scholarly journals Efficient Query Processing for Large XML Data in Distributed Environments

2007 ◽  
Author(s):  
Hiroto Kurita ◽  
Kenji Hatano ◽  
Jun Miyazaki ◽  
Shunsuke Uemura
Keyword(s):  
Query Processing ◽  
Distributed Environments ◽  
Xml Data ◽  
Efficient Query Processing

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.

Impact of Applying Aggregate Query Processing in Mobile Commerce

2012 ◽  
Vol 8 (2) ◽  
pp. 1-17
Author(s):  
Say Ying Lim ◽  
Siew Fan Wong
Keyword(s):  
Query Processing ◽  
Mobile Devices ◽  
Communication Cost ◽  
Single Server ◽  
Distributed Environments ◽  
Mobile Environments ◽  
Memory Consumption ◽  
Screen Size ◽  
Aggregate Query ◽  
Small Screen

With the increased usage of mobile devices, society is seeing more and more users doing transactions wirelessly. Often, data from a single server may not be sufficient. Rather, data may need to be manipulated and to be gathered from multiple remote servers before useful information can be formed. Mobile transactions are constrained by small screen size of mobile devices, high communication cost, and high memory consumption. Existing techniques from traditional query processing in distributed environments cannot be directly applied to mobile environments. In this paper, the authors propose techniques for processing mobile queries that address the issue of high memory consumption. A set of walkthrough examples was provided and performances of various techniques were examined. The results show that the technique of first downloading primary keys only from one server and then sending a query to the second server using these primary keys before processing for qualified match in the second server gives the best performance.

Efficient Query Processing with Structural Join Indexing in an Object Relational Data Warehousing Environment

2011 ◽  
pp. 243-256
Author(s):  
Vivekanand Gopalkrishnan ◽  
Qing Li ◽  
Kamalakar Karlapalem
Keyword(s):  
Query Processing ◽  
Data Warehousing ◽  
Cost Model ◽  
Hill Climbing ◽  
Relational Data ◽  
Structural Join ◽  
Object Relational ◽  
Hybrid Data ◽  
Efficient Query Processing ◽  
Query Semantics

In an Object Relational Data Warehousing (ORDW) environment, the semantics of data and queries can be explicitly captured, represented, and utilized based on is-a and class composition hierarchies, thereby resulting in more efficient OLAP query processing. In this chapter, we show the efficacy in building semantic-rich hybrid data indexes incorporating Structural Join Index Hierarchy (SJIH) on the ORDW views. Given a set of queries, we use a hill-climbing heuristic algorithm to select (near) optimal SJIHs, thereby embedding query semantics into the indexing framework. Finally, by a cost model, we analyze the effectiveness of our approach vis-a-vis the pointer chasing approach.

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