General Information
    • ISSN: 1793-8201 (Print), 2972-4511 (Online)
    • Abbreviated Title: Int. J. Comput. Theory Eng.
    • Frequency: Quarterly
    • DOI: 10.7763/IJCTE
    • Editor-in-Chief: Prof. Mehmet Sahinoglu
    • Associate Editor-in-Chief: Assoc. Prof. Alberto Arteta, Assoc. Prof. Engin Maşazade
    • Managing Editor: Ms. Mia Hu
    • Abstracting/Indexing: Scopus (Since 2022), INSPEC (IET), CNKI,  Google Scholar, EBSCO, etc.
    • Average Days from Submission to Acceptance: 192 days
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Prof. Mehmet Sahinoglu
Computer Science Department, Troy University, USA
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.

IJCTE 2011 Vol.3(2): 261-269 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2011.V3.314

Pattern discovery for semi-structured web pagesusing bar-tree representation

Z. Akbar, L. T. Handoko

Abstract—Many websites with an underlying database containing structured data provide the richest and most dense source of information relevant for topical data integration. The real data integration requires sustainable and reliable pattern discovery to enable accurate content retrieval and to recognize pattern changes from time to time; yet, extracting the structured data from web documents is still lacking from its accuracy. This paper proposes the bar-tree representation to describe the whole pattern of web pages in an efficient way based on the reverse algorithm. While previous algorithms always trace the pattern and extract the region of interest from top root, the reverse algorithm recognizes the pattern from the region of interest to both top and bottom roots simultaneously. The attributes are then extracted and labeled reversely from the region of interest of targeted contents. Since using conventional representations for the algorithm should require more computational power, the bar-tree method is developed to represent the generated patterns using bar graphs characterized by the depths and widths from the document roots. We show that this representation is suitable for extracting the data from the semistructured web sources, and for detecting the template changes of targeted pages. The experimental results show perfect recognition rate for template changes in several web targets.

Index Terms—data extraction, data mining, web-based information system

Z. Akbar is also with the Group for Bioinformatics and Information Mining, Department of Computer and Information Science, University of Konstanz, Box D188, D-78457 Konstanz, Germany.


Cite: Z. Akbar, L. T. Handoko, "Pattern discovery for semi-structured web pagesusing bar-tree representation," International Journal of Computer Theory and Engineering vol. 3, no. 2, pp. 261-269, 2011.

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