Design of energy-loaded systems using the Neo4j graph database

Relational database management systems (RDBMSs) have been a common option to manage structured data over the past decades. In recent years, with the prevalence of big data applications, vast unstructured and semi-structured data are generated, deeply challenging the relational model used in RDBMSs. For this reason, a wide spectrum of NoSQL databases are developed for managing unstructured, semi-structured or structured data. For example, graph database management systems (GDBMSs) are commonly used as an important category of NoSQL databases, to manage sophisticated graph data as well as relational data. Nonetheless, as claimed in existing literatures, both RDBMSs and GDBMSs are capable of managing graph data and relational data, the boundaries of them still remain unclear. In this paper, we propose a unified benchmark for RDBMSs and GDBMSs, to evaluate them under the same metrics, and report which category is better in different application scenarios. We conduct extensive experiments over the unified benchmark, and report our findings: (1) RDBMSs are significantly faster for aggregations and order by operations, (2) GDBMSs are shown to be superior for projection, multi-table join and deep recursive operations, (3) GDBMSs and RDBMSs are comparable for two-table join and shallow recursive operations.

The data volume for the resource description framework (RDF) is growing rapidly. To query this large amount of data, two types of query processing approaches are there: the relational approach and graph-based approach. The relational approach offers better scalability and good security, but it suffers from query joins. The graph-based approach helps to eliminate query joins. Both relational and graph-based approach have their own advantages. This paper presents the performance analysis of relational database management system (RDBMS) and graph database management system (GDBMS) in terms of query processing. The evaluation of query execution time (QET) is carried out for the same benchmark linked observation data (LOD). A detailed comparison is carried out using two popular graph-based tools, Neo4j, and DGraph with relational database PostgreSQL. Neo4j uses cipher-query language, and DGraph uses GraphQL+−, and PostgreSQL uses standard SQL language for the RDBMS. The experiments are performed for four different types of queries. It has been observed that RDBMS outperforms GDBMS for star and range queries, and graph database works well with join and projection queries. We have extended our evaluation for the comparison of available graph tools, and it has been found that DGraph outperforms Neo4j for all the types of queries and works 2.5% faster for the query execution time as well.KeywordsCypherGraph databaseGraphQL+−RDFSQL

In the last few decades, Database Management Systems (DBMSs) became powerful tools for storing large amount of data and executing complex queries over them. In the recent years, the growing amount of unstructured or semi-structured data has seen a shift from representing data in the relational model towards alternative data models. Graph Databases and Graph Database Management Systems (GDBMSs) have seen an increase in use due to their ability to manage highly-interconnected, continuously evolving data. This thesis is a documentation of the work done in implementing a system to identify clusters in graph modeled data using a Label Propagation Community Detection Algorithm. The graph was built using datasets of academic publications in the field of Computer Science obtained from dblp.org . The system developed is a FullStack WebApp consisting of a web-based user interface, an API and the data (nodes, edges, graph) stored in a Graph Database Management System (GDBMS). Described in this document are: - the process of manipulation pre-import and import of the data in a Graph Database Management System (GDBMS) such as ArangoDB, creation of nodes, relations (edges) between the nodes and a graph composed of these nodes and edges; - the GraphQL API implemented in NodeJS to request data from the Graph Database Management System (GDBMS); - the frontend interface made with TypeScript and React consisting of the search functionalities and ability to visualize results in Cytoscape Network Graphs; - the Label Propagation Community Detection Algorithm execution on the graph, the found clusters which are stored and visualized to the user whenever requested. This thesis hopes to contribute with a practical hands-on approach on the graph representation, integration and analysis of interconnected data.

Efficient spatial query processing in Graph Database Management Systems (GDBMSs) has become increasingly important owing to the prevalence of spatial graph data. However, current GDBMSs lack effective spatial indexing, causing performance issues with complex spatial graph queries. This study proposes a spatial index called Subgraph Integrated R-Tree (SGIR-Tree) for efficient spatial query processing in GDBMSs. The SGIR-Tree integrates the hierarchical R-Tree structure with the graph structure of GDBMSs by converting R-Tree elements into graph components like nodes and edges. The Minimum Bounding Rectangle (MBR) information of spatial objects and R-Tree nodes is stored as properties of these graph elements, and the leaf nodes are directly connected to the spatial nodes. This approach combines the efficiency of spatial indexing with the flexibility of graph databases, thereby allowing spatial query results to be directly utilized in graph traversal. Experiments using OpenStreetMap datasets demonstrate that the SGIR-Tree outperforms the previous approaches in terms of query overhead and index overhead. The results are expected to improve spatial graph data processing in various fields, including location-based service and urban planning, significantly advancing spatial data management in GDBMSs.

In the era of big data, data analytics, business intelligence database management plays a vital role from technical business management and research point of view. Over many decades, database management has been a topic of active research. There are different type of database management system have been proposed over a period of time but Relational Database Management System (RDBMS) is the one which has been most popularly used in academic research as well as industrial setup[1]. In recent years, graph databases regained interest among the researchers for certain obvious reasons. One of the most important reasons for such an interest in a graph database is because of the inherent property of graphs as a graph structure. Graphs are present everywhere in the data structure, which represents the strong connectivity within the data. Most of the graph database models are defined in which data-structure for schema and instances are modeled as graph or generalization of a graph. In such graph database models, data manipulations are expressed by graph-oriented operations and type constructors [9]. Now days, most of the real world applications can be modeled as a graph and one of the best real world examples is social or biological network. This paper gives an overview of the different type of graph databases, applications, and comparison between their models based on some properties.

Using the reparation movement for Japanese colonial and wartime atrocities as a case study, this paper explores the applicability of graph database management systems to network analysis research and teaching in the field of digital humanities. Since the late 1980s, Asian victims of Japanese colonial and wartime atrocities (such as the infamous “comfort women” system) began to sue the Japanese government and corporations with the help of local and Japanese lawyers and activists. This paper uses the plethora of publicly available court materials produced by the movement to study social networks in this transnational historical justice movement and illustrate how graph database management systems can upgrade traditional network analysis methodologies in digital humanities. By inputting the data about lawsuits and lawyers in the movement into graph database applications like Neo4j and GraphXR, the paper demonstrates the advantages of managing network data in graph database structure in terms of scalability, modifiability, intuitive visibility, query efficiency, and analysis potential over relational database structure, which is the mainstream in network analysis research.

The analysis and utilization of grid big data, big data driven grid analysis and optimization have received extensive attention in recent years, and non-relational databases can handle big data problems efficiently. The graph database is a kind of non-relational database. This paper takes the graph database Neo4j as an example. Based on the domain modeling theory and follows the CIM model, the grid data modeling principle is proposed. Under these principles, some power grid data models and their applicable occasions, and methods for converting these models to each other are proposed. Finally, three graph database models are built using the modeling method proposed in this paper. The query performance of Neo4j and MySQL is compared by three examples. The conclusion is that in the traversal with obvious topological characteristics, such as shortest path traversal, deep traversal and time tree traversal, etc., the graph database model proposed in this paper can greatly improve the retrieval efficiency. It proves the effectiveness of the proposed modeling method and the efficiency of the graph database in specific retrieval.

This special issue of IT Professional focuses on the graph database. The graph database, a relatively new phenomenon, is well suited to the burgeoning information era in which we are increasingly becoming immersed. Here, the guest editors briefly explain how a graph database works, its relation to the relational database management system (RDBMS), and its quantitative and qualitative pros and cons, including how graph databases can be harnessed in a hybrid environment. They also survey the excellent articles submitted for this special issue.

Graph database management systems (GDBMSs) are highly optimized to perform fast traversals, i.e., joins of vertices with their neighbours, by indexing the neighbourhoods of vertices in adjacency lists. However, existing GDBMSs have system-specific and fixed adjacency list structures, which makes each system efficient on only a fixed set of workloads. We describe a new tunable indexing subsystem for GDBMSs, we call A+ indexes, with materialized view support. The subsystem consists of two types of indexes: (i) vertex-partitioned indexes that partition 1-hop materialized views into adjacency lists on either the source or destination vertex IDs; and (ii) edge-partitioned indexes that partition 2-hop views into adjacency lists on one of the edge IDs. As in existing GDBMSs, a system by default requires one forward and one backward vertex-partitioned index, which we call the primary A+ index. Users can tune the primary index or secondary indexes by adding nested partitioning and sorting criteria. Our secondary indexes are space-efficient and use a technique we call offset lists. Our indexing subsystem allows a wider range of applications to benefit from GDBMSs’ fast join capabilities. We demonstrate the tunability and space efficiency of A+ indexes through extensive experiments on three workloads.

Both object-oriented and relational database management systems are referred to as DBMS. The web-scale is expanding at an incredible rate, and with it comes an increase in mobile applications, technologies, and social media platforms. The rapid growth of these technologies produces a variety of structured and unstructured data in various forms, each of which has various consequences for data security. Using RDBMS and OODBMS, this study's methodology outlines the development, installation, and management process of multiple and complicated data sets. This study gives a documented study comparing different kinds of data stored in databases by employing two types of database techniques: relational database method and object-oriented database technique. We use a different fingerprint database for the web and mobile devices. This database is also managed using different technologies, such as MySQL, and it accommodates data generated using distinct methods. In addition, this study explores the benefits and advantages of the various databases and determines the ideal database management system for organizations. According to the survey's findings, RDBMS stores data and the relationships between them in tables, but OODBMS stores data in objects, in a manner analogous to Object-Oriented Programming (OOP). According to the study's findings, the primary distinction between relational database management systems (RDBMS) and object-oriented database management systems (OODBMS) is that RDBMS is a Database Management System based on the relational model. In contrast, OODBMS is a Database Management System that facilitates creating and modeling data as objects.

The architecture of Geographic Information Systems (GISs) is changing: more and more systems are based on the integrated architecture, i.e. storing geometric data in the Data Base Management System (DBMS) together with administrative data. The first step in building a Geo-DBMS is by having data types and operators for simple features (i.e. geometric primitives): point, line and polygon. This has reached a level of standardisation and is now implemented in several commercial DBMSs. The next step is to have support for the topologically structured features in the DBMS, i.e. complex features. The DBMS can then check and guarantee consistency. In addition, complex operations can be executed within the DBMS. Despite the fact that topologically structured models are well known and that it is not difficult to store the topological references, it still remains an unresolved issue as to how to effectively implement these models within a relational DBMS. In this paper, we describe the design and implementation of a topologically structured management at the DBMS level. Our focus is to translate topological structures into geometric primitives. It is then possible to define a DBMS view on a topological primitive, which makes this appear as a geometric primitive. This process supports the best of both worlds: on the one hand there are advantages of the topological structure (no redundancy) and on the other hand the ease of explicit geometric primitives in querying, analysis and presentation is available.

This paper compares two approaches to meeting the data handling requirements of Intermedia, a hypermedia system developed at the Institute for Research in Information and Scholarship at Brown University. Intermedia, though written using an object-oriented programming language, relies on a traditional relational database management system for data storage and retrieval. We examine the ramifications of replacing the relational database with an object-oriented database. We begin by describing the important characteristics each database system. We then describe Intermedia and give an overview of its architecture and its data handling requirements. We explain why and how we used a relational database management system and the problems that we encountered with this system. We then present the design of an object-oriented database schema for Intermedia and compare the relational and object-oriented database management system approaches.

This paper presents an original approach to parallel processing of very large databases by means of encapsulation of partitioned parallelism into open-source database management systems (DBMSs). The architecture and methods for implementing a parallel DBMS through encapsulation of partitioned parallelism into PostgreSQL DBMS are described. Experimental results that confirm the effectiveness of the proposed approach are presented.

In recent years, the manufacturing industry is changing its way of production enabled by emerging technologies. Enterprises publish visualised resources as services on the cloud. To fulfil a complex manufacturing request, services can be composed together. However, how to compose proper services among a large number of services becomes a challenge. This paper presents a service composition graph model to deal with the challenge by using a graph database. Specifically, the service composition graph model is stored in the Neo4j graph database. Services and their inputs/outputs are stored as nodes in a directed bipartite graph that are connected with edges. The weights of edges are synthesised via a fuzzy control system according to the QoS of services. Possible compositions of services are calculated and pre-composed. When the manufacturing task arrives, an extended Dijkstra algorithm is used to find a solution. Experimental results show that the approach could return a solution satisfying both functional and QoS requirements within a short time.

In modern and interconnected world, information is accumulatively stored digitally, making the process of exchanging, gathering and querying the information much easier. Continuously, it has introduced new challenges about how to ensure its consistency and reliability due to the sheer volume of data. A blockchain-based information system can provide an incorruptible record of history, enabling better auditing and data management practices. The paper describes how to combine an Exonum blockchain and a Neo4j graph database into a system that can provide a verifiable audit trail of data integrity and its modifications for information stored in a graph database.