Efficiently Coding and Indexing XML Document

2005 ◽  
pp. 138-150 ◽  
Author(s):  
Zhongming Han ◽  
Congting Xi ◽  
Jiajin Le
Keyword(s):  
Xml Document

XML Document Transformation for Data Manipulation Operations

2021 ◽  
Author(s):  
Aigul Mukhitova ◽  
Aigerim Yerimbetova ◽  
Nenad Mladenovic
Keyword(s):  
Data Manipulation ◽  
Xml Document ◽  
Document Transformation

scholarly journals From Capturing to XML　Document Exchange Total Solution

1999 ◽  
Vol 9 (3) ◽  
pp. 45-48
Author(s):  
Fisher Lee
Keyword(s):  
Xml Document

scholarly journals Keyword Search over Probabilistic XML Documents Based on Node Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yue Zhao ◽  
Ye Yuan ◽  
Guoren Wang
Keyword(s):  
Keyword Search ◽  
Possible World ◽  
Xml Data ◽  
Fast Learning ◽  
Probabilistic Xml ◽  
Learning Speed ◽  
Xml Document ◽  
Probability Threshold ◽  
Node Classification ◽  
Learning Machine

This paper describes a keyword search measure on probabilistic XML data based on ELM (extreme learning machine). We use this method to carry out keyword search on probabilistic XML data. A probabilistic XML document differs from a traditional XML document to realize keyword search in the consideration of possible world semantics. A probabilistic XML document can be seen as a set of nodes consisting of ordinary nodes and distributional nodes. ELM has good performance in text classification applications. As the typical semistructured data; the label of XML data possesses the function of definition itself. Label and context of the node can be seen as the text data of this node. ELM offers significant advantages such as fast learning speed, ease of implementation, and effective node classification. Set intersection can compute SLCA quickly in the node sets which is classified by using ELM. In this paper, we adopt ELM to classify nodes and compute probability. We propose two algorithms that are based on ELM and probability threshold to improve the overall performance. The experimental results verify the benefits of our methods according to various evaluation metrics.

scholarly journals Discovering XML Conditional Dependencies for Data Quality Issues

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Mohammed Ragheb Hakawati ◽  
Yasmin Yacob ◽  
Amiza Amir ◽  
Jabiry M. Mohammed ◽  
Khalid Jamal Jadaa
Keyword(s):  
Data Quality ◽  
Primary Standard ◽  
Markup Language ◽  
Document Type ◽  
Data Dependencies ◽  
Master Data ◽  
Xml Document ◽  
Extensible Markup ◽  
Quality Issues ◽  
Mining Algorithms

Extensible Markup Language (XML) is emerging as the primary standard for representing and exchanging data, with more than 60% of the total; XML considered the most dominant document type over the web; nevertheless, their quality is not as expected. XML integrity constraint especially XFD plays an important role in keeping the XML dataset as consistent as possible, but their ability to solve data quality issues is still intangible. The main reason is that old-fashioned data dependencies were basically introduced to maintain the consistency of the schema rather than that of the data. The purpose of this study is to introduce a method for discovering pattern tableaus for XML conditional dependencies to be used for enhancing XML document consistency as a part of data quality improvement phases. The notations of the conditional dependencies as new rules are designed mainly for improving data instance and extended traditional XML dependencies by enforcing pattern tableaus of semantically related constants. Subsequent to this, a set of minimal approximate conditional dependencies (XCFD, XCIND) is discovered and learned from the XML tree using a set of mining algorithms. The discovered patterns can be used as a Master data in order to detect inconsistencies that don’t respect the majority of the dataset.

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