Conference paper Open Access

Visual Trend Analytics in Digital Libraries

Nazemi, Kawa

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  <identifier identifierType="DOI">10.5281/zenodo.3264801</identifier>
      <creatorName>Nazemi, Kawa</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="">0000-0002-2907-2740</nameIdentifier>
      <affiliation>Darmstadt University of Applied Sciences</affiliation>
    <title>Visual Trend Analytics in Digital Libraries</title>
    <subject>digital library</subject>
    <date dateType="Issued">2019-07-01</date>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3264800</relatedIdentifier>
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    <rights rightsURI="">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;The early awareness of upcoming trends in technology enables a more goal-directed and efficient way for deciding future strategic directions in enterprises and research. Possible sources for this valuable information are ubiquitously and freely available in the Web, e.g. news services, companies&amp;rsquo; reports, social media platforms and blog infrastructures. To support users in handling these information sources and to keep track of the newest developments, current information systems make intensively use of information retrieval methods that extract relevant information out of the mass amount of data. The related information systems are commonly focused on providing users with easy access to information of their interest and deal with the access to information items and resources [1], but they neither provide an overview of the content nor enable the exploration of emerging or decreasing trends for inferring possible future innovations. The gathering and analysis of this continuously increasing knowledge pool is a very tedious and time-consuming task and borders on the limits of manual feasibility. The interactive overview on data, the continuous changes in data, and the ability to explore data and gain insights are sufficiently supported by Visual Analytics and information visualization approaches, whereas the appliance of such approach in combination with trend analysis are rarely propagated. In fact, these so-called early signals require not only an analysis through machine learning techniques to identify emerging trends, but also human interaction and intervention to adapt the parameters used to their own needs [2]. There are two main aspects to consider in the analysis process: 1) which data reveal very early trends and 2) how can human be involved in the analysis process [3].&lt;/p&gt;</description>
    <description descriptionType="Other">{"references": ["Baeza-Yates, R. and Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley Publishing Company, 2nd edition, 2010.", "Obermaier, H.; Bensema, K. &amp; Joy, K. I.: Visual Trends Analysis in Time-Varying Ensembles. IEEE Transactions on Visualization and Computer Graphics, DOI= 10.1109/TVCG.2015.2507592, 2016", "Nazemi, K., Retz, R., Burkhardt, D., Kuijper, A., Kohlhammer, J. and Fellner, D. W: Visual trend analysis with digital libraries. In Proceedings of i-KNOW '15. ACM, New York, NY, USA, Article 14 DOI=, 2015", "Blei, D. M, Ng, A. Y and Jordan, M. I.: Latent Dirichlet Allocation. Journal of Machine Learning Research 3 (2003) 993-1022., 2003", "Keim D., Kohlhammer J., Ellis G., Mansmann F.: Mastering the Information Age Solving Problems with Visual Analytics. Eurographics Association,, 2010", "Nazemi, K.: Adaptive Semantics Visualization. Studies in Computational Intelligence. Springer International Publishing,, 2016"]}</description>
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