scholarly journals Quasi-SLCA Based Keyword Query Processing over Probabilistic XML Data

2014 ◽  
Vol 26 (4) ◽  
pp. 957-969 ◽  
Author(s):  
Jianxin Li ◽  
Chengfei Liu ◽  
Rui Zhou ◽  
Jeffrey Xu Yu
Keyword(s):  
Query Processing ◽  
Keyword Query ◽  
Xml Data ◽  
Probabilistic Xml

scholarly journals Share Market Management System Based Keyword Query Processing on XML Data

2016 ◽  
Vol 5 (2) ◽  
pp. 245-251
Author(s):  
Darsana C.S ◽  
Roshni P ◽  
Chandini K ◽  
Surekha Mariam Varghese
Keyword(s):  
Query Processing ◽  
Management System ◽  
Keyword Query ◽  
Xml Data ◽  
Market Management

Top-down keyword query processing on XML data

2013 ◽  
Author(s):  
Junfeng Zhou ◽  
Xingmin Zhao ◽  
Wei Wang ◽  
Ziyang Chen ◽  
Jeffrey Xu Yu
Keyword(s):  
Query Processing ◽  
Top Down ◽  
Keyword Query ◽  
Xml Data

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.

Top-Down XML Keyword Query Processing

2016 ◽  
Vol 28 (5) ◽  
pp. 1340-1353 ◽  
Author(s):  
Junfeng Zhou ◽  
Wei Wang ◽  
Ziyang Chen ◽  
Jeffrey Xu Yu ◽  
Xian Tang ◽  
...  
Keyword(s):  
Query Processing ◽  
Top Down ◽  
Keyword Query ◽  
Xml Keyword Query

Efficient Storage and Parallel Query of Massive XML Data in Hadoop

2019 ◽  
pp. 242-262
Author(s):  
Wei Yan
Keyword(s):  
Query Processing ◽  
Query Optimization ◽  
Data Transfer ◽  
Programming Model ◽  
Xml Data ◽  
Efficient Storage ◽  
Parallel Query ◽  
Parallel Query Processing

In order to solve the problem of storage and query for massive XML data, a method of efficient storage and parallel query for a massive volume of XML data with Hadoop is proposed. This method can store massive XML data in Hadoop and the massive XML data is divided into many XML data blocks and loaded on HDFS. The parallel query method of massive XML data is proposed, which uses parallel XPath queries based on multiple predicate selection, and the results of parallel query can satisfy the requirement of query given by the user. In this chapter, the map logic algorithm and the reduce logic algorithm based on parallel XPath queries based using MapReduce programming model are proposed, and the parallel query processing of massive XML data is realized. In addition, the method of MapReduce query optimization based on multiple predicate selection is proposed to reduce the data transfer volume of the system and improve the performance of the system. Finally, the effectiveness of the proposed method is verified by experiment.

XML Data Integration

2010 ◽  
pp. 333-360 ◽  
Author(s):  
Yan Qi ◽  
Huiping Cao ◽  
K. Selçuk Candan ◽  
Maria Luisa Sapino
Keyword(s):  
Data Integration ◽  
Query Processing ◽  
Multiple Sources ◽  
Data Types ◽  
Xml Data ◽  
Data Schema ◽  
Integration Data ◽  
Feedback Techniques ◽  
Different Sources ◽  
Xml Data Integration

In XML Data Integration, data/metadata merging and query processing are indispensable. Specifically, merging integrates multiple disparate (heterogeneous and autonomous) input data sources together for further usage, while query processing is one main reason why the data need to be integrated in the first place. Besides, when supported with appropriate user feedback techniques, queries can also provide contexts in which conflicts among the input sources can be interpreted and resolved. The flexibility of XML structure provides opportunities for alleviating some of the difficulties that other less flexible data types face in the presence of uncertainty; yet, this flexibility also introduces new challenges in merging multiple sources and query processing over integrated data. In this chapter, the authors discuss two alternative ways XML data/schema can be integrated: conflict-eliminating (where the result is cleaned from any conflicts that the different sources might have with each other) and conflict-preserving (where the resulting XML data or XML schema captures the alternative interpretations of the data). They also present techniques for query processing over integrated, possibly imprecise, XML data, and cover strategies that can be used for resolving underlying conflicts.

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