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Visualizing Large Graphs Out of Unstructured Data for Competitive Intelligence Purposes

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Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016 (IntelliSys 2016)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 15))

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Abstract

In the information era, people’s lives are deeply impacted by IT via exposure to social media, emails, RSS feed, chats, web pages, etc. Such data is considered very valuable nowadays since it may help companies to better their strategies. For example, companies can analyse their customers’ trends or their competitors marketing interventions and adjust their strategies accordingly. Several decisional tools have been developed but most of them rely on relational databases. This makes it difficult for decision makers to take advantage of unstructured data which today represents more than 85% of the available data. Thus, there is a rising need for a suitable management process of unstructured data through collecting, managing, transferring and transforming it into a meaningful informed data. This paper will introduce a new tool for Big Unstructured Data for the Competitive Intelligence named Xplor EveryWhere (XEW). It will also describe the enhancement brought to its newest feature XEWGraph. This tool, or as described later on the paper, this “Service”, offers the decision makers the possibility to have a better user experience regarding large graph visualization on their web browsers as well as their mobile devices.

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References

  1. Abdullah, M.F., Ahmad, K.: The mapping process of unstructured data to structured data. In: 3rd Conference on Research and Innovation in Information Systems, ICRIIS (2013)

    Google Scholar 

  2. Abidin, S.Z.Z., Idris, N.M., Husain, A.H. Extraction and Classification of Unstructured Data in Web Pages for Structured Multimedia Database via XML. IEEE (2010)

    Google Scholar 

  3. Blumberg, R., Atre, S.: The problem with unstructured data. DM Rev. 13, 42–46 (2003)

    Google Scholar 

  4. Spence, R.: The issues. In: Information Visualization: Design for Interaction, 2nd edn., pp. 16–28. ACM Press, New York (2007)

    Google Scholar 

  5. Chu, E., Baid, A., Chen, T., Doan, A., Naughton, J.: A relational approach to incrementally extracting and querying structure in unstructured data. In: Proceedings of the 33rd International Conference on Very Large Databases, vol. VLDB Endowment (2007)

    Google Scholar 

  6. Doan, A., Naughton, J.F., Baid, A., Chai, X., Chen, F., Chen, T., Chu, E., DeRose, P., Gao, B., Gokhale, C., Huang, J., Shen, W., Vuong, B.Q.: The case for a structured approach to managing unstructured data. arXiv preprint arXiv:0909.1783 (2009)

  7. Eads, P.: A heuristic for graph drawing. Congr. Numer. 42, 149–160 (1984)

    MathSciNet  Google Scholar 

  8. Fruchterman, T.M.J., Reingold, E.M.: Graph Drawing by Force-Directed Placement. Software: Practice and Experience, pp. 1129–1164. Wiley, Software (1991)

    Google Scholar 

  9. Gajer, P., Goodrich, M.T., Kobourov, S.G.: A Fast Multidimensional Algorithm for Drawing Large Graphs. Lecture Notes on Computer Sciences, pp. 211–221. Springer, Berlin (2000)

    Google Scholar 

  10. Geetha, S., Mala, G.S.A.: Effectual extraction of data relations from unstructured data. In: 3rd International Conference on Sustainable Energy and Intelligent System, VCTW (2012)

    Google Scholar 

  11. Hadani, R., Harel, D.: A Multi-scale Algorithm for Drawing Graphs Nicely. Discrete Applied Mathematics, pp. 3–21. Elsevier, Amsterdam (2001)

    Google Scholar 

  12. Hall, K.M.: An r-dimensional quadratic placement algorithm. Manag. Sci. Informs J. Comput. 17(3), 219–229 (1970)

    MATH  Google Scholar 

  13. Harel, D., Koren, Y.: High dimensional embedding. J. Graph Algorithms Appl. Brown Univ. 8(2), 195–214 (2004)

    Article  MATH  Google Scholar 

  14. Hu, Y., Shi, L.: Visualizing large graphs, pp. 115–136. Wiley Interdisciplinary Reviews: Computational Statistics. Wiley Periodicals Inc., New York (2015)

    Google Scholar 

  15. Jacomy, M., Venturini, T., Heymann, S., Bastian, M.: ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software. PLoS ONE 9, 1–12 (2014)

    Google Scholar 

  16. Kamada, T., Kawai, S.: An Algorithm for Drawing General Undirected Graphs. Information Processing Letters, pp. 7–15. Elsevier, Amsterdam (1989)

    MATH  Google Scholar 

  17. Koren, Y., Carmel, L., Harel, D.: Ace: a fast multiscale eigenvectors computation for drawing huge graphs. In: Proceedings of the IEEE Symposium on Information Visualization (InfoVis 2002), pp. 137–144 (2002)

    Google Scholar 

  18. Liu, X., Lang, B., Yu, W., Luo, J., Huang, L.: AUDR: an advanced unstructured data repository. In: 6th International Conference on Pervasive Computing and Applications (ICPCA). IEEE (2011)

    Google Scholar 

  19. Lomotey, R.K., Deters, R.: Topics and terms mining in unstructured data stores. In: 16th International Conference on Computational Science and Engineering. IEEE (2013)

    Google Scholar 

  20. Loubier, E.: Analyse et visualisation de données relationnelles par morphing de graphe prenant en compte la dimension temporelle. Ph.D. thesis, IRIT, Paul Sabatier University (2009)

    Google Scholar 

  21. Mansuri, I.R., Sarawagi, S. Integrating unstructured data into relational databases. In: Proceedings of the 22nd International Conference on Data Engineering, ICDE 2006. IEEE (2006)

    Google Scholar 

  22. Noack, A.: An energy model for visual graph clustering. In: Proceedings of the 11th International Symposium on Graph Drawing (GD 2003). LNCS, vol. 2912, pp. 425–436. Springer, Berlin (2004)

    Google Scholar 

  23. Noack, A.: Energy models for graph clustering. J. Graph Algorithms Appl. 11(2), 453–480 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  24. Plejic, B., Vujnovic, B., Penco, R.: Transforming unstructured data from scattered sources into knowledge. In: IEEE International Symposium on Knowledge Acquisition and Modeling Workshop, 2008. KAM Workshop 2008, pp. 924–927 (2008)

    Google Scholar 

  25. Purchase, H.C.: Performance of layout algorithms: comprehension, not computation. J. Visual Lang. Comput. 9(6), 647–657 (1998)

    Article  Google Scholar 

  26. Quigley, A.: Large Scale Relational Information Visualization, Clustering, and Abstraction. Ph.D. Thesis, Department of Computer Science and Software Engineering, University of Newcastle, Australia (2001)

    Google Scholar 

  27. Sequeda, J., Miranker, D.P.: Linked Data. Linked Data tutorial at Semtech. (2010). http://fr.slideshare.net/juansequeda/linked-data-tutorial-at-semtech-2012

  28. Tari, L., Tu, P.H., Hakenberg, J., Chen, Y., Son, T.C., Gonzalez, G., Baral, C.: Parse tree database for information extraction. IEEE Trans. Knowl. Data Eng. (2010).http://www.public.asu.edu/~cbaral/papers/tkde10.pdf

  29. Tunkelang, D.: A numerical optimization approach to general graph drawing. Ph.D. Thesis, Carnegie Mellon University (1999)

    Google Scholar 

  30. Tutte, W.T.: How to draw a graph. In: Proceedings of the London Mathematical Society, pp. 743–767 (1963)

    Google Scholar 

  31. Vishal Gupta, G.S.L.: A survey of text mining technics and applications. J. Emerg. Technol. Web Intell. 1(1), 60–76 (2009)

    Google Scholar 

  32. Yafooz, W.M.S., Abidin, S.Z.Z., Omar, N.: Towards automatic column-based data object clustering for multilingual databases. In: IEEE International Conference on Control System, Computing and Engineering (ICCSCE), IEEE (2011)

    Google Scholar 

  33. Yafooz, W.M.S., Abidin, S.Z.Z., Omar, N., Idrus, Z.: Managing unstructured data in relational database. In: IEEE Conference on Systems, Process & Control (ICSPC) (2013)

    Google Scholar 

  34. Zikopoulos, P.C., Eaton, C., DeRoos, D., Deutsch, T., Lapis, G.: Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. McGraw-Hill, New York (2012)

    Google Scholar 

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Author information

Authors and Affiliations

  1. LabSIV, Faculty of Sciences, Ibn Zohr University, Agadir, Morocco

    Zakaria Boulouard & Lahcen Koutti

  2. LabTEC, National School of Applied Sciences, Tangier, Morocco

    Nihal Chouati

  3. SIG, Institut de Recherche en Informatique de Toulouse, Toulouse, France

    Amine El Haddadi & Bernard Dousset

  4. Mathematics and Computer Sciences Department, National School of Applied Sciences, Al Hoceima, Morocco

    Anass El Haddadi & Fadwa Bouhafer

Authors
  1. Zakaria Boulouard
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  2. Lahcen Koutti
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  3. Nihal Chouati
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  4. Amine El Haddadi
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  5. Bernard Dousset
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  6. Anass El Haddadi
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  7. Fadwa Bouhafer
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Corresponding author

Correspondence to Zakaria Boulouard .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Engineering, School of Computing and Mathematics, University of Ulster at Jordanstown, Newtownabbey, United Kingdom

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Rahul Bhatia

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Boulouard, Z. et al. (2018). Visualizing Large Graphs Out of Unstructured Data for Competitive Intelligence Purposes. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. IntelliSys 2016. Lecture Notes in Networks and Systems, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-319-56994-9_41

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  • DOI: https://doi.org/10.1007/978-3-319-56994-9_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56993-2

  • Online ISBN: 978-3-319-56994-9

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