Embedding XML Schema Constraints in Search-Based Intersection Tests for XPath Query Optimization

2006 ◽  
Author(s):  
S. Bottcher ◽  
R. Steinmetz
Keyword(s):  
Query Optimization ◽  
Xml Schema ◽  
Xpath Query

A Structural XPath Query Expansion Approach Based on XML Schema in Publish Subscribe System

2014 ◽  
Vol 681 ◽  
pp. 239-243
Author(s):  
Jun Bo Pei ◽  
Jin Chun Gao ◽  
Yuan An Liu ◽  
Xiao Lei Ma
Keyword(s):  
Structural Property ◽  
Query Expansion ◽  
Structural Information ◽  
Xml Schema ◽  
Related Data ◽  
Query Node ◽  
Xpath Query

XML based publish/subscribe systems are on tremendous rise during recent years. In order to get interested documents, the subscribers submit an XPath Query. In such applications, there is often a mismatch between how publishers describe entities and how different subscribers express their interests. Lots of researches focus on filtering mechanisms, exists such as XFilter, YFilter, Afilter etc, but most of these mechanisms do not use the structural property of XML. This paper proposes an approach which dose XPath query expansion based on structural information. Firstly check query node in OWL classes and return semantically related data performing semantic normalized. Secondly, perform structural expansion depend on the XSD which set constraint on content published. Experiment results show that our approach performs well across a range of XPath queries and documents.

Multi-dimensional query optimization algorithm for bitmap index with binning

2010 ◽  
Vol 30 (8) ◽  
pp. 2013-2016 ◽  
Author(s):  
Li-ming WANG ◽  
Xiao CHENG ◽  
Yu-mei CHAI
Keyword(s):  
Query Optimization ◽  
Optimization Algorithm ◽  
Bitmap Index

A Query Optimization Method Based on User Profiles Reasoning

2011 ◽  
Vol 30 (1) ◽  
pp. 33-37
Author(s):  
Xiang Mei ◽  
Xiang-wu Meng ◽  
Jun-Liang Chen ◽  
Meng Xu
Keyword(s):  
Query Optimization ◽  
Optimization Method ◽  
User Profiles

An Optimal Framework for Spatial Query Optimization Using Hadoop in Big Data Analytics

2019 ◽  
Vol 12 ◽  
Author(s):  
Pankaj Dadheech ◽  
Dinesh Goyal ◽  
Sumit Srivastava ◽  
Ankit Kumar
Keyword(s):  
Big Data ◽  
Query Optimization ◽  
Spatial Data ◽  
Spatial Information ◽  
Big Data Analytics ◽  
Spatial Query ◽  
Data Process ◽  
Boolean Queries ◽  
Spatial Query Optimization ◽  
Hadoop System

Spatial queries frequently used in Hadoop for significant data process. However, vast and massive size of spatial information makes it difficult to process the spatial inquiries proficiently, so they utilized the Hadoop system for process Big Data. We have used Boolean Queries & Geometry Boolean Spatial Data for Query Optimization using Hadoop System. In this paper, we show a lightweight and adaptable spatial data index for big data which will process in Hadoop frameworks. Results demonstrate the proficiency and adequacy of our spatial ordering system for various spatial inquiries.

scholarly journals Query Rewriting for Incremental Continuous Query Evaluation in HIFUN

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 149
Author(s):  
Petros Zervoudakis ◽  
Haridimos Kondylakis ◽  
Nicolas Spyratos ◽  
Dimitris Plexousakis
Keyword(s):  
Query Optimization ◽  
Query Language ◽  
Computational Cost ◽  
Continuous Queries ◽  
Continuous Query ◽  
Query Rewriting ◽  
Query Evaluation ◽  
Clear Separation ◽  
Complete Dataset ◽  
High Level

HIFUN is a high-level query language for expressing analytic queries of big datasets, offering a clear separation between the conceptual layer, where analytic queries are defined independently of the nature and location of data, and the physical layer, where queries are evaluated. In this paper, we present a methodology based on the HIFUN language, and the corresponding algorithms for the incremental evaluation of continuous queries. In essence, our approach is able to process the most recent data batch by exploiting already computed information, without requiring the evaluation of the query over the complete dataset. We present the generic algorithm which we translated to both SQL and MapReduce using SPARK; it implements various query rewriting methods. We demonstrate the effectiveness of our approach in temrs of query answering efficiency. Finally, we show that by exploiting the formal query rewriting methods of HIFUN, we can further reduce the computational cost, adding another layer of query optimization to our implementation.

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