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.

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

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.

scholarly journals N3Logic: A logical framework for the World Wide Web

2008 ◽  
Vol 8 (3) ◽  
pp. 249-269 ◽  
Author(s):  
TIM BERNERS-LEE ◽  
DAN CONNOLLY ◽  
LALANA KAGAL ◽  
YOSI SCHARF ◽  
JIM HENDLER
Keyword(s):  
Semantic Web ◽  
Data Model ◽  
Structured Data ◽  
Minimal Extension ◽  
Web Environment ◽  
Knowledge Models ◽  
Rdf Data ◽  
Description Framework ◽  
Resource Description ◽  
The Web

AbstractThe Semantic Web drives toward the use of the Web for interacting with logically interconnected data. Through knowledge models such as Resource Description Framework (RDF), the Semantic Web provides a unifying representation of richly structured data. Adding logic to the Web implies the use of rules to make inferences, choose courses of action, and answer questions. This logic must be powerful enough to describe complex properties of objects but not so powerful that agents can be tricked by being asked to consider a paradox. The Web has several characteristics that can lead to problems when existing logics are used, in particular, the inconsistencies that inevitably arise due to the openness of the Web, where anyone can assert anything. N3Logic is a logic that allows rules to be expressed in a Web environment. It extends RDF with syntax for nested graphs and quantified variables and with predicates for implication and accessing resources on the Web, and functions including cryptographic, string, math. The main goal of N3Logic is to be a minimal extension to the RDF data model such that the same language can be used for logic and data. In this paper, we describe N3Logic and illustrate through examples why it is an appropriate logic for the Web.

A novel property graph model for knowledge representation on the Web

2018 ◽  
Vol 7 (3.33) ◽  
pp. 187
Author(s):  
Heekyung Moon ◽  
Zhanfang Zhao ◽  
Jintak Choi ◽  
Sungkook Han
Keyword(s):  
Open Data ◽  
Graph Model ◽  
Graph Models ◽  
Nosql Databases ◽  
Graph Data ◽  
The Core ◽  
Description Framework ◽  
Resource Description ◽  
The Web ◽  
Core Features

Graphs provide an effective way to represent information and knowledge of real world domains. Resource Description Framework (RDF) model and Labeled Property Graphs (LPG) model are dominant graph data models widely used in Linked Open Data (LOD) and NoSQL databases. Although these graph models have plentiful data modeling capabilities, they reveal some drawbacks to model the complicated structures. This paper proposes a new property graph model called a universal property graph (UPG) that can embrace the capability of both RDF and LPG. This paper explores the core features of UPG and their functions. 

A comprehensive overview of RDF for spatial and spatiotemporal data management

2021 ◽  
Vol 36 ◽  
Author(s):  
Fu Zhang ◽  
Qingzhe Lu ◽  
Zhenjun Du ◽  
Xu Chen ◽  
Chunhong Cao
Keyword(s):  
Data Management ◽  
Spatiotemporal Data ◽  
Future Research ◽  
Effective Management ◽  
Comprehensive Overview ◽  
Management Tools ◽  
Rdf Data ◽  
Description Framework ◽  
Main Ideas ◽  
Resource Description

Abstract Currently, a large amount of spatial and spatiotemporal RDF data has been shared and exchanged on the Internet and various applications. Resource Description Framework (RDF) is widely accepted for representing and processing data in different (including spatiotemporal) application domains. The effective management of spatial and spatiotemporal RDF data are becoming more and more important. A lot of work has been done to study how to represent, query, store, and manage spatial and spatiotemporal RDF data. In order to grasp and learn the main ideas and research results of spatial and spatiotemporal RDF data, in this paper, we provide a comprehensive overview of RDF for spatial and spatiotemporal data management. We summarize spatial and spatiotemporal RDF data management from several essential aspects such as representation, querying, storage, performance assessment, datasets, and management tools. In addition, the direction of future research and some comparisons and analysis are also discussed in depth.

Relational Techniques for Storing and Querying RDF Data

2011 ◽  
pp. 269-285
Author(s):  
Sherif Sakr ◽  
Ghazi Al-Naymat
Keyword(s):  
Semantic Web ◽  
Resource Description Framework ◽  
Query Evaluation ◽  
Evaluation Strategies ◽  
Fundamental Challenge ◽  
Rdf Data ◽  
Description Framework ◽  
Resource Description ◽  
Flexible Model ◽  
The Web

The Resource Description Framework (RDF) is a flexible model for representing information about resources in the Web. With the increasing amount of RDF data which is becoming available, efficient and scalable management of RDF data has become a fundamental challenge to achieve the Semantic Web vision. The RDF model has attracted attentions in the database community and many researchers have proposed different solutions to store and query RDF data efficiently. This chapter focuses on using relational query processors to store and query RDF data. It gives an overview of the different approaches and classifies them according to their storage and query evaluation strategies.

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.

scholarly journals Agile Transformation at Scale: A Tertiary Study

2021 ◽  
pp. 3-11
Author(s):  
Suddhasvatta Das ◽  
Kevin Gary
Keyword(s):  
Large Scale ◽  
Software Industry ◽  
Future Research ◽  
Small Scale ◽  
Agile Processes ◽  
Current State ◽  
Research Findings ◽  
State Of Research ◽  
Agile Software ◽  
Tertiary Study

AbstractDue to the fast-paced nature of the software industry and the success of small agile projects, researchers and practitioners are interested in scaling agile processes to larger projects. Agile software development (ASD) has been growing in popularity for over two decades. With the success of small-scale agile transformation, organizations started to focus on scaling agile. There is a scarcity of literature in this field making it harder to find plausible evidence to identify the science behind large scale agile transformation. The objective of this paper is to present a better understanding of the current state of research in the field of scaled agile transformation and explore research gaps. This tertiary study identifies seven relevant peer reviewed studies and reports research findings and future research avenues.

Sign in / Sign up

Export Citation Format

Share Document