Approximate graph schema extraction for semi-structured data
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摘要Semi-structured data are typically represented in the form of labeled directed graphs. They are self-describing and schemaless. The lack of a schema renders query processing over semi-structured data expensive. To overcome this predicament, some researchers proposed to use the structure of the data for schema representation. Such schemas are commonly referred to as graph schemas. Nevertheless, since semistructured data are irregular and frequently subjected to modifications, it is costly to construct an accurate graph schema and worse still, it is difficult to maintain it thereafter. Furthermore, an accurate graph schema is generally very large, hence impractical. In this paper, an approximation approach is proposed for graph schema extraction. Approximation is achieved by summarizing the semi-structured data graph using an incremental clustering method. The preliminary experimental results have shown that approximate graph schemas were more compact than the conventional accurate graph schemas and promising in query evaluation that involved regular path expressions.
著者Wang QY, Yu JX, Wong KF
會議名稱7th International Conference on Extending Database Technology
會議開始日27.03.2000
會議完結日31.03.2000
會議地點CONSTANCE
期刊名稱Lecture Notes in Artificial Intelligence
出版年份2000
月份1
日期1
卷號1777
出版社SPRINGER-VERLAG BERLIN
頁次302 - 316
國際標準書號3-540-67227-3
國際標準期刊號0302-9743
語言英式英語
Web of Science 學科類別Computer Science; Computer Science, Information Systems; Computer Science, Theory & Methods

上次更新時間 2021-26-02 於 02:00