Designing Graph Databases With GRAPHED

2019 ◽  
Vol 30 (1) ◽  
pp. 41-60 ◽  
Author(s):  
Gustavo Cordeiro Galvão Van Erven ◽  
Rommel Novaes Carvalho ◽  
Waldeyr Mendes Cordeiro da Silva ◽  
Sergio Lifschitz ◽  
Harley Vera-Olivera ◽  
...  
Keyword(s):  
Social Networks ◽  
Data Model ◽  
Biological Network ◽  
Database Systems ◽  
Graph Database ◽  
Graph Databases ◽  
Database Modeling ◽  
Schema Design ◽  
Conceptual Data ◽  
The Relationship

In recent years, graph database systems have become very popular and been deployed mainly in situations where the relationship between data is significant, such as in social networks. Although they do not require a particular schema design, a data model contributes to their consistency. Designing diagrams is an approach to satisfying this demand for a conceptual data model. While researchers and companies have been developing concepts and notations for graph database modeling, their notations focus on their specific implementations. In this article, the authors propose a diagram to address this lack of a generic and comprehensive notation for graph databases modeling, named GRAPHED (Graph Description Diagram for Graph Databases). The authors verified the effectiveness and compatibility of GRAPHED in two case studies: fraud identification, and a biological network model.

scholarly journals A Rule-based Conversion of an EER Schema to Neo4j Schema Constraints

2021 ◽  
Author(s):  
Telmo Henrique Valverde da Silva ◽  
Ronaldo dos Santos Mello
Keyword(s):  
Data Model ◽  
Design Methodology ◽  
A Priori ◽  
Database Management System ◽  
Conversion Process ◽  
Graph Database ◽  
Graph Databases ◽  
Rule Based ◽  
Promising Solution ◽  
Entity Relationship

Several application domains hold highly connected data, like supply chain and social network. In this context, NoSQL graph databases raise as a promising solution since relationships are first class citizens in their data model. Nevertheless, a traditional database design methodology initially defines a conceptual schema of the domain data, and the Enhanced Entity-Relationship (EER) model is a common tool. This paper presents a rule-based conversion process from an EER schema to Neo4j schema constraints, as Neo4j is the most representative NoSQL graph database management system with an expressive data model. Different from related work, our conversion process deals with all EER model concepts and generates rules for ensuring schema constraints through a set of Cypher instructions ready to run into a Neo4j database instance, as Neo4J is a schemaless system, and it is not possible to create a schema a priori. We also present an experimental evaluation that demonstrates the viability of our process in terms of performance.

Databases Modeling of Engineering Information

2009 ◽  
pp. 338-361
Author(s):  
Z. M. Ma
Keyword(s):  
Data Storage ◽  
Database Systems ◽  
Data Models ◽  
Information Modeling ◽  
Database Modeling ◽  
Business Applications ◽  
Database Models ◽  
Engineering Information ◽  
Computer Based ◽  
Conceptual Data

Information systems have become the nerve center of current computer-based engineering applications, which hereby put the requirements on engineering information modeling. Databases are designed to support data storage, processing, and retrieval activities related to data management, and database systems are the key to implementing engineering information modeling. It should be noted that, however, the current mainstream databases are mainly used for business applications. Some new engineering requirements challenge today’s database technologies and promote their evolvement. Database modeling can be classified into two levels: conceptual data modeling and logical database modeling. In this chapter, we try to identify the requirements for engineering information modeling and then investigate the satisfactions of current database models to these requirements at two levels: conceptual data models and logical database models. In addition, the relationships among the conceptual data models and the logical database models for engineering information modeling are presented in the chapter viewed from database conceptual design.

Design for Database of Energy Project Management Based on Portfolio

2011 ◽  
Vol 211-212 ◽  
pp. 62-67
Author(s):  
Yun Na Wu ◽  
Jiang Shuai Li ◽  
Jia Li Wang
Keyword(s):  
Project Management ◽  
Portfolio Management ◽  
Data Model ◽  
Management System ◽  
Management Technique ◽  
Project Portfolio ◽  
Project Portfolio Management ◽  
Database Modeling ◽  
Project Management System ◽  
Conceptual Data

With the continuous development of energy projects and the actual needs of the project, project portfolio management technique is known by people more and more. However, current databases of energy project management system are too different. This paper studies actual demand of energy project database, taking portfolio management theory as the basic, and use database modeling technology to build database’s conceptual data model, logical data model and physics data model based on the portfolio of energy project management. These models can be very good instruction of energy database design and construction, and will support energy project portfolio management system design to some guidance.

Efficient Fault-Tolerant Transactions for Distributed Graph Database

2019 ◽  
Vol 1 (2) ◽  
pp. 14-20
Author(s):  
Ray Neiheiser ◽  
Roland Schmitz ◽  
Luciana Rech ◽  
Manfredo Manfredini
Keyword(s):  
Social Networks ◽  
Fault Tolerance ◽  
Linked Data ◽  
Fault Tolerant ◽  
Graph Database ◽  
Graph Databases ◽  
Massive Growth ◽  
Atomic Broadcast ◽  
A Performance

Through the ongoing trend in graph technologies due to the massive growth of linked data produced by social networks graph databases gained popularity. Replication, a common approach to increase availability in databases, is also used by diverse graph database solutions. Few approaches implementing fault-tolerance in graph databases have been proposed yet.This paper considers deferred update replication using atomic broadcast in order to implement fault-tolerance in distributed graph databases. The main contribution of this paper is a deferred update algorithm adapted to graph databases offering a more scalable and faster solution, showing a performance advantage of over 30\% compared to existing approaches.

scholarly journals Application of graph databases for transport purposes

2016 ◽  
Vol 64 (3) ◽  
pp. 457-466 ◽  
Author(s):  
A. Czerepicki
Keyword(s):  
Information System ◽  
Data Structures ◽  
Public Transport ◽  
Data Model ◽  
Query Language ◽  
Relational Model ◽  
Graph Database ◽  
Graph Databases ◽  
Graph Data ◽  
Innovative Concept

Abstract The article presents an innovative concept of applying graph databases in transport information systems. The model of a graph database has been presented together with implementation of data structures and search operations in a graph. The transformation concept of relational model to a graph data model has been developed. The schema of graph database has been proposed for public transport information system purposes. The realization methods have been illustrated by the use of search function based on the Cypher query language.

scholarly journals An Efficient Graph Database Model

2019 ◽  
Vol 8 (10) ◽  
pp. 1292-1295
Keyword(s):  
Data Model ◽  
Relational Databases ◽  
Maximum Amount ◽  
Graph Database ◽  
Graph Databases ◽  
Query Execution ◽  
Relational Data Model ◽  
Query Response Time ◽  
Model Graph ◽  
Target Database

Relational databases are holding the maximum amount of data underpinning the web. They show excellent record of convenience and efficiency in repository, optimized query execution, scalability, security and accuracy. Recently graph databases are seen as an good replacement for relational database. When compared to the relational data model, graph data model is more vivid, strong and data expressed in it models relationships among data properly. An important requirement is to increase the vast quantities of data stored in RDB into web. In this situation, migration from relational to graph format is very advantageous. Both databases have advantages and limitations depending on the form of queries. Thus, this paper converts relational to graph database by utilizing the schema in order to develop a dual database system through migration, which merges the capability of both relational db and graph db. The experimental results are provided to demonstrate the practicability of the method and query response time over the target database. The proposed concept is proved by implementing it on MySQL and Neo4j

Technical Survey Graph Databases and Applications

2015 ◽  
Vol 09 (04) ◽  
pp. 523-545 ◽  
Author(s):  
Shao-Ting Wang ◽  
Jennifer Jin ◽  
Pete Rivett ◽  
Atsushi Kitazawa
Keyword(s):  
Social Networks ◽  
Relational Databases ◽  
Query Languages ◽  
The Internet ◽  
Complex Data ◽  
Graph Database ◽  
Graph Databases ◽  
Amount Of Information ◽  
Related Application ◽  
Fast Access

Graph databases can be defined as databases that use graph structures with nodes, edges and properties to store data. Semantic queries and graph-oriented operations are used to access them. With a rapidly growing amount of information on the Internet in recent years, relational databases suffer performance degradation as a large number of nodes are added due to the number of entries in join tables. Therefore, based on the network nature of Internet activities, graph databases are designed for fast access to complex data found in social networks, recommendation engines and networked system. The main objective of this survey is to present the work that has been done in the area of graph database, including query languages, processing, and related application.

Databases Modeling of Engineering Information

2011 ◽  
pp. 65-84
Author(s):  
Z.M. Ma
Keyword(s):  
Data Storage ◽  
Database Systems ◽  
Data Models ◽  
Information Modeling ◽  
Database Modeling ◽  
Business Applications ◽  
Database Models ◽  
Engineering Information ◽  
Computer Based ◽  
Conceptual Data

Information systems have become the nerve center of current computer-based engineering applications, which hereby put the requirements on engineering information modeling. Databases are designed to support data storage, processing, and retrieval activities related to data management, and database systems are the key to implementing engineering information modeling. It should be noted that, however, the current mainstream databases are mainly used for business applications. Some new engineering requirements challenge today’s database technologies and promote their evolvement. Database modeling can be classified into two levels: conceptual data modeling and logical database modeling. In this chapter, we try to identify the requirements for engineering information modeling and then investigate the satisfactions of current database models to these requirements at two levels: conceptual data models and logical database models. In addition, the relationships among the conceptual data models and the logical database models for engineering information modeling are presented in the chapter viewed from database conceptual design.

Handling Imprecise and Uncertain Engineering Information in IDEF1X and Relational Data Models

2004 ◽  
Author(s):  
Z. M. Ma
Keyword(s):  
Relational Database ◽  
Data Model ◽  
Manufacturing Systems ◽  
Relational Databases ◽  
Manufacturing Industries ◽  
Fuzzy Data ◽  
Uncertain Information ◽  
Database Modeling ◽  
Engineering Information ◽  
Conceptual Data

Database modeling of engineering information is crucial for constructing manufacturing systems because current manufacturing industries are typically information-based enterprises and information systems have become their nervous center. Engineering information can be modeled at two levels: conceptual data model and logical database model. Generally a conceptual data model is designed and then the designed conceptual data model will be transformed into the chosen logical database schema. Imprecise and uncertain information, however, is generally involved in many engineering activities and imprecise and uncertain engineering information are represented by fuzzy sets. Nowadays relational databases are still the most useful database product and IDEF1X is most useful for logical database design of relational databases in engineering. So in this paper, we focus on fuzzy data modeling in IDEF1X and relational databases. The formal approaches to mapping fuzzy IDEF1X models to fuzzy relational database schemes are hereby developed.

scholarly journals GRAPH DATABASE MANAGEMENT SYSTEMS AND GRAPH THEORY

2020 ◽  
Author(s):  
Kornelije Rabuzin ◽  
◽  
Sonja Ristić ◽  
Robert Kudelić ◽  
◽  
...  
Keyword(s):  
Graph Theory ◽  
Data Model ◽  
Database Management ◽  
Database Management Systems ◽  
Management Systems ◽  
Graph Database ◽  
Graph Databases ◽  
Graph Data ◽  
Model Based

In recent years, graph databases have become far more important. They have been proven to be an excellent choice for storing and managing large amounts of interconnected data. Since graph databases (GDB) rely on a graph data model based on graph theory, this study examines whether currently available graph database management systems support the principles of graph theory, and, if so, to what extent. We also show how these systems differ in terms of implementation and languages, and we also discuss which graph database management systems are used today and why.

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