Home / Journals / JBD / Vol.3, No.4, 2021
  • Open Access

    ARTICLE

    Application of Quicksort Algorithm in Information Retrieval

    Jiajun Xie1, Zuyan Li1, Han Wu1, Linhan Li2, Bin Pan1, Peng Guo3,Guang Sun1,*
    Journal on Big Data, Vol.3, No.4, pp. 135-145, 2021, DOI:10.32604/jbd.2021.017017
    Abstract With the development and progress of today’s network information technology, a variety of large-scale network databases have emerged with the situation, such as Baidu Library and Weipu Database, the number of documents in the inventory has reached nearly one million. So how do you quickly and effectively retrieve the information you want in such a huge database? This requires finding efficient algorithms to reduce the computational complexity of the computer during Information Retrieval, improve retrieval efficiency, and adapt to the rapid expansion of document data. The Quicksort Algorithm gives different weights to each position of the document, and multiplies the… More >

  • Open Access

    ARTICLE

    Design of Cybersecurity Threat Warning Model Based on Ant Colony Algorithm

    Weiwei Lin1,2,*, Reiko Haga3
    Journal on Big Data, Vol.3, No.4, pp. 147-153, 2021, DOI:10.32604/jbd.2021.017299
    Abstract In this paper, a cybersecurity threat warning model based on ant colony algorithm is designed to strengthen the accuracy of the cybersecurity threat warning model in the warning process and optimize its algorithm structure. Through the ant colony algorithm structure, the local global optimal solution is obtained; and the cybersecurity threat warning index system is established. Next, the above two steps are integrated to build the cybersecurity threat warning model based on ant colony algorithm, and comparative experiment is also designed. The experimental results show that, compared with the traditional qualitative differential game-based cybersecurity threat warning model, the cybersecurity threat… More >

  • Open Access

    ARTICLE

    Can Twitter Sentiment Gives the Weather of the Financial Markets?

    Imen Hamraoui*, Adel Boubaker
    Journal on Big Data, Vol.3, No.4, pp. 155-173, 2021, DOI:10.32604/jbd.2021.018703
    Abstract Finance 3.0 is still in its infancy. Yet big data represents an unprecedented opportunity for finance. The massive increase in the volume of data generated by individuals every day on the Internet offers researchers the opportunity to approach the question of financial market predictability from a new perspective. In this article, we study the relationship between a well-known Twitter micro-blogging platform and the Tunisian financial market. In particular, we consider, over a 12-month period, Twitter volume and sentiment across the 22 stock companies that make up the Tunindex index. We find a relatively weak Pearson correlation and Granger causality between… More >

  • Open Access

    ARTICLE

    Research on the Application of Big Data Technology in the Integration of Enterprise Business and Finance

    Hanbo Liu*, Guang Sun
    Journal on Big Data, Vol.3, No.4, pp. 175-182, 2021, DOI:10.32604/jbd.2021.024074
    Abstract With the advent of the era of big data, traditional financial management has been unable to meet the needs of modern enterprise business. Enterprises hope that financial management has the function of improving the accuracy of corporate financial data, assisting corporate management to make decisions that are more in line with the actual development of the company, and optimizing corporate management systems, thereby comprehensively improving the overall level of the company and ensuring that the company can be in business with the assistance of financial integration, can better improve and develop themselves. Based on the investigation of enterprises and universities,… More >

  • Open Access

    ARTICLE

    A Lightning Disaster Risk Assessment Model Based on SVM

    Jianqiao Sheng1, Mengzhu Xu2, Jin Han3,*, Xingyan Deng2
    Journal on Big Data, Vol.3, No.4, pp. 183-190, 2021, DOI:10.32604/jbd.2021.024892
    Abstract Lightning disaster risk assessment, as an intuitive method to reflect the risk of regional lightning disasters, has aroused the research interest of many researchers. Nowadays, there are many schemes for lightning disaster risk assessment, but there are also some shortcomings, such as the resolution of the assessment is not clear enough, the accuracy rate cannot be verified, and the weight distribution has a strong subjective trend. This paper is guided by lightning disaster data and combines lightning data, population data and GDP data. Through support vector machine (SVM), it explores a way to combine artificial intelligence algorithms with lightning disaster… More >

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