Pervasive information gathering and data mining for efficient business administration

被引:3
作者
Garrigos-Simon, Fernando J. [1 ]
Llorente, Roberto [1 ]
Morant, Maria [1 ]
Narangajavana, Yeamduan [2 ]
机构
[1] Univ Politecn Valencia, Camino Vera S-N,7D Bldg, E-46022 Valencia, Spain
[2] Jaume I Univ, Castellon de La Plana, Spain
关键词
Consumer behavior; data mining; habit identification; marketing; new technologies; sensor networks; TECHNOLOGIES; ADOPTION; RECOGNITION; HOSPITALITY; MANAGEMENT; PROGRESS;
D O I
10.1177/1356766715617219
中图分类号
F [经济];
学科分类号
02 ;
摘要
Following a multidisciplinary perspective (that combines the literature from management, information systems, marketing and engineering telecommunications perspectives), the purpose of this article is to create and analyze a conceptual framework and to propose a new methodology that encompasses different techniques for pervasive information gathering in hotels and for identifying clients' habits. Focusing on the future of hotels, this work presents new technologies for hotels suitable for correlating the customers' on-site activities with online activities including passive location tracking using Wi-Fi devices' connectivity, customer satisfaction evaluated via facial or voice recognition using inbuilt cameras/microphones altogether with data mining analysis. Moreover, this article explains how multidisciplinary consumer behavior can be analyzed by data mining to include this information in the vacation marketing approach for efficient business administration.
引用
收藏
页码:295 / 306
页数:12
相关论文
共 50 条
  • [31] Use of data mining at the Food and Drug Administration
    Duggirala, Hesha J.
    Tonning, Joseph M.
    Smith, Ella
    Bright, Roselie A.
    Baker, John D.
    Ball, Robert
    Bell, Carlos
    Bright-Ponte, Susan J.
    Botsis, Taxiarchis
    Bouri, Khaled
    Boyer, Marc
    Burkhart, Keith
    Condrey, G. Steven
    Chen, James J.
    Chirtel, Stuart
    Filice, Ross W.
    Francis, Henry
    Jiang, Hongying
    Levine, Jonathan
    Martin, David
    Oladipo, Taiye
    O'Neill, Rene
    Palmer, Lee Anne M.
    Paredes, Antonio
    Rochester, George
    Sholtes, Deborah
    Szarfman, Ana
    Wong, Hui-Lee
    Xu, Zhiheng
    Kass-Hout, Taha
    JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2016, 23 (02) : 428 - 434
  • [32] Healthcare Information System and Data Mining
    Kuo, Nai-Wen
    RECENT TRENDS IN MATERIALS AND MECHANICAL ENGINEERING MATERIALS, MECHATRONICS AND AUTOMATION, PTS 1-3, 2011, 55-57 : 561 - 566
  • [33] The contribution of data mining to information science
    Chen, SY
    Liu, XH
    JOURNAL OF INFORMATION SCIENCE, 2004, 30 (06) : 550 - 558
  • [34] Information Security in Big Data Mining
    Revathi, T.
    Sudharsana, V
    2017 IEEE INTERNATIONAL CONFERENCE ON POWER, CONTROL, SIGNALS AND INSTRUMENTATION ENGINEERING (ICPCSI), 2017, : 2045 - 2049
  • [35] Data Mining in Movement Visual Information
    Wu, Yin
    Xie, Danxia
    PROCEEDINGS OF THE 2010 CONFERENCE ON COMPUTER SCIENCE IN SPORTS, 2010, : 175 - 181
  • [36] An Efficient Reduction Method for Data Mining
    Hou, Lifen
    Wang, Yonghao
    Liu, Xinyu
    2013 INTERNATIONAL CONFERENCE ON COMPUTER SCIENCES AND APPLICATIONS (CSA), 2013, : 825 - 828
  • [37] Pervasive Decision Support to predict football corners and goals by means of data mining
    Gomes, Joao
    Portela, Filipe
    Santos, Manuel F.
    NEW ADVANCES IN INFORMATION SYSTEMS AND TECHNOLOGIES, VOL 2, 2016, 445 : 547 - 556
  • [38] Information Security in Big Data: Privacy and Data Mining
    Xu, Lei
    Jiang, Chunxiao
    Wang, Jian
    Yuan, Jian
    Ren, Yong
    IEEE ACCESS, 2014, 2 : 1149 - 1176
  • [39] Scholarly Big Data: Information Extraction and Data Mining
    Giles, C. Lee
    PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), 2013, : 1 - 1
  • [40] A data mining proxy approach for efficient frequent itemset mining
    Yu, Jeffrey Xu
    Li, Zhiheng
    Liu, Guimei
    VLDB JOURNAL, 2008, 17 (04) : 947 - 970