Search (25 results, page 1 of 2)

  • × theme_ss:"Visualisierung"
  • × year_i:[2010 TO 2020}
  1. Osinska, V.; Bala, P.: New methods for visualization and improvement of classification schemes : the case of computer science (2010) 0.10
    0.09561041 = product of:
      0.19122082 = sum of:
        0.19122082 = sum of:
          0.1500228 = weight(_text_:maps in 3693) [ClassicSimilarity], result of:
            0.1500228 = score(doc=3693,freq=4.0), product of:
              0.28477904 = queryWeight, product of:
                5.619245 = idf(docFreq=435, maxDocs=44218)
                0.050679237 = queryNorm
              0.5268042 = fieldWeight in 3693, product of:
                2.0 = tf(freq=4.0), with freq of:
                  4.0 = termFreq=4.0
                5.619245 = idf(docFreq=435, maxDocs=44218)
                0.046875 = fieldNorm(doc=3693)
          0.041198023 = weight(_text_:22 in 3693) [ClassicSimilarity], result of:
            0.041198023 = score(doc=3693,freq=2.0), product of:
              0.17747006 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.050679237 = queryNorm
              0.23214069 = fieldWeight in 3693, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046875 = fieldNorm(doc=3693)
      0.5 = coord(1/2)
    
    Abstract
    Generally, Computer Science (CS) classifications are inconsistent in taxonomy strategies. t is necessary to develop CS taxonomy research to combine its historical perspective, its current knowledge and its predicted future trends - including all breakthroughs in information and communication technology. In this paper we have analyzed the ACM Computing Classification System (CCS) by means of visualization maps. The important achievement of current work is an effective visualization of classified documents from the ACM Digital Library. From the technical point of view, the innovation lies in the parallel use of analysis units: (sub)classes and keywords as well as a spherical 3D information surface. We have compared both the thematic and semantic maps of classified documents and results presented in Table 1. Furthermore, the proposed new method is used for content-related evaluation of the original scheme. Summing up: we improved an original ACM classification in the Computer Science domain by means of visualization.
    Date
    22. 7.2010 19:36:46
  2. Bornmann, L.; Haunschild, R.: Overlay maps based on Mendeley data : the use of altmetrics for readership networks (2016) 0.06
    0.064961776 = product of:
      0.12992355 = sum of:
        0.12992355 = product of:
          0.2598471 = sum of:
            0.2598471 = weight(_text_:maps in 3230) [ClassicSimilarity], result of:
              0.2598471 = score(doc=3230,freq=12.0), product of:
                0.28477904 = queryWeight, product of:
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.050679237 = queryNorm
                0.9124516 = fieldWeight in 3230, product of:
                  3.4641016 = tf(freq=12.0), with freq of:
                    12.0 = termFreq=12.0
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3230)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Visualization of scientific results using networks has become popular in scientometric research. We provide base maps for Mendeley reader count data using the publication year 2012 from the Web of Science data. Example networks are shown and explained. The reader can use our base maps to visualize other results with the VOSViewer. The proposed overlay maps are able to show the impact of publications in terms of readership data. The advantage of using our base maps is that it is not necessary for the user to produce a network based on all data (e.g., from 1 year), but can collect the Mendeley data for a single institution (or journals, topics) and can match them with our already produced information. Generation of such large-scale networks is still a demanding task despite the available computer power and digital data availability. Therefore, it is very useful to have base maps and create the network with the overlay technique.
  3. Kraker, P.; Kittel, C,; Enkhbayar, A.: Open Knowledge Maps : creating a visual interface to the world's scientific knowledge based on natural language processing (2016) 0.05
    0.053041074 = product of:
      0.10608215 = sum of:
        0.10608215 = product of:
          0.2121643 = sum of:
            0.2121643 = weight(_text_:maps in 3205) [ClassicSimilarity], result of:
              0.2121643 = score(doc=3205,freq=8.0), product of:
                0.28477904 = queryWeight, product of:
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.050679237 = queryNorm
                0.7450137 = fieldWeight in 3205, product of:
                  2.828427 = tf(freq=8.0), with freq of:
                    8.0 = termFreq=8.0
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3205)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    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.
  4. Maaten, L. van den; Hinton, G.: Visualizing non-metric similarities in multiple maps (2012) 0.05
    0.053041074 = product of:
      0.10608215 = sum of:
        0.10608215 = product of:
          0.2121643 = sum of:
            0.2121643 = weight(_text_:maps in 3884) [ClassicSimilarity], result of:
              0.2121643 = score(doc=3884,freq=8.0), product of:
                0.28477904 = queryWeight, product of:
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.050679237 = queryNorm
                0.7450137 = fieldWeight in 3884, product of:
                  2.828427 = tf(freq=8.0), with freq of:
                    8.0 = termFreq=8.0
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3884)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Techniques for multidimensional scaling visualize objects as points in a low-dimensional metric map. As a result, the visualizations are subject to the fundamental limitations of metric spaces. These limitations prevent multidimensional scaling from faithfully representing non-metric similarity data such as word associations or event co-occurrences. In particular, multidimensional scaling cannot faithfully represent intransitive pairwise similarities in a visualization, and it cannot faithfully visualize "central" objects. In this paper, we present an extension of a recently proposed multidimensional scaling technique called t-SNE. The extension aims to address the problems of traditional multidimensional scaling techniques when these techniques are used to visualize non-metric similarities. The new technique, called multiple maps t-SNE, alleviates these problems by constructing a collection of maps that reveal complementary structure in the similarity data. We apply multiple maps t-SNE to a large data set of word association data and to a data set of NIPS co-authorships, demonstrating its ability to successfully visualize non-metric similarities.
  5. Leydesdorff, L.; Persson, O.: Mapping the geography of science : distribution patterns and networks of relations among cities and institutes (2010) 0.05
    0.045934916 = product of:
      0.09186983 = sum of:
        0.09186983 = product of:
          0.18373966 = sum of:
            0.18373966 = weight(_text_:maps in 3704) [ClassicSimilarity], result of:
              0.18373966 = score(doc=3704,freq=6.0), product of:
                0.28477904 = queryWeight, product of:
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.050679237 = queryNorm
                0.6452008 = fieldWeight in 3704, product of:
                  2.4494898 = tf(freq=6.0), with freq of:
                    6.0 = termFreq=6.0
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3704)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Using Google Earth, Google Maps, and/or network visualization programs such as Pajek, one can overlay the network of relations among addresses in scientific publications onto the geographic map. The authors discuss the pros and cons of various options, and provide software (freeware) for bridging existing gaps between the Science Citation Indices (Thomson Reuters) and Scopus (Elsevier), on the one hand, and these various visualization tools on the other. At the level of city names, the global map can be drawn reliably on the basis of the available address information. At the level of the names of organizations and institutes, there are problems of unification both in the ISI databases and with Scopus. Pajek enables a combination of visualization and statistical analysis, whereas the Google Maps and its derivatives provide superior tools on the Internet.
    Object
    Google Maps
  6. Rafols, I.; Porter, A.L.; Leydesdorff, L.: Science overlay maps : a new tool for research policy and library management (2010) 0.05
    0.045934916 = product of:
      0.09186983 = sum of:
        0.09186983 = product of:
          0.18373966 = sum of:
            0.18373966 = weight(_text_:maps in 3987) [ClassicSimilarity], result of:
              0.18373966 = score(doc=3987,freq=6.0), product of:
                0.28477904 = queryWeight, product of:
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.050679237 = queryNorm
                0.6452008 = fieldWeight in 3987, product of:
                  2.4494898 = tf(freq=6.0), with freq of:
                    6.0 = termFreq=6.0
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3987)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    We present a novel approach to visually locate bodies of research within the sciences, both at each moment of time and dynamically. This article describes how this approach fits with other efforts to locally and globally map scientific outputs. We then show how these science overlay maps help benchmarking, explore collaborations, and track temporal changes, using examples of universities, corporations, funding agencies, and research topics. We address their conditions of application and discuss advantages, downsides, and limitations. Overlay maps especially help investigate the increasing number of scientific developments and organizations that do not fit within traditional disciplinary categories. We make these tools available online to enable researchers to explore the ongoing sociocognitive transformations of science and technology systems.
  7. Kraker, P.; Schramm, M.; Kittel, C.: Open knowledge maps : visuelle Literatursuche basierend auf den Prinzipien von Open Science (2019) 0.04
    0.04420089 = product of:
      0.08840178 = sum of:
        0.08840178 = product of:
          0.17680356 = sum of:
            0.17680356 = weight(_text_:maps in 5702) [ClassicSimilarity], result of:
              0.17680356 = score(doc=5702,freq=8.0), product of:
                0.28477904 = queryWeight, product of:
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.050679237 = queryNorm
                0.6208447 = fieldWeight in 5702, product of:
                  2.828427 = tf(freq=8.0), with freq of:
                    8.0 = termFreq=8.0
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=5702)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Die Wissenschaft befindet sich in einer Auffindbarkeitskrise. Obwohl durch die Open Access-Bewegung Forschungsergebnisse besser zugänglich geworden sind, wird ein signifikanter Teil der Outputs nicht nachgenutzt. Einen großen Anteil an der Krise haben die Tools, die für die Literatursuche verwendet werden. Angesichts von drei Millionen Veröffentlichungen pro Jahr sind klassische Ansätze, wie etwa listenbasierte Suchmaschinen, nicht mehr ausreichend. Open Knowledge Maps hat es sich zum Ziel gesetzt, die Auffindbarkeit wissenschaftlichen Wissens zu verbessern. Dafür betreibt die gemeinnützige Organisation aus Österreich die weltweit größte visuelle Suchmaschine für Forschung. Das Grundprinzip besteht darin, Wissenslandkarten für die Literatursuche zu nutzen. Diese geben einen Überblick über ein Forschungsfeld und ermöglichen so einen schnelleren Einstieg in die Literatur. Open Knowledge Maps basiert auf den Prinzipien von Open Science: Inhalte, Daten und Software werden unter einer freien Lizenz veröffentlicht. Dadurch entsteht eine offene, wiederverwendbare Infrastruktur; Lock-In-Effekte, wie sie bei proprietären Systemen auftreten, werden vermieden. Open Knowledge Maps arbeitet seit Beginn eng mit Bibliotheken und BibliothekarInnen als ExpertInnen für Wissensorganisation und -verwaltung zusammen. Im Rahmen eines konsortialen Fördermodells werden Bibliotheken nun eingeladen, das System stärker mitzugestalten - unter anderem bei wichtigen Zukunftsthemen wie der besseren Auffindbarkeit von Datensätzen.
  8. Wen, B.; Horlings, E.; Zouwen, M. van der; Besselaar, P. van den: Mapping science through bibliometric triangulation : an experimental approach applied to water research (2017) 0.04
    0.038279098 = product of:
      0.076558195 = sum of:
        0.076558195 = product of:
          0.15311639 = sum of:
            0.15311639 = weight(_text_:maps in 3437) [ClassicSimilarity], result of:
              0.15311639 = score(doc=3437,freq=6.0), product of:
                0.28477904 = queryWeight, product of:
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.050679237 = queryNorm
                0.53766733 = fieldWeight in 3437, product of:
                  2.4494898 = tf(freq=6.0), with freq of:
                    6.0 = termFreq=6.0
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=3437)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    The idea of constructing science maps based on bibliographic data has intrigued researchers for decades, and various techniques have been developed to map the structure of research disciplines. Most science mapping studies use a single method. However, as research fields have various properties, a valid map of a field should actually be composed of a set of maps derived from a series of investigations using different methods. That leads to the question of what can be learned from a combination-triangulation-of these different science maps. In this paper we propose a method for triangulation, using the example of water science. We combine three different mapping approaches: journal-journal citation relations (JJCR), shared author keywords (SAK), and title word-cited reference co-occurrence (TWRC). Our results demonstrate that triangulation of JJCR, SAK, and TWRC produces a more comprehensive picture than each method applied individually. The outcomes from the three different approaches can be associated with each other and systematically interpreted to provide insights into the complex multidisciplinary structure of the field of water research.
  9. Hook, P.A.; Gantchev, A.: Using combined metadata sources to visualize a small library (OBL's English Language Books) (2017) 0.04
    0.038279098 = product of:
      0.076558195 = sum of:
        0.076558195 = product of:
          0.15311639 = sum of:
            0.15311639 = weight(_text_:maps in 3870) [ClassicSimilarity], result of:
              0.15311639 = score(doc=3870,freq=6.0), product of:
                0.28477904 = queryWeight, product of:
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.050679237 = queryNorm
                0.53766733 = fieldWeight in 3870, product of:
                  2.4494898 = tf(freq=6.0), with freq of:
                    6.0 = termFreq=6.0
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=3870)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Data from multiple knowledge organization systems are combined to provide a global overview of the content holdings of a small personal library. Subject headings and classification data are used to effectively map the combined book and topic space of the library. While harvested and manipulated by hand, the work reveals issues and potential solutions when using automated techniques to produce topic maps of much larger libraries. The small library visualized consists of the thirty-nine, digital, English language books found in the Osama Bin Laden (OBL) compound in Abbottabad, Pakistan upon his death. As this list of books has garnered considerable media attention, it is worth providing a visual overview of the subject content of these books - some of which is not readily apparent from the titles. Metadata from subject headings and classification numbers was combined to create book-subject maps. Tree maps of the classification data were also produced. The books contain 328 subject headings. In order to enhance the base map with meaningful thematic overlay, library holding count data was also harvested (and aggregated from duplicates). This additional data revealed the relative scarcity or popularity of individual books.
  10. Wu, Y.; Bai, R.: ¬An event relationship model for knowledge organization and visualization (2017) 0.04
    0.0375057 = product of:
      0.0750114 = sum of:
        0.0750114 = product of:
          0.1500228 = sum of:
            0.1500228 = weight(_text_:maps in 3867) [ClassicSimilarity], result of:
              0.1500228 = score(doc=3867,freq=4.0), product of:
                0.28477904 = queryWeight, product of:
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.050679237 = queryNorm
                0.5268042 = fieldWeight in 3867, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3867)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    An event is a specific occurrence involving participants, which is a typed, n-ary association of entities or other events, each identified as a participant in a specific semantic role in the event (Pyysalo et al. 2012; Linguistic Data Consortium 2005). Event types may vary across domains. Representing relationships between events can facilitate the understanding of knowledge in complex systems (such as economic systems, human body, social systems). In the simplest form, an event can be represented as Entity A <Relation> Entity B. This paper evaluates several knowledge organization and visualization models and tools, such as concept maps (Cmap), topic maps (Ontopia), network analysis models (Gephi), and ontology (Protégé), then proposes an event relationship model that aims to integrate the strengths of these models, and can represent complex knowledge expressed in events and their relationships.
  11. Cobo, M.J.; López-Herrera, A.G.; Herrera-Viedma, E.; Herrera, F.: Science mapping software tools : review, analysis, and cooperative study among tools (2011) 0.04
    0.035360713 = product of:
      0.070721425 = sum of:
        0.070721425 = product of:
          0.14144285 = sum of:
            0.14144285 = weight(_text_:maps in 4486) [ClassicSimilarity], result of:
              0.14144285 = score(doc=4486,freq=2.0), product of:
                0.28477904 = queryWeight, product of:
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.050679237 = queryNorm
                0.4966758 = fieldWeight in 4486, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.0625 = fieldNorm(doc=4486)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Science mapping aims to build bibliometric maps that describe how specific disciplines, scientific domains, or research fields are conceptually, intellectually, and socially structured. Different techniques and software tools have been proposed to carry out science mapping analysis. The aim of this article is to review, analyze, and compare some of these software tools, taking into account aspects such as the bibliometric techniques available and the different kinds of analysis.
  12. Yan, B.; Luo, J.: Filtering patent maps for visualization of diversification paths of inventors and organizations (2017) 0.03
    0.03125475 = product of:
      0.0625095 = sum of:
        0.0625095 = product of:
          0.125019 = sum of:
            0.125019 = weight(_text_:maps in 3651) [ClassicSimilarity], result of:
              0.125019 = score(doc=3651,freq=4.0), product of:
                0.28477904 = queryWeight, product of:
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.050679237 = queryNorm
                0.43900353 = fieldWeight in 3651, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=3651)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    In the information science literature, recent studies have used patent databases and patent classification information to construct network maps of patent technology classes. In such a patent technology map, almost all pairs of technology classes are connected, whereas most of the connections between them are extremely weak. This observation suggests the possibility of filtering the patent network map by removing weak links. However, removing links may reduce the explanatory power of the network on inventor or organization diversification. The network links may explain the patent portfolio diversification paths of inventors and inventing organizations. We measure the diversification explanatory power of the patent network map, and present a method to objectively choose an optimal tradeoff between explanatory power and removing weak links. We show that this method can remove a degree of arbitrariness compared with previous filtering methods based on arbitrary thresholds, and also identify previous filtering methods that created filters outside the optimal tradeoff. The filtered map aims to aid in network visualization analyses of the technological diversification of inventors, organizations, and other innovation agents, and potential foresight analysis. Such applications to a prolific inventor (Leonard Forbes) and company (Google) are demonstrated.
  13. Buchel, O.; Sedig, K.: Extending map-based visualizations to support visual tasks : the role of ontological properties (2011) 0.03
    0.030940626 = product of:
      0.06188125 = sum of:
        0.06188125 = product of:
          0.1237625 = sum of:
            0.1237625 = weight(_text_:maps in 2307) [ClassicSimilarity], result of:
              0.1237625 = score(doc=2307,freq=2.0), product of:
                0.28477904 = queryWeight, product of:
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.050679237 = queryNorm
                0.43459132 = fieldWeight in 2307, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.0546875 = fieldNorm(doc=2307)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Map-based visualizations of document collections have become popular in recent times. However, most of these visualizations emphasize only geospatial properties of objects, leaving out other ontological properties. In this paper we propose to extend these visualizations to include nongeospatial properties of documents to support users with elementary and synoptic visual tasks. More specifically, additional suitable representations that can enhance the utility of map-based visualizations are discussed. To demonstrate the utility of the proposed solution, we have developed a prototype map-based visualization system using Google Maps (GM), which demonstrates how additional representations can be beneficial.
  14. Su, H.-N.: Visualization of global science and technology policy research structure (2012) 0.03
    0.026520537 = product of:
      0.053041074 = sum of:
        0.053041074 = product of:
          0.10608215 = sum of:
            0.10608215 = weight(_text_:maps in 4969) [ClassicSimilarity], result of:
              0.10608215 = score(doc=4969,freq=2.0), product of:
                0.28477904 = queryWeight, product of:
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.050679237 = queryNorm
                0.37250686 = fieldWeight in 4969, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.046875 = fieldNorm(doc=4969)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    This study proposes an approach for visualizing knowledge structures that creates a "research-focused parallelship network," "keyword co-occurrence network," and a knowledge map to visualize Sci-Tech policy research structure. A total of 1,125 Sci-Tech policy-related papers (873 journal papers [78%], 205 conference papers [18%], and 47 review papers [4%]) have been retrieved from the Web of Science database for quantitative analysis and mapping. Different network and contour maps based on these 1,125 papers can be constructed by choosing different information as the main actor, such as the paper title, the institute, the country, or the author keywords, to reflect Sci-Tech policy research structures in micro-, meso-, and macro-levels, respectively. The quantitative way of exploring Sci-Tech policy research papers is investigated to unveil important or emerging Sci-Tech policy implications as well as to demonstrate the dynamics and visualization of the evolution of Sci-Tech policy research.
  15. Osiñska, V.: Visual analysis of classification scheme (2010) 0.02
    0.022100445 = product of:
      0.04420089 = sum of:
        0.04420089 = product of:
          0.08840178 = sum of:
            0.08840178 = weight(_text_:maps in 4068) [ClassicSimilarity], result of:
              0.08840178 = score(doc=4068,freq=2.0), product of:
                0.28477904 = queryWeight, product of:
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.050679237 = queryNorm
                0.31042236 = fieldWeight in 4068, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=4068)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    This paper proposes a novel methodology to visualize a classification scheme. It is demonstrated with the Association for Computing Machinery (ACM) Computing Classification System (CCS). The collection derived from the ACM digital library, containing 37,543 documents classified by CCS. The assigned classes, subject descriptors, and keywords were processed in a dataset to produce a graphical representation of the documents. The general conception is based on the similarity of co-classes (themes) proportional to the number of common publications. The final number of all possible classes and subclasses in the collection was 353 and therefore the similarity matrix of co-classes had the same dimension. A spherical surface was chosen as the target information space. Classes and documents' node locations on the sphere were obtained by means of Multidimensional Scaling coordinates. By representing the surface on a plane like a map projection, it is possible to analyze the visualization layout. The graphical patterns were organized in some colour clusters. For evaluation of given visualization maps, graphics filtering was applied. This proposed method can be very useful in interdisciplinary research fields. It allows for a great amount of heterogeneous information to be conveyed in a compact display, including topics, relationships among topics, frequency of occurrence, importance and changes of these properties over time.
  16. Zhang, Y.; Zhang, G.; Zhu, D.; Lu, J.: Scientific evolutionary pathways : identifying and visualizing relationships for scientific topics (2017) 0.02
    0.022100445 = product of:
      0.04420089 = sum of:
        0.04420089 = product of:
          0.08840178 = sum of:
            0.08840178 = weight(_text_:maps in 3758) [ClassicSimilarity], result of:
              0.08840178 = score(doc=3758,freq=2.0), product of:
                0.28477904 = queryWeight, product of:
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.050679237 = queryNorm
                0.31042236 = fieldWeight in 3758, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=3758)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Whereas traditional science maps emphasize citation statistics and static relationships, this paper presents a term-based method to identify and visualize the evolutionary pathways of scientific topics in a series of time slices. First, we create a data preprocessing model for accurate term cleaning, consolidating, and clustering. Then we construct a simulated data streaming function and introduce a learning process to train a relationship identification function to adapt to changing environments in real time, where relationships of topic evolution, fusion, death, and novelty are identified. The main result of the method is a map of scientific evolutionary pathways. The visual routines provide a way to indicate the interactions among scientific subjects and a version in a series of time slices helps further illustrate such evolutionary pathways in detail. The detailed outline offers sufficient statistical information to delve into scientific topics and routines and then helps address meaningful insights with the assistance of expert knowledge. This empirical study focuses on scientific proposals granted by the United States National Science Foundation, and demonstrates the feasibility and reliability. Our method could be widely applied to a range of science, technology, and innovation policy research, and offer insight into the evolutionary pathways of scientific activities.
  17. Lin, F.-T.: Drawing a knowledge map of smart city knowledge in academia (2019) 0.02
    0.022100445 = product of:
      0.04420089 = sum of:
        0.04420089 = product of:
          0.08840178 = sum of:
            0.08840178 = weight(_text_:maps in 5454) [ClassicSimilarity], result of:
              0.08840178 = score(doc=5454,freq=2.0), product of:
                0.28477904 = queryWeight, product of:
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.050679237 = queryNorm
                0.31042236 = fieldWeight in 5454, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  5.619245 = idf(docFreq=435, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=5454)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    This research takes the academic articles in the Web of Science's core collection database as a corpus to draw a series of knowledge maps, to explore the relationships, connectivity, dis-tribution, and evolution among their keywords with respect to smart cities in the last decade. Beyond just drawing a text cloud or measuring their sizes, we further explore their texture by iden-tifying the hottest keywords in academic articles, construct links between and among them that share common keywords, identify islands, rocks, reefs that are formed by connected articles-a metaphor inspired by Ong et al. (2005)-and analyze trends in their evolution. We found the following phenomena: 1) "Internet of Things" is the most frequently mentioned keyword in recent research articles; 2) the numbers of islands and reefs are increas-ing; 3) the evolutions of the numbers of weighted links have frac-tal-like structure; and, 4) the coverage of the largest rock, formed by articles that share a common keyword, in the largest island is converging into around 10% to 20%. These phenomena imply that a common interest in the technology of smart cities has been emerging among researchers. However, the administrative, social, economic, and cultural issues need more attention in academia in the future.
  18. Platis, N. et al.: Visualization of uncertainty in tag clouds (2016) 0.02
    0.017165843 = product of:
      0.034331687 = sum of:
        0.034331687 = product of:
          0.06866337 = sum of:
            0.06866337 = weight(_text_:22 in 2755) [ClassicSimilarity], result of:
              0.06866337 = score(doc=2755,freq=2.0), product of:
                0.17747006 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.050679237 = queryNorm
                0.38690117 = fieldWeight in 2755, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.078125 = fieldNorm(doc=2755)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Date
    1. 2.2016 18:25:22
  19. Börner, K.: Atlas of knowledge : anyone can map (2015) 0.01
    0.014565702 = product of:
      0.029131403 = sum of:
        0.029131403 = product of:
          0.058262806 = sum of:
            0.058262806 = weight(_text_:22 in 3355) [ClassicSimilarity], result of:
              0.058262806 = score(doc=3355,freq=4.0), product of:
                0.17747006 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.050679237 = queryNorm
                0.32829654 = fieldWeight in 3355, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.046875 = fieldNorm(doc=3355)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Date
    22. 1.2017 16:54:03
    22. 1.2017 17:10:56
  20. Jäger-Dengler-Harles, I.: Informationsvisualisierung und Retrieval im Fokus der Infromationspraxis (2013) 0.01
    0.010299506 = product of:
      0.020599011 = sum of:
        0.020599011 = product of:
          0.041198023 = sum of:
            0.041198023 = weight(_text_:22 in 1709) [ClassicSimilarity], result of:
              0.041198023 = score(doc=1709,freq=2.0), product of:
                0.17747006 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.050679237 = queryNorm
                0.23214069 = fieldWeight in 1709, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.046875 = fieldNorm(doc=1709)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Date
    4. 2.2015 9:22:39

Languages

  • e 22
  • d 2
  • a 1
  • More… Less…

Types

  • a 23
  • el 5
  • m 1
  • x 1
  • More… Less…