Visual trend analysis with digital libraries

The early awareness of new technologies and upcoming trends is essential for making strategic decisions in enterprises and research. Trends may signal that technologies or related topics might be of great interest in the future or obsolete for future directions. The identification of such trends premises analytical skills that can be supported through trend mining and visual analytics. Thus the earliest trends or signals commonly appear in science, the investigation of digital libraries in this context is inevitable. However, digital libraries do not provide sufficient information for analyzing trends. It is necessary to integrate data, extract information from the integrated data and provide effective interactive visual analysis tools. We introduce in this paper a model that investigates all stages from data integration to interactive visualization for identifying trends and analyzing the market situation through our visual trend analysis environment. Our approach improves the visual analysis of trends by investigating the entire transformation steps from raw and structured data to visual representations.

[1]  Cai-Nicolas Ziegler,et al.  Towards Automated Reputation and Brand Monitoring on the Web , 2006, 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06).

[2]  Rosane Minghim,et al.  Text Map Explorer: a Tool to Create and Explore Document Maps , 2006, Tenth International Conference on Information Visualisation (IV'06).

[3]  Lucy T. Nowell,et al.  ThemeRiver: Visualizing Thematic Changes in Large Document Collections , 2002, IEEE Trans. Vis. Comput. Graph..

[4]  Kawa Nazemi,et al.  Adaptive Semantics Visualization , 2016, Studies in Computational Intelligence.

[5]  Toshinori Munakata,et al.  Knowledge discovery , 1999, Commun. ACM.

[6]  M. Sheelagh T. Carpendale,et al.  SparkClouds: Visualizing Trends in Tag Clouds , 2010, IEEE Transactions on Visualization and Computer Graphics.

[7]  Cai-Nicolas Ziegler,et al.  Tracking Topic Evolution in News Environments , 2008, 2008 10th IEEE Conference on E-Commerce Technology and the Fifth IEEE Conference on Enterprise Computing, E-Commerce and E-Services.

[8]  David Glance,et al.  Making the Most of Citation Data: The Integration of Thomson Reuters Web of Science and UWA's Research Management System, Socrates , 2009 .

[9]  Chaomei Chen,et al.  CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature , 2006, J. Assoc. Inf. Sci. Technol..

[10]  Ramakrishnan Srikant,et al.  Discovering Trends in Text Databases , 1997, KDD.

[11]  Mary Czerwinski,et al.  Understanding research trends in conferences using paperLens , 2005, CHI Extended Abstracts.

[12]  David Newman,et al.  Are learned topics more useful than subject headings , 2011, JCDL '11.

[13]  Yonatan Aumann,et al.  Trend Graphs: Visualizing the Evolution of Concept Relationships in Large Document Collections , 1998, PKDD.

[14]  Dunja Mladenic,et al.  Visualization of Text Document Corpus , 2005, Informatica.

[15]  ChengXiang Zhai,et al.  Discovering evolutionary theme patterns from text: an exploration of temporal text mining , 2005, KDD '05.

[16]  Kawa Nazemi,et al.  Adaptive Semantic Visualization for Bibliographic Entries , 2013, ISVC.

[17]  Ben Shneiderman,et al.  Readings in information visualization - using vision to think , 1999 .

[18]  Ludo Waltman,et al.  CitNetExplorer: A new software tool for analyzing and visualizing citation networks , 2014, J. Informetrics.

[19]  Shimei Pan,et al.  TIARA: Interactive, Topic-Based Visual Text Summarization and Analysis , 2012, TIST.

[20]  Iraklis Varlamis,et al.  How to Become a Group Leader? or Modeling Author Types Based on Graph Mining , 2011, TPDL.

[21]  Sung-Hyon Myaeng,et al.  Automatic discovery of technology trends from patent text , 2009, SAC '09.

[22]  Dunja Mladenic,et al.  Visualization of News Articles , 2004, Informatica.

[23]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[24]  Ido Dagan,et al.  Knowledge Discovery in Textual Databases (KDT) , 1995, KDD.

[25]  Peter Bergström,et al.  Augmenting the exploration of digital libraries with web-based visualizations , 2009, 2009 Fourth International Conference on Digital Information Management.

[26]  Alexander F. Gelbukh,et al.  Mining the News: Trends, Associations, and Deviations , 2001, Computación y Sistemas.

[27]  Martin Wattenberg,et al.  Parallel Tag Clouds to explore and analyze faceted text corpora , 2009, 2009 IEEE Symposium on Visual Analytics Science and Technology.

[28]  Jia-Kai Chou,et al.  PaperVis: Literature Review Made Easy , 2011, Comput. Graph. Forum.

[29]  William Ribarsky,et al.  ParallelTopics: A probabilistic approach to exploring document collections , 2011, 2011 IEEE Conference on Visual Analytics Science and Technology (VAST).

[30]  Lyle Ungar,et al.  Discovery of significant emerging trends , 2010, KDD.

[31]  Kwan-Liu Ma,et al.  BiblioViz: a system for visualizing bibliography information , 2006, APVIS.