SLAX: An Improved Leaf-Clustering Based Approximate XML Join Algorithm for Integrating XML Data at Subtree Classes

• Item type: Trans(1)
• 公開日: 2006-06-15
• タイトル: SLAX: An Improved Leaf-Clustering Based Approximate XML Join Algorithm for Integrating XML Data at Subtree Classes
• タイトル / 言語: en
• タイトル: SLAX: An Improved Leaf-Clustering Based Approximate XML Join Algorithm for Integrating XML Data at Subtree Classes
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: 研究論文
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Department of Computer Science Graduate School of Information Science and Engineering Tokyo Institute of Technology
• 著者所属: Global Scientific Information and Computing Center Tokyo Institute of Technology
• 著者所属(英): en
• 著者所属(英): Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology
• 著者所属(英): en
• 著者所属(英): Global Scientific Information and Computing Center, Tokyo Institute of Technology
• 著者名: Wenxin, Liang; Haruo, Yokota
• 著者名(英): Wenxin, Liang; Haruo, Yokota
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: XML is widely applied to represent and exchange data on the Internet. However XML documents from different sources may convey nearly or exactly the same information but may be different on structures. In previous work we have proposed LAX ( Leaf-clustering based Approximate XML join algorithm) in which the two XML document trees are divided into independent subtrees and the approximate similarity between them are determined by the tree similarity degree based on the mean value of the similarity degrees of matched subtrees. Our previous experimental results show that LAX comparing with the tree edit distance is more efficient in performance and more effective for measuring the approximate similarity between XML documents. However because the tree edit distance is extremely time-consuming we only used bibliography data of very small sizes to compare the performance of LAX with that of the tree edit distance in our previous experiments. Besides in LAX the output is oriented to the pair of documents that have larger tree similarity degree than the threshold. Therefore when LAX is applied to the fragments divided from large XML documents the hit subtree selected from the output pair of fragment documents that has large tree similarity degree might not be the proper one that should be integrated. In this paper we propose SLAX ( Subtree-class Leaf-clustering based Approximate XML join algorithm ) for integrating the fragments divided from large XML documents by using the maximum match value at subtree classes. And we conduct further experiments to evaluate SLAX comparing with LAX by using both real large bibliography and bioinformatics data. The experimental results show that SLAX is more effective than LAX for integrating both large bibliography and bioinformatics data at subtree classes.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: XML is widely applied to represent and exchange data on the Internet. However, XML documents from different sources may convey nearly or exactly the same information but may be different on structures. In previous work, we have proposed LAX ( Leaf-clustering based Approximate XML join algorithm), in which the two XML document trees are divided into independent subtrees and the approximate similarity between them are determined by the tree similarity degree based on the mean value of the similarity degrees of matched subtrees. Our previous experimental results show that LAX, comparing with the tree edit distance, is more efficient in performance and more effective for measuring the approximate similarity between XML documents. However, because the tree edit distance is extremely time-consuming, we only used bibliography data of very small sizes to compare the performance of LAX with that of the tree edit distance in our previous experiments. Besides, in LAX, the output is oriented to the pair of documents that have larger tree similarity degree than the threshold. Therefore, when LAX is applied to the fragments divided from large XML documents, the hit subtree selected from the output pair of fragment documents that has large tree similarity degree might not be the proper one that should be integrated. In this paper, we propose SLAX ( Subtree-class Leaf-clustering based Approximate XML join algorithm ) for integrating the fragments divided from large XML documents by using the maximum match value at subtree classes. And we conduct further experiments to evaluate SLAX, comparing with LAX, by using both real large bibliography and bioinformatics data. The experimental results show that SLAX is more effective than LAX for integrating both large bibliography and bioinformatics data at subtree classes.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA11464847
• 書誌情報: 情報処理学会論文誌データベース（TOD）; 巻 47, 号 SIG8(TOD30), p. 47-57, 発行日 2006-06-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7799
• 出版者 / 言語: ja
• 出版者: 情報処理学会