scholarly journals Temporal RDF(S) Data Storage and Query with HBase

2020 ◽  
Vol 27 (4) ◽  
pp. 17-30
Author(s):  
Li Yan ◽  
Zheqing Zhang ◽  
Dan Yang
Keyword(s):  
Information Management ◽  
Data Storage ◽  
Temporal Information ◽  
Storage Model ◽  
Web Resources ◽  
Metadata Model ◽  
Query Efficiency ◽  
Rdf Data ◽  
Description Framework ◽  
Resource Description

Resource Description Framework (RDF) is a metadata model recommended by World Wide Web Consortium (W3C) for describing the Web resources. With the arrival of the era of Big Data, very large amounts of RDF data are continuously being created and need to be stored for management. The traditional centralized RDF storage models cannot meet the need of largescale RDF data storage. Meanwhile, the importance of temporal information management and processing has been acknowledged by academia and industry. In this paper, we propose a storage model to store temporal RDF based on HBase. The proposed storage model applies the built-in time mechanism of HBase. Our experiments on LUBM dataset with temporal information added show that our storage model can store large temporal RDF data and obtain good query efficiency.

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.

A Review of RDF Storage in NoSQL Databases

Big Data ◽  
2016 ◽  
pp. 85-104
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.

Storing massive Resource Description Framework (RDF) data: a survey

2016 ◽  
Vol 31 (4) ◽  
pp. 391-413 ◽  
Author(s):  
Zongmin Ma ◽  
Miriam A. M. Capretz ◽  
Li Yan
Keyword(s):  
Data Storage ◽  
Resource Description Framework ◽  
Relational Databases ◽  
Query Language ◽  
Current State ◽  
Rdf Data ◽  
Description Framework ◽  
Resource Description ◽  
Flexible Model ◽  
The Web

AbstractThe Resource Description Framework (RDF) is a flexible model for representing information about resources on the Web. As a W3C (World Wide Web Consortium) Recommendation, RDF has rapidly gained popularity. With the widespread acceptance of RDF on the Web and in the enterprise, a huge amount of RDF data is being proliferated and becoming available. Efficient and scalable management of RDF data is therefore of increasing importance. RDF data management has attracted attention in the database and Semantic Web communities. Much work has been devoted to proposing different solutions to store RDF data efficiently. This paper focusses on using relational databases and NoSQL (for ‘not only SQL (Structured Query Language)’) databases to store massive RDF data. A full up-to-date overview of the current state of the art in RDF data storage is provided in the paper.

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.

A Survey on JSON Mapping With XML/RDF

2019 ◽  
pp. 92-113
Author(s):  
Gbéboumé Crédo Charles Adjallah-Kondo ◽  
Zongmin Ma
Keyword(s):  
Resource Description Framework ◽  
Data Format ◽  
Web Resources ◽  
Description Framework ◽  
Resource Description ◽  
Exchange Data

As a data format, JSON is able to store and exchange data. It can be mapped with RDF (resource description framework), which is an ontology technology in the direction of web resources. This chapter replies to the question about which techniques or methods to utilize for mapping XML to JSON and RDF. However, a plethora of methods have been explored. Consequently, the goal of this survey is to give the whole presentation of the currents approaches to map JSON with XML and RDF by providing their differences.

Towards a Semantic Web of Evidence-Based Medical Information

2011 ◽  
pp. 254-273
Author(s):  
Rolf Grutter ◽  
Claus Eikemeier ◽  
Johann Steurer
Keyword(s):  
Semantic Web ◽  
Medical Information ◽  
Uniform Resource Identifier ◽  
Simple Method ◽  
Web Resources ◽  
Web Of Data ◽  
Description Framework ◽  
Object Models ◽  
Application Specific ◽  
Resource Description

It is the vision of the protagonists of the Semantic Web to achieve “a set of connected applications for data on the Web in such a way as to form a consistent logical Web of data” (Berners-Lee, 1998, p. 1). Therefore, the Semantic Web approach develops languages for expressing information in a machine-processable form (“machine-understandable” in terms of the Semantic Web community). Particularly, the Resource Description Framework, RDF (Lassila & Swick, 1999), and RDF Schema, RDFS (Brickley & Guha, 2000), are considered as the foundations for the implementation of the Semantic Web. RDF provides a data model and a serialization language; RDFS a distinguished vocabulary to model class and property hierarchies and other basic schema primitives that can be referred to from RDF models, thereby allowing for the modeling of object models with cleanly defined semantics. The idea behind this approach is to provide a common minimal framework for the description of Web resources while allowing for application-specific extensions (Berners-Lee, 1998). Such extensions in terms of additional classes and/or properties must be documented in an application-specific schema. Application-specific schemata can be integrated into RDFS by the namespace mechanism (Bray, Hollander & Layman, 1999). Namespaces provide a simple method for qualifying element and attribute names used in RDF documents by associating them with namespaces identified by URI (Uniform Resource Identifier) references (Berners-Lee, Fielding, Irvine & Masinter, 1998).

scholarly journals MuSe: a multi-level storage scheme for big RDF data using MapReduce

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Tanvi Chawla ◽  
Girdhari Singh ◽  
Emmanuel S. Pilli
Keyword(s):  
Linked Data ◽  
State Of The Art ◽  
Query Execution ◽  
Hadoop Mapreduce ◽  
Storage Scheme ◽  
The Common ◽  
Multi Level ◽  
Rdf Data ◽  
Description Framework ◽  
Resource Description

AbstractResource Description Framework (RDF) model owing to its flexible structure is increasingly being used to represent Linked data. The rise in amount of Linked data and Knowledge graphs has resulted in an increase in the volume of RDF data. RDF is used to model metadata especially for social media domains where the data is linked. With the plethora of RDF data sources available on the Web, scalable RDF data management becomes a tedious task. In this paper, we present MuSe—an efficient distributed RDF storage scheme for storing and querying RDF data with Hadoop MapReduce. In MuSe, the Big RDF data is stored at two levels for answering the common triple patterns in SPARQL queries. MuSe considers the type of frequently occuring triple patterns and optimizes RDF storage to answer such triple patterns in minimum time. It accesses only the tables that are sufficient for answering a triple pattern instead of scanning the whole RDF dataset. The extensive experiments on two synthetic RDF datasets i.e. LUBM and WatDiv, show that MuSe outperforms the compared state-of-the art frameworks in terms of query execution time and scalability.

Global Sensitivity Analysis for Fuzzy RDF Data

2021 ◽  
Vol 31 (08) ◽  
pp. 1119-1144
Author(s):  
Hatem Soliman ◽  
Izhar Ahmed Khan ◽  
Yasir Hussain
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Analysis Model ◽  
Global Sensitivity ◽  
Proposed Model ◽  
The World ◽  
Practical Tool ◽  
Rdf Data ◽  
Description Framework ◽  
Resource Description

The resource description framework (RDF) was adopted by the World Wide Web (W3C) as an essential semantic web standard and the RDF scheme. It accords the hard semantics in the description and wields the crisp metadata. However, it usually produces vague or ambiguous information. Consequently, fuzzy RDF helps deal with such special data by transforming the crisp values into a fuzzy set. A method for analyzing fuzzy RDF data is proposed in this paper. To this end, first, we decompose the RDF into fuzzy RDF variables. Second, we are designing a model for global sensitivity analysis based on the decomposition of fuzzy RDF. It figures out the ambiguities of fuzzy RDF data. The proposed global sensitivity analysis model provides the importance of fuzzy RDF data by considering the response function’s structure and reselects it to a certain degree. A practical tool for sensitivity analysis of fuzzy RDF data has also been implemented based on the proposed model.

Storage, Indexing, Query Processing, and Benchmarking in Centralized and Distributed RDF Engines: A Survey

2020 ◽  
Author(s):  
Waqas Ali ◽  
Muhammad Saleem ◽  
Bin Yao ◽  
Axel-Cyrille Ngonga Ngomo
Keyword(s):  
Query Processing ◽  
Data Storage ◽  
Substantial Effect ◽  
Query Execution ◽  
Web Based ◽  
Language Support ◽  
Query Response Time ◽  
Storage Solutions ◽  
Description Framework ◽  
Resource Description

The recent advancements of the Semantic Web and Linked Data have changed the working of the traditional web. There is a huge adoption of the Resource Description Framework (RDF) format for saving of web-based data. This massive adoption has paved the way for the development of various centralized and distributed RDF processing engines. These engines employ different mechanisms to implement key components of the query processing engines such as data storage, indexing, language support, and query execution. All these components govern how queries are executed and can have a substantial effect on the query runtime. For example, the storage of RDF data in various ways significantly affects the data storage space required and the query runtime performance. The type of indexing approach used in RDF engines is key for fast data lookup. The type of the underlying querying language (e.g., SPARQL or SQL) used for query execution is a key optimization component of the RDF storage solutions. Finally, query execution involving different join orders significantly affects the query response time. This paper provides a comprehensive review of centralized and distributed RDF engines in terms of storage, indexing, language support, and query execution.

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