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

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

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.

Research on Sequence Query Processing Techniques over Data Streams

2013 ◽  
Vol 284-287 ◽  
pp. 3507-3511 ◽  
Author(s):  
Edgar Chia Han Lin
Keyword(s):  
Query Processing ◽  
Data Streams ◽  
Management System ◽  
Data Stream ◽  
Sensor Data ◽  
Data Management System ◽  
Content Filtering ◽  
Data Stream Management ◽  
Stream Management ◽  
Great Progress

Due to the great progress of computer technology and mature development of network, more and more data are generated and distributed through the network, which is called data streams. During the last couple of years, a number of researchers have paid their attention to data stream management, which is different from the conventional database management. At present, the new type of data management system, called data stream management system (DSMS), has become one of the most popular research areas in data engineering field. Lots of research projects have made great progress in this area. Since the current DSMS does not support queries on sequence data, this project will study the issues related to two types of data. First, we will focus on the content filtering on single-attribute streams, such as sensor data. Second, we will focus on multi-attribute streams, such as video films. We will discuss the related issues such as how to build an efficient index for all queries of different streams and the corresponding query processing mechanisms.

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