Efficient mining of temporal traversal patterns from very large Web logs

被引:0
|
作者
Chen, ZX [1 ]
机构
[1] Univ Texas Pan Amer, Dept Comp Sci, Edinburg, TX 78539 USA
关键词
web mining; access session; temporal content page; temporal traversal pattern; suffix tree;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A Web page in a Web access session is considered as a temporal content page, if its access time is greater than the average access time of all the pages in the session. A maximal temporal reference of a Web user in an access session is a longest consecutive sequence of Web pages in the session which ends at a temporal content page and has no other temporal content pages in the sequence. The problem of efficient mining of frequent temporal traversal patterns, i.e., large temporal reference sequences of maximal temporal references, from very large Web logs is important in Web mining. This paper aims for algorithmic solutions to the problem with best possible efficiency. We first design linear time algorithms for finding maximal temporal references from Web logs. We then devise a linear time algorithm for mining frequent temporal traversal patterns, utilizing the technique developed in [8, 9] for fast construction of "shallow" generalized suffix trees over a very large alphabet.
引用
收藏
页码:10 / 16
页数:7
相关论文
共 50 条
  • [41] Extracting Knowledge from Web Server Logs Using Web Usage Mining
    Eltahir, Mirghani A.
    Dafa-Alla, Anour F. A.
    [J]. 2013 INTERNATIONAL CONFERENCE ON COMPUTING, ELECTRICAL AND ELECTRONICS ENGINEERING (ICCEEE), 2013, : 413 - 417
  • [42] Web Usage Mining: users' navigational patterns extraction from web logs using Ant-based Clustering Method
    Etminani, Kobra
    Akbarzadeh-T, Mohammad-R.
    Yanehsari, Noorali Raeeji
    [J]. PROCEEDINGS OF THE JOINT 2009 INTERNATIONAL FUZZY SYSTEMS ASSOCIATION WORLD CONGRESS AND 2009 EUROPEAN SOCIETY OF FUZZY LOGIC AND TECHNOLOGY CONFERENCE, 2009, : 396 - 401
  • [43] An Integrated Framework for Mining Temporal Logs from Fluctuating Events
    Zeng, Chunqiu
    Tang, Liang
    Zhou, Wubai
    Li, Tao
    Shwartz, Larisa
    Grabarnik, Genady Ya.
    [J]. IEEE TRANSACTIONS ON SERVICES COMPUTING, 2019, 12 (02) : 199 - 213
  • [44] FSMTree: An efficient algorithm for mining frequent temporal patterns
    Kempe, Steffen
    Hipp, Jochen
    Kruse, Rudolf
    [J]. DATA ANALYSIS, MACHINE LEARNING AND APPLICATIONS, 2008, : 253 - +
  • [45] A data clustering algorithm for mining patterns from event logs
    Vaarandi, R
    [J]. PROCEEDINGS OF THE 3RD IEEE WORKSHOP ON IP OPERATIONS & MANAGEMENT (IPOM2003), 2003, : 119 - 126
  • [46] Mining maximum frequent access patterns in web logs based on unique labeled tree
    Zhang, Ling
    Yin, Ran-ping
    Zhan, Yu-bin
    [J]. WEB INFORMATION SYSTEMS - WISE 2006 WORKSHOPS, PROCEEDINGS, 2006, 4256 : 73 - 82
  • [47] Internet Usage Patterns Mining from Firewall Event Logs
    Polpinij, Jantima
    Namee, Khanista
    [J]. 2019 INTERNATIONAL CONFERENCE ON BIG DATA AND EDUCATION (ICBDE 2019), 2019, : 93 - 97
  • [48] Detecting Web Crawlers from Web Server Access Logs with Data Mining Classifiers
    Stevanovic, Dusan
    An, Aijun
    Vlajic, Natalija
    [J]. FOUNDATIONS OF INTELLIGENT SYSTEMS, 2011, 6804 : 483 - 489
  • [49] An approach for mining service composition patterns from execution logs
    Upadhyaya, Bipin
    Tang, Ran
    Zou, Ying
    [J]. JOURNAL OF SOFTWARE-EVOLUTION AND PROCESS, 2013, 25 (08) : 841 - 870
  • [50] Extracting Log Patterns from System Logs in LARGE
    Zhao, Yining
    Xiao, Haili
    [J]. 2016 IEEE 30TH INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM WORKSHOPS (IPDPSW), 2016, : 1645 - 1652