures in color on-line, although as of this submission date it only provides access to a summary of the book and its contents. The overall contribution of this work is its focus an bridging two distinct areas of research, making it a valuable addition to the Morgan Kaufmann Series in Database Management Systems. The editors of this text have met their main goal of providing the first textbook integrating knowledge discovery, data mining, and visualization. Although it contributes greatly to our under- standing of the development and current state of the field, a major weakness of this text is that there is no concluding chapter to discuss the contributions of the sum of these contributed papers or give direction to possible future areas of research. "Integration of expertise between two different disciplines is a difficult process of communication and reeducation. Integrating data mining and visualization is particularly complex because each of these fields in itself must draw an a wide range of research experience" (p. 300). Although this work contributes to the crossdisciplinary communication needed to advance visualization in KDD, a more formal call for an interdisciplinary research agenda in a concluding chapter would have provided a more satisfying conclusion to a very good introductory text.
With contributors almost exclusively from the computer science field, the intended audience of this work is heavily slanted towards a computer science perspective. However, it is highly readable and provides introductory material that would be useful to information scientists from a variety of domains. Yet, much interesting work in information visualization from other fields could have been included giving the work more of an interdisciplinary perspective to complement their goals of integrating work in this area. Unfortunately, many of the application chapters are these, shallow, and lack complementary illustrations of visualization techniques or user interfaces used. However, they do provide insight into the many applications being developed in this rapidly expanding field. The authors have successfully put together a highly useful reference text for the data mining and information visualization communities. Those interested in a good introduction and overview of complementary research areas in these fields will be satisfied with this collection of papers. The focus upon integrating data visualization with data mining complements texts in each of these fields, such as Advances in Knowledge Discovery and Data Mining (Fayyad et al., MIT Press) and Readings in Information Visualization: Using Vision to Think (Card et. al., Morgan Kauffman). This unique work is a good starting point for future interaction between researchers in the fields of data visualization and data mining and makes a good accompaniment for a course focused an integrating these areas or to the main reference texts in these fields."

LCSH

Knowledge acquisition (Expert systems)

Series

Morgan Kaufmann series in data management systems

Subject

Knowledge acquisition (Expert systems)

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Abstract

This paper addresses a bibliometric methodology to discover the structure of the scientific 'landscape' in order to gain detailed insight into the development of MD fields, their interaction, and the transfer of knowledge between them. This methodology is appropriate to visualize the position of MD activities in relation to interdisciplinary MD developments, and particularly in relation to socio-economic problems. Furthermore, it allows the identification of the major actors. It even provides the possibility of foresight. We describe a first approach to apply bibliometric mapping as an instrument to investigate characteristics of knowledge transfer. In this paper we discuss the creation of 'maps of science' with help of advanced bibliometric methods. This 'bibliometric cartography' can be seen as a specific type of data-mining, applied to large amounts of scientific publications. As an example we describe the mapping of the field neuroscience, one of the largest and fast growing fields in the life sciences. The number of publications covered by this database is about 80,000 per year, the period covered is 1995-1998. Current research is going an to update the mapping for the years 1999-2002. This paper addresses the main lines of the methodology and its application in the study of knowledge transfer.

Source

Gaining insight from research information (CRIS2002): Proceedings of the 6th International Conference an Current Research Information Systems, University of Kassel, August 29 - 31, 2002. Eds: W. Adamczak u. A. Nase

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Abstract

Surveys the use of empirical, machine-learning methods for information extraction. Presents a generic architecture for information extraction systems and surveys the learning algorithms that have been developed to address the problems of accuracy, portability, and knowledge acquisition for each component of the architecture

Date

6. 3.1999 13:50:29

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Abstract

In this paper we give a synoptic view of the growth of the text processing technology of informatione xtraction (IE) whose function is to extract information about a pre-specified set of entities, relations or events from natural language texts and to record this information in structured representations called templates. Here we describe the nature of the IE task, review the history of the area from its origins in AI work in the 1960s and 70s till the present, discuss the techniques being used to carry out the task, describe application areas where IE systems are or are about to be at work, and conclude with a discussion of the challenges facing the area. What emerges is a picture of an exciting new text processing technology with a host of new applications, both on its own and in conjunction with other technologies, such as information retrieval, machine translation and data mining

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Abstract

Given the huge amount of information in the internet and in practically every domain of knowledge that we are facing today, knowledge discovery calls for automation. The book deals with methods from classification and data analysis that respond effectively to this rapidly growing challenge. The interested reader will find new methodological insights as well as applications in economics, management science, finance, and marketing, and in pattern recognition, biology, health, and archaeology.

Content

Data Analysis, Statistics, and Classification.- Pattern Recognition and Automation.- Data Mining, Information Processing, and Automation.- New Media, Web Mining, and Automation.- Applications in Management Science, Finance, and Marketing.- Applications in Medicine, Biology, Archaeology, and Others.- Author Index.- Subject Index.

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Abstract

Defines digital libraries and discusses the effects of new technology on librarians. Examines the different viewpoints of librarians and information technologists on digital libraries. Describes the development of a digital library at the National Drug Intelligence Center, USA, which was carried out in collaboration with information technology experts. The system is based on Web enabled search technology to find information, data visualization and data mining to visualize it and use of SGML as an information standard to store it

Date

22.11.1998 18:57:22

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Abstract

With more than two billion pages created by millions of Web page authors and organizations, the World Wide Web is a tremendously rich knowledge base. The knowledge comes not only from the content of the pages themselves, but also from the unique characteristics of the Web, such as its hyperlink structure and its diversity of content and languages. Analysis of these characteristics often reveals interesting patterns and new knowledge. Such knowledge can be used to improve users' efficiency and effectiveness in searching for information an the Web, and also for applications unrelated to the Web, such as support for decision making or business management. The Web's size and its unstructured and dynamic content, as well as its multilingual nature, make the extraction of useful knowledge a challenging research problem. Furthermore, the Web generates a large amount of data in other formats that contain valuable information. For example, Web server logs' information about user access patterns can be used for information personalization or improving Web page design.

Source

Annual review of information science and technology. 38(2004), S.289-330

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Abstract

In data mining, we emphasize the need for learning from huge, incomplete, and imperfect data sets. To handle noise in the problem domain, existing learning systems avoid overfitting the imperfect training examples by excluding insignificant patterns. The problem is that these systems use a limiting attribute-value language for representing the training examples and the induced knowledge. Moreover, some important patterns are ignored because they are statistically insignificant. In this article, we present a framework that combines genetic programming and inductive logic programming to induce knowledge represented in various knowledge representation formalisms from noisy databases (LOGENPRO). Moreover, the system is applied to one real-life medical database. The knowledge discovered provides insights to and allows better understanding of the medical domains

Source

Journal of the American Society for Information Science. 51(2000) no.9, S.870-881

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Abstract

External information plays an important role in group decision-making processes, yet research about external information support for Group Support Systems (GSS) has been lacking. In this study, we propose an approach to build a concept space to provide external concept support for GSS users. Built on a Web mining algorithm, the approach can mine a concept space from the Web and retrieve related concepts from the concept space based on users' comments in a real-time manner. We conduct two experiments to evaluate the quality of the proposed approach and the effectiveness of the external concept support provided by this approach. The experiment results indicate that the concept space mined from the Web contained qualified concepts to stimulate divergent thinking. The results also demonstrate that external concept support in GSS greatly enhanced group productivity for idea generation tasks.

Source

Journal of the American Society for Information Science and Technology. 60(2009) no.5, S.1057-1070

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Abstract

With the development of Web2.0, social tagging systems in which users can freely choose tags to annotate resources according to their interests have attracted much attention. In particular, literature on the emergence of collective intelligence in social tagging systems has increased. In this article, we propose a probabilistic generative model to detect latent topical communities among users. Social tags and resource contents are leveraged to model user interest in two similar and correlated ways. Our primary goal is to capture user tagging behavior and interest and discover the emergent topical community structure. The communities should be groups of users with frequent social interactions as well as similar topical interests, which would have important research implications for personalized information services. Experimental results on two real social tagging data sets with different genres have shown that the proposed generative model more accurately models user interest and detects high-quality and meaningful topical communities.

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.2, S.321-333

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Abstract

This research creates an intelligent agent for automated collection development in a digital library setting. It uses a predictive model based an facets of each Web page to select scholarly works. The criteria came from the academic library selection literature, and a Delphi study was used to refine the list to 41 criteria. A Perl program was designed to analyze a Web page for each criterion and applied to a large collection of scholarly and nonscholarly Web pages. Bibliomining, or data mining for libraries, was then used to create different classification models. Four techniques were used: logistic regression, nonparametric discriminant analysis, classification trees, and neural networks. Accuracy and return were used to judge the effectiveness of each model an test datasets. In addition, a set of problematic pages that were difficult to classify because of their similarity to scholarly research was gathered and classified using the models. The resulting models could be used in the selection process to automatically create a digital library of Webbased scholarly research works. In addition, the technique can be extended to create a digital library of any type of structured electronic information.

Source

Journal of the American Society for Information Science and technology. 54(2003) no.12, S.1081-1090