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Big Graph Analytics
Published in Mohiuddin Ahmed, Al-Sakib Khan Pathan, Data Analytics, 2018
Ahsanur Rahman, Tamanna Motahar
DG-SPARQL [47] is a distributed graph database management system. Graph databases store the graph data as entities (vertex) and relationships (edges). Each vertex in a graph database stores a list of relationship records that signify its relationships to other vertices. These relationship records are organized by type and direction and may hold additional attributes. Whenever a JOIN operation is executed, the database just uses this list and directly accesses its neighbors, which eliminates the need for an expensive search or match computation that are done in traditional relational databases. As a result, graph databases often outperform relational databases in terms of efficiency. Graph databases also allow the user to perform different graph queries including subgraph matching queries, path algebras, regular path queries, and reachability queries. Queries in a graph database are written using a graph query language, such as OpenCypher, SPARQL, GRAPQL, or Gremlin.15 For example, DG-SPARQL uses a version of SPARQL as a query language.
A graph-based approach for management and linking of BIM models with further AEC domain models
Published in Jan Karlshøj, Raimar Scherer, eWork and eBusiness in Architecture, Engineering and Construction, 2018
Each node of a property graph has a unique identifier and zero or more labels (object classes). Node labels could be associated to node typing in order to provide schema-based restrictions and a kind of basic classification mechanism. Similarly, each edge/relationship has a unique identifier, and one or more labels. In our research we use Neo4j graph database which implements the Property Graph Model and it provides full database characteristics including ACID transaction compliance (Robinson, Webber, & Eifrem, 2015). The Neo4j graph model can be accessed through a graph query language called Cypher that was introduced in the Neo4j. Cypher uses symbols to express patterns that correspond to a visual understanding of data, making it particularly well-suited to the challenges of querying connected data.
Application of graph databases in the communication and information asset management in power grid
Published in Lin Liu, Automotive, Mechanical and Electrical Engineering, 2017
Xuming Lv, Shanqi Zheng, Zhao Li, Siyan Liu, Yue Wang
Neo4j, a graph database management system developed by Neo Technology Inc, is one of the most popular graph databases according to www.db-engines.com. The first version is released in 2010, and the latest version, i.e., the third version is released in 2016. Neo4j supports graph model called “Property graph”, which includes nodes, edged and attribute (i.e., properties). Neo4j is written in Java, and provides APIs which are exposed through a whole range of various languages, e.g. Java, Python, Ruby, JavaScript, PHP, .NET, etc. It is noteworthy that C and C++ are not in the above list. Cypher is a declarative graph query language for the graph database Neo4j, and is roughly equivalent to SQL querying language in relational databases. The database system also implements the Blue-prints interface and a native REST interface to further expand the ways to communicate with the database. Neo4j provides three editions: Community, Enterprise, and Government. Community version is provided for individuals to learn graph databases and conduct smaller projects that do not require high levels of scaling. However, it excludes professional services and support. The Neo4j Enterprise edition offers incredible power and flexibility, with enterprise-grade availability, management and scale-up & scale-out capabilities.
Analysing the past to prepare for the future: Writing a literature review a roadmap for release 2.0
Published in Journal of Decision Systems, 2020
Richard T. Watson, Jane Webster
A graph is composed of nodes and edges. In the domain of literature reviewing, an element of interest (e.g. a concept or process) is a node and a relationship between a pair of elements is an edge. A labelled graph properties database allows nodes and relationships to have properties (Negro, 2018; Robinson et al., 2013). A property of an element might be a concept’s name (e.g. information asymmetry) and its type (e.g. a concept). The property of an edge relationship could be a descriptor of the relationship, such as ‘precedes‘ in the case of a process diagram or ‘causes’ for a causal model. Another property could indicate the nature of a relationship, such as causal or temporal. Nodes can also have one or more labels, which are used to group nodes together and indicate one or more roles. Thus, all elements of the same type (e.g. processes) could be so labelled to group them. We suggest that a graph description language (GDL) could help in this regard in defining elements and nomological relationship maps. A graph query language (GQL) is used to query a graph database and provides features similar to SQL for the relational model. ISO is working on specifying a standard GQL based on openCypher and similar languages.3 In this article, we use openCypher (or simply Cypher), which is currently the most widely adopted open query language for graph databases.4 Cypher can be used to define and manipulate property graphs (Appendix A). We now consider relationship maps and their descriptions.