Bead: Explorations in information visualization

2004

introducing the whole framework, the DBL-Browser itself and various visualization models are described.

The Information Retrieval Series, 2008

The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

Journal of Data Mining & Digital Humanities, Special issue on Visualisations in Historical Linguistics, 2020

Discovery of the chronological or geographical distribution of collections of historical text can be more reliable when based on multivariate rather than on univariate data because multivariate data provide a more complete description. Where the data are high-dimensional, however, their complexity can defy analysis using traditional philological methods. The first step in dealing with such data is to visualize it using graphical methods in order to identify any latent structure. If found, such structure facilitates formulation of hypotheses which can be tested using a range of mathematical and statistical methods. Where, however, the dimensionality is greater than 3, direct graphical investigation is impossible. The present discussion presents a roadmap of how this obstacle can be overcome, and is in three main parts: the first part presents some fundamental data concepts, the second describes an example corpus and a high-dimensional data set derived from it, and the third outlines two approaches to visualization of that data set: dimensionality reduction and cluster analysis. keywords Data visualization, multivariate data, high dimensionality, dimensionality reduction, cluster analysis. INTRODUCTION Discovery of the chronological or geographical distribution of collections of historical text can be more reliable when based on multivariate rather than on univariate data because, assuming that the variables describe different aspects of the texts in question, multivariate data provide a more complete description. Where the multivariate data are high-dimensional, however, their complexity can defy analysis using traditional philological methods. The first step in dealing with such data is to visualize it using graphical methods in order to identify any latent structure. If found, such structure facilitates formulation of hypotheses which can be tested using a range of mathematical and statistical methods. Where, however, the dimensionality is greater than 3, direct graphical investigation is impossible. The present discussion presents a roadmap of how this obstacle can be overcome. Exemplification is based on data abstracted from a corpus of English historical texts with a known temporal distribution, allowing the efficacy of the methods covered in the discussion to be readily verified by the reader. The discussion is in three main parts. The first part presents some fundamental data concepts-its nature, its representation using vectors and matrices, and its interpretation in terms of concepts of vector space and manifold, the second part describes the corpus and a high-dimensional data set abstracted from it, and the third outlines approaches to visualization of that data set using the concepts from (1) applied to (2). These approaches are of two types.  The first, dimensionality reduction, reduces high-dimensional data to dimensionality 3 or less to enable graphical representation; the methods presented are (i) variable selection based on variance and (ii) principal component analysis.  The second, cluster analysis, represents the structure of data in high-dimensional space directly without dimensionality reduction.

The current availability of information many times impair the tasks of searching, browsing and analyzing information pertinent to a topic of interest. This paper presents a methodology to create a meaningful graphical representation of documents corpora targeted at supporting exploration of correlated documents. The purpose of such an approach is to produce a map from a document body on a research topic or field based on the analysis of their contents, and similarities amongst articles. The document map is generated, after text pre-processing, by projecting the data in two dimensions using Latent Semantic Indexing. The projection is followed by hierarchical clustering to support sub-area identification. The map can be interactively explored, helping to narrow down the search for relevant articles. Tests were performed using a collection of documents pre-classified into three research subject classes: Case-Based Reasoning, Information Retrieval, and Inductive Logic Programming. The map produced was capable of separating the main areas and approaching documents by their similarity, revealing possible topics, and identifying boundaries between them. The tool can deal with the exploration of inter-topics and intra-topic relationship and is useful in many contexts that need deciding on relevant articles to read, such as scientific research, education, and training.

2012

In this work the well known scientometric concepts of bibliographically coupled publications and co-cited references were applied to produce interactive maps of research fronts and knowledge bases of research fields. This article proposes a method and some standardization for the detection and visualization of research fronts and knowledge bases with two and three dimensional graphics inspired by geographical maps. Agglomerations of bibliographically coupled publications with a common knowledge base are identified and graphically represented by a density function of publications per area unit. The research fronts become visible if publications with similar vectors of common citations are associated and visualized as an ensemble in a three dimensional graphical representation as a mountain scenery measured with the help of a spatial density. Knowledge bases were calculated in the same way. Maps similar to the geographic representation of oceans and islands are used to visualize the twodimensional spatial density function of references weighted by individual links. The proposed methodology is demonstrated by publications in the field of battery research.

Applied Sciences, 2021

Real-world systems that are composed of various types of components, their interactions, and relationships, and numerous applications are often modeled as graphs or network structures to represent and analyze the presence of relationship, shape, and meaning of objects. Network-structured data are used for various exploration and in-depth analysis through visualization of information in various fields. In particular, online bibliographic databases are a service that is used for a myriad of purposes, such as simple search of research materials as well as understanding the history and flow of research, current status, and trends. A visualization tool that can intuitively perform exploration and analysis by modeling the data provided by the online bibliographic database in a network structure will be a very meaningful study for the exploration of various information using a large amount of complex bibliographic data. This study has modeled an online bibliographic database as an informat...

2019

Despite the demonstrated value of visualization-based modalities for measuring and mapping science, it remains common practice to search and explore the literature via databases that present lists of articles with little, if any, supplementary visual information. Identifying the desired item in a list is a familiar information retrieval paradigm with a low cognitive load. However, given the rapid emergence of the field of visual text analytics, it is time to challenge the notion that article lists should remain the dominant method to search and organize the scientific literature. One reason that visualization methods are applied relatively rarely in information retrieval may be that it is difficult to develop useful and user-friendly science mapping systems. This article summarizes key workflows for bibliometric mapping, a technique for visually representing information from scientific publications, including citation data, bibliographic metadata, and article content. It describes m...

Information Visualization, 2002

The Brazilian scientific community has available a curricular information system called Lattes. The analysis in the area of scientific production is rapidly evolving as we have an increase in the number of publications combined with increasingly interdisciplinary sources of information. The Brazilian scientific community has available a database with curricular information called Lattes which contain information about academic and scientific achievements. There is a demand to carry out the search and retrieval of information from the Lattes curriculum in order to reduce the difficulties in exploring bibliometric data and to aid in the analysis of this information. This article uses textual data mining techniques, multidimensional projection techniques and visualization based on analysis of bibliometric data extracted from the Lattes platform. The results obtained were clusters of research groups that are related to each other and clusters of research groups that work with very diffe...

Journal of the American Society for Information Science and Technology, 2013

Bibliometric mapping and visualization techniques represent one of the main pillars in the field of scientometrics. Traditionally, the main methodologies employed for representing data are Multi-Dimensional Scaling, Principal Component Analysis or Correspondence Analysis. In this paper we aim at presenting a visualization methodology known as Biplot analysis for representing bibliometric and science and technology indicators. A Biplot is a graphical representation of multivariate data, where the elements of a data matrix are represented according to dots and vectors associated with the rows and columns of the matrix. In this paper we explore the possibilities of applying the Biplot analysis in the research policy area. More specifically we will first describe and introduce the reader to this methodology and secondly, we will analyze its strengths and weaknesses through three different study cases: countries, universities and scientific fields. For this, we use a Biplot analysis known as JK-Biplot. Finally we compare the Biplot representation with other multivariate analysis techniques. We conclude that Biplot analysis could be a useful technique in scientometrics when studying multivariate data and an easy-to-read tool for research decision makers.