scholarly journals Spatiotemporal RDF Data Query Based on Subgraph Matching

2021 ◽  
Vol 10 (12) ◽  
pp. 832
Author(s):  
Xiangfu Meng ◽  
Lin Zhu ◽  
Qing Li ◽  
Xiaoyan Zhang
Keyword(s):  
Web Data ◽  
Subgraph Matching ◽  
Data Query ◽  
Query Process ◽  
Query Result ◽  
Temporal Features ◽  
Speed Up ◽  
Rdf Data ◽  
Description Framework ◽  
Query Algorithm

Resource Description Framework (RDF), as a standard metadata description framework proposed by the World Wide Web Consortium (W3C), is suitable for modeling and querying Web data. With the growing importance of RDF data in Web data management, there is an increasing need for modeling and querying RDF data. Previous approaches mainly focus on querying RDF. However, a large amount of RDF data have spatial and temporal features. Therefore, it is important to study spatiotemporal RDF data query approaches. In this paper, firstly, we formally define spatiotemporal RDF data, and construct a spatiotemporal RDF model st-RDF that is used to represent and manipulate spatiotemporal RDF data. Secondly, we present a spatiotemporal RDF query algorithm stQuery based on subgraph matching. This algorithm can quickly determine whether the query result is empty for queries whose temporal or spatial range exceeds a specific range by adopting a preliminary query filtering mechanism in the query process. Thirdly, we propose a sorting strategy that calculates the matching order of query nodes to speed up the subgraph matching. Finally, we conduct experiments in terms of effect and query efficiency. The experimental results show the performance advantages of our approach.

Efficient Indexing RDF Query Algorithm for Big Data

2013 ◽  
Vol 441 ◽  
pp. 691-694
Author(s):  
Yi Qun Zeng ◽  
Jing Bin Wang
Keyword(s):  
Large Scale ◽  
Rapid Development ◽  
Large Data ◽  
Index Structure ◽  
Data Query ◽  
Large Scale Data ◽  
Tree Index ◽  
Rdf Data ◽  
Query Algorithm ◽  
Scale Data

With the rapid development of information technology, data grows explosionly, how to deal with the large scale data become more and more important. Based on the characteristics of RDF data, we propose to compress RDF data. We construct an index structure called PAR-Tree Index, then base on the MapReduce parallel computing framework and the PAR-Tree Index to execute the query. Experimental results show that the algorithm can improve the efficiency of large data query.

scholarly journals gst-store: Querying Large Spatiotemporal RDF Graphs

2017 ◽  
Vol 1 (2) ◽  
pp. 84-103 ◽  
Author(s):  
Dong Wang ◽  
Lei Zou ◽  
Dongyan Zhao
Keyword(s):  
Query Language ◽  
Data Management System ◽  
Rdf Graph ◽  
Spatiotemporal Information ◽  
Simple Protocol ◽  
Rdf Data ◽  
Description Framework ◽  
Query Algorithm ◽  
Rdf Graphs ◽  
Resource Description

Abstract The Simple Protocol and RDF Query Language (SPARQL) query language allows users to issue a structural query over a resource description framework (RDF) graph. However, the lack of a spatiotemporal query language limits the usage of RDF data in spatiotemporal-oriented applications. As the spatiotemporal information continuously increases in RDF data, it is necessary to design an effective and efficient spatiotemporal RDF data management system. In this paper, we formally define the spatiotemporal information-integrated RDF data, introduce a spatiotemporal query language that extends the SPARQL language with spatiotemporal assertions to query spatiotemporal information-integrated RDF data, and design a novel index and the corresponding query algorithm. The experimental results on a large, real RDF graph integrating spatial and temporal information (> 180 million triples) confirm the superiority of our approach. In contrast to its competitors, gst-store outperforms by more than 20%-30% in most cases.

Characterising RDF data sets

2017 ◽  
Vol 44 (2) ◽  
pp. 203-229 ◽  
Author(s):  
Javier D Fernández ◽  
Miguel A Martínez-Prieto ◽  
Pablo de la Fuente Redondo ◽  
Claudio Gutiérrez
Keyword(s):  
Data Structures ◽  
Large Scale ◽  
Open Data ◽  
Structural Features ◽  
Data Sets ◽  
Data Set ◽  
Wide Range ◽  
Rdf Data ◽  
Description Framework ◽  
Resource Description

The publication of semantic web data, commonly represented in Resource Description Framework (RDF), has experienced outstanding growth over the last few years. Data from all fields of knowledge are shared publicly and interconnected in active initiatives such as Linked Open Data. However, despite the increasing availability of applications managing large-scale RDF information such as RDF stores and reasoning tools, little attention has been given to the structural features emerging in real-world RDF data. Our work addresses this issue by proposing specific metrics to characterise RDF data. We specifically focus on revealing the redundancy of each data set, as well as common structural patterns. We evaluate the proposed metrics on several data sets, which cover a wide range of designs and models. Our findings provide a basis for more efficient RDF data structures, indexes and compressors.

Towards Massive RDF Storage in NoSQL Databases

2019 ◽  
pp. 263-284 ◽  
Author(s):  
Zongmin Ma ◽  
Li Yan
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Future Research ◽  
Nosql Databases ◽  
Current State ◽  
Data Store ◽  
Rdf Data ◽  
Description Framework ◽  
Resource Description ◽  
The Web

The resource description framework (RDF) is a model for representing information resources on the web. With the widespread acceptance of RDF as the de-facto standard recommended by W3C (World Wide Web Consortium) for the representation and exchange of information on the web, a huge amount of RDF data is being proliferated and becoming available. So, RDF data management is of increasing importance and has attracted attention in the database community as well as the Semantic Web community. Currently, much work has been devoted to propose different solutions to store large-scale RDF data efficiently. In order to manage massive RDF data, NoSQL (not only SQL) databases have been used for scalable RDF data store. This chapter focuses on using various NoSQL databases to store massive RDF data. An up-to-date overview of the current state of the art in RDF data storage in NoSQL databases is provided. The chapter aims at suggestions for future research.

A Review of RDF Storage in NoSQL Databases

2016 ◽  
pp. 210-229 ◽  
Author(s):  
Zongmin Ma ◽  
Li Yan
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Future Research ◽  
Nosql Databases ◽  
Current State ◽  
Data Store ◽  
Rdf Data ◽  
Description Framework ◽  
Resource Description ◽  
The Web

The Resource Description Framework (RDF) is a model for representing information resources on the Web. With the widespread acceptance of RDF as the de-facto standard recommended by W3C (World Wide Web Consortium) for the representation and exchange of information on the Web, a huge amount of RDF data is being proliferated and becoming available. So RDF data management is of increasing importance, and has attracted attentions in the database community as well as the Semantic Web community. Currently much work has been devoted to propose different solutions to store large-scale RDF data efficiently. In order to manage massive RDF data, NoSQL (“not only SQL”) databases have been used for scalable RDF data store. This chapter focuses on using various NoSQL databases to store massive RDF data. An up-to-date overview of the current state of the art in RDF data storage in NoSQL databases is provided. The chapter aims at suggestions for future research.

Efficient Fault-Tolerant Event Query Algorithm in Distributed Wireless Sensor Networks

2010 ◽  
Vol 6 (1) ◽  
pp. 593849 ◽  
Author(s):  
Rongbo Zhu
Keyword(s):  
Fault Tolerant ◽  
Wireless Sensor ◽  
Correction Algorithm ◽  
Short Term ◽  
Self Evaluation ◽  
Data Query ◽  
Simulation Results ◽  
Query Algorithm ◽  
Cooperative Evaluation

To overcome the faulty data query problem to improve the accuracy of data query, an efficient fault-tolerant event query algorithm (FTEQ) is proposed, which takes the short-term and long-term spatial and temporal similarities between sensors and environment into considerations. An imprecise and missing data correction algorithm based on Kalman filter is proposed to correct fault sensing data, and a score rank algorithm also is proposed to assign each sensor an appropriate value to reflect the importance of sensors. FTEQ performs self-evaluation and cooperative evaluation schemes with its trustful r neighbor nodes to filter fault data query with the importance of sensor. Simulation results prove that FTEQ performs extremely well in terms of faulty detection rate and data query cost.

A Semantic Query Method for Deep Web

2013 ◽  
Vol 347-350 ◽  
pp. 2559-2563
Author(s):  
Hao Jiang ◽  
Wen Ju Liu ◽  
Li Li Lu
Keyword(s):  
Semantic Web ◽  
Software Architecture ◽  
Deep Web ◽  
New Method ◽  
Web Data ◽  
Semantic Query ◽  
Web Environment ◽  
Query Result ◽  
Complete Set ◽  
Sql Query

Based on the idea of "functionality-centric", this paper proposes a complete set of oriented semantic query methods for Deep Web, builds up the relevant software architecture, provides a new method for full use of Deep Web data resources in semantic web environment through describing the establishment of the semantic environment, re-writing the SPARQL-to-SQL query, semantic packaging of semantic query result, and the architecture of semantic query services.

A Negative Selection Algorithm Base on the Self R-Tree

2013 ◽  
Vol 411-414 ◽  
pp. 2007-2012
Author(s):  
Kun Peng Wang
Keyword(s):  
Time Complexity ◽  
Negative Selection ◽  
Selection Process ◽  
The Self ◽  
Selection Algorithm ◽  
Negative Selection Algorithm ◽  
Data Query ◽  
Query Process ◽  
Algorithm Base ◽  
Selection Algorithms

In this article, we present a new negative selection algorithm which the self-data is organized as a R-Tree structure. And the negative selection process could be transformed into the data query process in the self-R-Tree, if a new detector is indexed in any leaf node it will be dropped. As the time complexity of data query process in the tree is in the log level, the negative selection process of our algorithm is superior to the linearly comparation procedure in the traditional negative selection algorithms.

Homomorphic Encryption Based Data Storage and Query Algorithm

2013 ◽  
Vol 846-847 ◽  
pp. 1582-1589
Author(s):  
Chun Yun Di ◽  
Chen Lei Cao ◽  
Ru Zhang ◽  
Jian Yi Liu
Keyword(s):  
Data Storage ◽  
Homomorphic Encryption ◽  
Query Time ◽  
Query Result ◽  
Query Algorithm

To solve the problems of ciphertext storage and query on cloud, this paper proposes a ciphertext query algorithm according to the multiplication homomorphic encryption characteristic of RSA. Moreover, this paper sorts and displays the query result of the ciphertext efficiently by the ciphertext similarity, which is the combination of inclined angle's cosine of vector, vector's mapping length and words' weight. Finally, the ciphertext query time is investigated in a lab environment. The ciphertext query time is considered as acceptable to users.

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