Search (125 results, page 1 of 7)

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Date

2. 4.2000 18:01:22

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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

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Abstract

Current algorithms for finding associations among the attributes describing data in a database have a number of shortcomings. Presents a novel method for association generation, that answers all desiderata. The method is different from all existing algorithms and especially suitable to textual databases with binary attributes. Uses subword trees for quick indexing into the required database statistics. Tests the algorithm on the Reuters-22173 database with satisfactory results

Source

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

Type

a

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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

Type

a

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Abstract

Focuses on the information modelling side of conceptual modelling. Deals with the exploitation of fact verbalisations after finishing the actual information system. Verbalisations are used as input for the design of the so-called information model. Exploits these verbalisation in 4 directions: considers their use for a conceptual query language, the verbalisation of instances, the description of the contents of a database and for the verbalisation of queries in a computer supported query environment. Provides an example session with an envisioned tool for end user query formulations that exploits the verbalisation

Source

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

Type

a

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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

Type

a

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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

Type

a

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Abstract

Numerous studies have explored the possibility of uncovering information from web search queries but few have examined the factors that affect web query data sources. We conducted a study that investigated this issue by comparing Google Trends and Baidu Index. Data from these two services are based on queries entered by users into Google and Baidu, two of the largest search engines in the world. We first compared the features and functions of the two services based on documents and extensive testing. We then carried out an empirical study that collected query volume data from the two sources. We found that data from both sources could be used to predict the quality of Chinese universities and companies. Despite the differences between the two services in terms of technology, such as differing methods of language processing, the search volume data from the two were highly correlated and combining the two data sources did not improve the predictive power of the data. However, there was a major difference between the two in terms of data availability. Baidu Index was able to provide more search volume data than Google Trends did. Our analysis showed that the disadvantage of Google Trends in this regard was due to Google's smaller user base in China. The implication of this finding goes beyond China. Google's user bases in many countries are smaller than that in China, so the search volume data related to those countries could result in the same issue as that related to China.

Source

Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22

Type

a

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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."

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Abstract

Geolocated social media data provide a powerful source of information about places and regional human behavior. Because only a small amount of social media data have been geolocation-annotated, inference techniques play a substantial role to increase the volume of annotated data. Conventional research in this area has been based on the text content of posts from a given user or the social network of the user, with some recent crossovers between the text- and network-based approaches. This paper proposes a novel approach to categorize highly-mentioned users (celebrities) into Local and Global types, and consequently use Local celebrities as location indicators. A label propagation algorithm is then used over the refined social network for geolocation inference. Finally, we propose a hybrid approach by merging a text-based method as a back-off strategy into our network-based approach. Empirical experiments over three standard Twitter benchmark data sets demonstrate that our approach outperforms state-of-the-art user geolocation methods.

Type

a

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Abstract

The recent interest in Big Data has generated a broad range of new academic, corporate, and policy practices along with an evolving debate among its proponents, detractors, and skeptics. While the practices draw on a common set of tools, techniques, and technologies, most contributions to the debate come either from a particular disciplinary perspective or with a focus on a domain-specific issue. A close examination of these contributions reveals a set of common problematics that arise in various guises and in different places. It also demonstrates the need for a critical synthesis of the conceptual and practical dilemmas surrounding Big Data. The purpose of this article is to provide such a synthesis by drawing on relevant writings in the sciences, humanities, policy, and trade literature. In bringing these diverse literatures together, we aim to shed light on the common underlying issues that concern and affect all of these areas. By contextualizing the phenomenon of Big Data within larger socioeconomic developments, we also seek to provide a broader understanding of its drivers, barriers, and challenges. This approach allows us to identify attributes of Big Data that require more attention-autonomy, opacity, generativity, disparity, and futurity-leading to questions and ideas for moving beyond dilemmas.

Type

a

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Abstract

Rough set theory is a mathematical tool to deal with vagueness and uncertainty. To apply the theory, it needs to be associated with efficient and effective computational methods. A relation can be used to represent a decison table for use in decision making. By using this kind of table, rough set theory can be applied successfully to rough classification and knowledge discovery. Presents computational methods for using rough sets to identify classes in datasets, finding dependencies in relations, and discovering rules which are hidden in databases. Illustrates the methods with a running example from a database of car test results

Footnote

Contribution to a special issue devoted to knowledge discovery and data mining

Type

a

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Abstract

This paper describes a system to semi-automatically extend and refine ontologies by mining textual data from the Web sites of international online media. Expanding a seed ontology creates a semantic network through co-occurrence analysis, trigger phrase analysis, and disambiguation based on the WordNet lexical dictionary. Spreading activation then processes this semantic network to find the most probable candidates for inclusion in an extended ontology. Approaches to identifying hierarchical relationships such as subsumption, head noun analysis and WordNet consultation are used to confirm and classify the found relationships. Using a seed ontology on "climate change" as an example, this paper demonstrates how spreading activation improves the result by naturally integrating the mentioned methods.

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a

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Abstract

In this paper, we present a topic discovery system aimed to reveal the implicit knowledge present in news streams. This knowledge is expressed as a hierarchy of topic/subtopics, where each topic contains the set of documents that are related to it and a summary extracted from these documents. Summaries so built are useful to browse and select topics of interest from the generated hierarchies. Our proposal consists of a new incremental hierarchical clustering algorithm, which combines both partitional and agglomerative approaches, taking the main benefits from them. Finally, a new summarization method based on Testor Theory has been proposed to build the topic summaries. Experimental results in the TDT2 collection demonstrate its usefulness and effectiveness not only as a topic detection system, but also as a classification and summarization tool.

Type

a

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Abstract

Many powerful methods and tools exist for extracting meaning from scientific publications, their texts, and their citation links. However, existing proposals often neglect a fundamental aspect of learning: that understanding and learning require an active and constructive exploration of a domain. In this paper, we describe a new method and a tool that use data mining and interactivity to turn the typical search and retrieve dialogue, in which the user asks questions and a system gives answers, into a dialogue that also involves sense-making, in which the user has to become active by constructing a bibliography and a domain model of the search term(s). This model starts from an automatically generated and annotated clustering solution that is iteratively modified by users. The tool is part of an integrated authoring system covering all phases from search through reading and sense-making to writing. Two evaluation studies demonstrate the usability of this interactive and constructive approach, and they show that clusters and groups represent identifiable sub-topics.

Type

a

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Abstract

Data dependencies are used in database schema design to enforce the correctness of a database as well as to reduce redundant data. These dependencies are usually determined from the semantics of the attributes and are then enforced upon the relations. Describes a bottom-up procedure for discovering multivalued dependencies in observed data without knowing a priori the relationships among the attributes. The proposed algorithm is an application of the technique designed for learning conditional independencies in probabilistic reasoning. A prototype system for automated database schema design has been implemented. Experiments were carried out to demonstrate both the effectiveness and efficiency of the method

Footnote

Contribution to a special issue devoted to knowledge discovery and data mining

Type

a

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Abstract

Hundreds of thousands of hashtags are generated every day on Twitter. Only a few will burst and become trending topics. In this article, we provide the definition of a bursting hashtag and conduct a systematic study of a series of challenging prediction problems that span the entire life cycles of bursting hashtags. Around the problem of "how to build a system to predict bursting hashtags," we explore different types of features and present machine learning solutions. On real data sets from Twitter, experiments are conducted to evaluate the effectiveness of the proposed solutions and the contributions of features.

Type

a

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Abstract

We present an approach to enhancing information access through Web structure mining in contrast to traditional approaches involving usage mining. Specifically, we mine the hardwired hierarchical hyperlink structure of Web sites to identify patterns of term-term co-occurrences we call Web functional dependencies (FDs). Intuitively, a Web FD x -> y declares that all paths through a site involving a hyperlink labeled x also contain a hyperlink labeled y. The complete set of FDs satisfied by a site help characterize (flexible and expressive) interaction paradigms supported by a site, where a paradigm is the set of explorable sequences therein. We describe algorithms for mining FDs and results from mining several hierarchical Web sites and present several interface designs that can exploit such FDs to provide compelling user experiences.

Type

a

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Abstract

The goal of Open Knowledge Maps is to create a visual interface to the world's scientific knowledge. The base for this visual interface consists of so-called knowledge maps, which enable the exploration of existing knowledge and the discovery of new knowledge. Our open source knowledge mapping software applies a mixture of summarization techniques and similarity measures on article metadata, which are iteratively chained together. After processing, the representation is saved in a database for use in a web visualization. In the future, we want to create a space for collective knowledge mapping that brings together individuals and communities involved in exploration and discovery. We want to enable people to guide each other in their discovery by collaboratively annotating and modifying the automatically created maps.

Type

a