SAR: A Graph-Based System with Text Stream Burst Detection and Visualization

  • Tham Vo Thi HongEmail author
  • Phuc Do
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 866)


Smart city trend with Artificial Intelligence, Internet Of Thing and Data Science has been attracting a lot of attention. Following this trend, smart applications that help users improve their quality of life, as well as work, has been investigating by many researchers. In an era of industry 4.0, collecting and exploiting information automatically is essential so that many studies have proposed models for solving storage problems and supporting efficient data processing. In this paper, we introduce our proposed graph-based system called SAR (Smart Article Reader) that can store, analyze, exploit and visualize text streams. This system first gathers daily articles automatically from online journals. After articles are collected, keywords’ frequency of existence is calculated to rank the importance of keywords, finding worthy topics and visually display the results from user requests. Especially, we present the application of Burst Detection technique for detecting periods of time in which some keywords are unusually popular. This technique is used for finding trends from online journals. In addition, we present our method for rating keywords, which share similar Bursts patterns, based on their term frequencies. We also perform system algorithm testing and evaluation to show its performance and estimate its responding time.


Graph database Visualization Keyword extraction Burst detection 



This research is funded by Vietnam National University Ho Chi Minh City (VNU-HCMC) under the grant number B2017-26-02.


  1. 1.
    Kleinberg, J., Bursty and hierarchical structure in streams. Data Mining and Knowledge Discovery, 2003. 7(4): p. 373-397MathSciNetCrossRefGoogle Scholar
  2. 2.
    Kürüm, E., G.-W. Weber, and C. Iyigun, Early warning on Stock Market Bubbles via methods of optimization, clustering and inverse problems. Annals of Operations Research, 2018. 260(1-2): p. 293-320MathSciNetCrossRefGoogle Scholar
  3. 3.
    Vlachos, M., et al. Identifying similarities, periodicities and bursts for online search queries. In: Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data. ACMGoogle Scholar
  4. 4.
    Bakkum, D.J., et al., Parameters for burst detection. Frontiers in computational neuroscience, 2014. 7: p. 193CrossRefGoogle Scholar
  5. 5.
    Weng, J., Lee, B.-S.: Event detection in twitter. In: ICWSM, vol. 11, pp. 401–408 (2011)Google Scholar
  6. 6.
    Romsaiyud, W.: Detecting emergency events and geo-location awareness from twitter streams. In: The International Conference on E-Technologies and Business on the Web (EBW2013). The Society of Digital Information and Wireless Communication (2013)Google Scholar
  7. 7.
    Fung, G.P.C., et al.: Parameter free bursty events detection in text streams. In: Proceedings of the 31st International Conference on Very Large Data Bases. VLDB Endowment (2005)Google Scholar
  8. 8.
    van Pelt, J., et al., Long-term characterization of firing dynamics of spontaneous bursts in cultured neural networks. IEEE Transactions on Biomedical Engineering, 2004. 51(11): p. 2051-2062CrossRefGoogle Scholar
  9. 9.
    Wagenaar, D., DeMarse, T.B., Potter, S.M.: MeaBench: a toolset for multi-electrode data acquisition and on-line analysis. In: 2nd International IEEE EMBS Conference on Neural Engineering, 2005. Conference Proceedings. IEEE (2005)Google Scholar
  10. 10.
    Lee, S., Y. Park, and W.C. Yoon, Burst analysis for automatic concept map creation with a single document. Expert Systems with Applications, 2015. 42(22): p. 8817-8829CrossRefGoogle Scholar
  11. 11.
    Lee, D., Lee, W.: Finding maximal frequent itemsets over online data streams adaptively. In: Fifth IEEE International Conference on Data Mining (ICDM’05). IEEE (2005)Google Scholar
  12. 12.
    Heydari, A., et al., Detection of review spam: A survey. Expert Systems with Applications, 2015. 42(7): p. 3634-3642MathSciNetCrossRefGoogle Scholar
  13. 13.
    Zhang, Y., W. Hua, and S. Yuan, Mapping the scientific research on open data: A bibliometric review. Learned Publishing, 2018. 31(2): p. 95-106CrossRefGoogle Scholar
  14. 14.
    Khaing, P.P., New, N.: 2017 IEEE/ACIS 16th International Conference on Adaptive methods for efficient burst and correlative burst detection. in Computer and Information Science (ICIS), IEEE (2017)Google Scholar
  15. 15.
    Yamamoto, S., et al., Twitter user tagging method based on burst time series. International Journal of Web Information Systems, 2016. 12(3): p. 292-311MathSciNetCrossRefGoogle Scholar
  16. 16.
    Hong, T.V.T., Do, P.: Developing a graph-based system for storing, exploiting and visualizing text stream. In: Proceedings of the 2nd International Conference on Machine Learning and Soft Computing. ACM (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Lac Hong UniversityBiên HòaVietnam
  2. 2.Thu Dau Mot UniversityThủ Dầu MộtVietnam
  3. 3.University of Information Technology, VNU-HCMThu Du, Ho Chi MinhVietnam

Personalised recommendations