SAR: A Graph-Based System with Text Stream Burst Detection and Visualization
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.
KeywordsGraph 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.
- 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
- 5.Weng, J., Lee, B.-S.: Event detection in twitter. In: ICWSM, vol. 11, pp. 401–408 (2011)Google Scholar
- 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.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
- 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
- 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
- 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
- 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