The Ranking of Deep Web Sources Based on Data Quality

被引:0
|
作者
Yin, Hu [1 ]
Lv, Yunfei [2 ]
Wang, Weiwei [2 ]
机构
[1] 719 Inst Technol Wuhan, Wuhan, Peoples R China
[2] Wuhan Second Ship Design Inst, Wuhan, Peoples R China
关键词
Sampling estimates; Data quality; Quality Vector; Deep Web ranking;
D O I
10.4028/www.scientific.net/AMM.303-306.2437
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Deep Web technology makes a large number of useful information which hidden behind the interface easier to be found by users. However, with the increase of data source, how to find a suitable result quickly from a number of sources is becoming more and more important. In this paper, we start discussing from the quality of the data, setting 6 quality standards for the data source and giving the method of calculation. Meanwhile, we solve corresponding weight vector of quality standards by the feeling of the users; and based on this quality standards, we calculate a random data source according to weight vector to gain a general score. Then this paper discusses the sampling theory and proposes a reasonable sampling method for the experiment. The experiment result shows that it is of good veracity and operability to evaluate and score the data quality of data source according to sampling analysis.
引用
收藏
页码:2437 / +
页数:2
相关论文
共 50 条
  • [41] A quality-driven approach for ranking web services
    Al-Masri, Eyhab
    Lecture Notes in Electrical Engineering, 2015, 312 : 599 - 606
  • [42] Ranking Spatial Data by Quality Preferences
    Yiu, Man Lung
    Lu, Hua
    Mamoulis, Nikos
    Vaitis, Michail
    IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2011, 23 (03) : 433 - 446
  • [43] Improving search ranking of geospatial data based on deep learning using user behavior data
    Li, Yun
    Jiang, Yongyao
    Yang, Chaowei
    Yu, Manzhu
    Kamal, Lara
    Armstrong, Edward M.
    Huang, Thomas
    Moroni, David
    McGibbney, Lewis J.
    COMPUTERS & GEOSCIENCES, 2020, 142
  • [44] Data extraction from Web data sources
    Robinson, J
    15TH INTERNATIONAL WORKSHOP ON DATABASE AND EXPERT SYSTEMS APPLICATIONS, PROCEEDINGS, 2004, : 282 - 288
  • [45] Answering Cross-Source Keyword Queries over Deep Web Data Sources
    Wang, Fan
    Agrawal, Gagan
    CONTEMPORARY COMPUTING, 2011, 168 : 475 - 490
  • [46] Quality-Based Learning for Web Data Classification
    Wu, Ou
    Hu, Ruiguang
    Mao, Xue
    Hu, Weiming
    PROCEEDINGS OF THE TWENTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2014, : 194 - 200
  • [47] A context-aware entity ranking method for web-based data imputation
    Chen, Zhao-Qiang
    Li, Jia-Jun
    Jiang, Chuan
    Liu, Hai-Long
    Chen, Qun
    Li, Zhan-Huai
    Jisuanji Xuebao/Chinese Journal of Computers, 2015, 38 (09): : 1755 - 1766
  • [48] Relating Web characteristics with link based Web page ranking
    Baeza-Yates, R
    Castillo, C
    EIGHTH SYMPOSIUM ON STRING PROCESSING AND INFORMATION RETRIEVAL, PROCEEDINGS, 2001, : 21 - 32
  • [49] TRank: Ranking Entity Types Using the Web of Data
    Tonon, Alberto
    Catasta, Michele
    Demartini, Gianluca
    Mauroux, Philippe Cudre
    Aberer, Karl
    SEMANTIC WEB - ISWC 2013, PART I, 2013, 8218 : 640 - 656
  • [50] Deep Web navigation in Web data extraction
    Baumgartner, Robert
    Ceresna, Michal
    Ledermueller, Gerald
    INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE FOR MODELLING, CONTROL & AUTOMATION JOINTLY WITH INTERNATIONAL CONFERENCE ON INTELLIGENT AGENTS, WEB TECHNOLOGIES & INTERNET COMMERCE, VOL 2, PROCEEDINGS, 2006, : 698 - +