Analyzing graph databases by aggregate queries

In developing NoSQL databases, a major motivation is to achieve better efficient query performance compared with relational databases. The graph database is a NoSQL paradigm where navigation is based on links instead of joining tables. Links can be implemented as pointers, and following a pointer is a constant time operation, whereas joining tables is more complicated and slower, even in the presence of foreign keys. Therefore, link-based navigation has been seen as a more efficient query approach than using join operations on tables. Existing studies strongly support this assumption. However, query complexity has received less attention. For example, in enterprise information systems, queries are usually complex so data need to be collected from several tables or by traversing paths of graph nodes of different types. In the present study, we compared the query performance of a graph-based database system (Neo4j) and relational database systems (MySQL and MariaDB). The effect of different efficiency issues (e.g., indexing and optimization) were included in the comparison in order to investigate the most efficient solutions for different query types. The outcome is that although Neo4j is more efficient for simple queries, MariaDB is essentially more efficient when the complexity of queries increases. The study also highlighted how dramatically the efficiency of relational database has grown during the last decade.

Graph theory, which is a branch of discrete mathematics, has emerged as a powerful tool in various domains, including database management. This abstract investigates the ways in which ideas and methods from graph theory which can be applied to database systems, offering a thorough synopsis of their benefits. Complex interactions within data can be well-modeled by using the basic concepts of graph theory, such as nodes, edges, and relationships. Because of its capacity to represent and query complex relationships, graph databases have become more and more popular in the field of database administration. Graph databases are well-suited for situations such as social networks, recommendation systems, and interconnected data domains because they are excellent at representing and traversing relationships, in contrast to standard relational databases, which are excellent at managing structured data. The abstract delves into the key graph-based data models, such as property graphs, RDF (Resource Description Framework), explaining how they facilitate the representation of diverse relationships. Furthermore, it explores the efficient storage and retrieval mechanisms that leverage graph traversal algorithms to extract valuable insights from interconnected datasets. The document highlights specific use cases where graph theory contributes to database management, including fraud detection, social network analysis, and recommendation systems. Additionally, it discusses the challenges associated with integrating graph databases into existing infrastructures and proposes solutions to address scalability and performance concerns. The abstract also touches upon the advancements in graph database query languages (Cypher) and SPARQL, showcasing their expressive power in querying complex relationships. The inclusion of graph-based indexing and optimization techniques demonstrates how database systems can efficiently handle queries involving large-scale graph data. As graph databases continue to evolve, this abstract concludes by outlining potential future directions in the intersection of graph theory and database management. It emphasizes the importance of ongoing research in developing scalable and efficient solutions for managing interconnected data, ultimately paving the way for more sophisticated and context-aware database systems relationships. Furthermore, it explores the efficient storage and retrieval mechanisms that leverage graph traversal algorithms to extract valuable insights from interconnected datasets.

Schema mappings establish a correspondence between data stored in two databases, called source and target respectively. Query processing under schema mappings has been investigated extensively in the two cases where each target atom is mapped to a query over the source (called GAV, global-as-view), and where each source atom is mapped to a query over the target (called LAV, local-as-view). The general case, called GLAV, in which queries over the source are mapped to queries over the target, has attracted a lot of attention recently, especially for data exchange. However, query processing for GLAV mappings has been considered only for the basic service of query answering, and mainly in the context of conjunctive queries (CQs) in relational databases. In this paper we study query processing for GLAV mappings in a wider sense, considering not only query answering, but also query rewriting, perfectness (the property of a rewriting to compute exactly the certain answers), and query containment relative to a mapping. We deal both with the relational case, and with graph databases, where the basic querying mechanism is that of regular path queries. Query answering in GLAV can be smoothly reduced to a combination of the LAV and GAV cases, and for CQs this reduction can be exploited also for the remaining query processing tasks. In contrast, as we show, GLAV query processing for graph databases is non-trivial and requires new insights and techniques. We obtain upper bounds for answering, rewriting, and perfectness, and show decidability of relative containment.

One of the key differences between graph and relational databases is that on graphs we are much more interested in navigational queries. As a consequence, graph database systems are specifically engineered to answer these queries efficiently, and there is a wide body of work on query languages that can express complex navigational patterns.

The importance of big data, one of the most popular and researched topics of today, has been a subject of constant debate. Big data appear in almost every aspect of our lives, such as healthcare, education, shopping, social media, and industry; however, its storage and processing is not very efficient when using traditional methods. Therefore, the aim of this study is to obtain big data using a novel social shopping application that collects data from its users. The application is designed to collect information about people, products, and friendships among people, as well as product relationships, and post creations, and also enables setting up various options to mark products, such as like, buy, have, or help. The data gathered by the system is analyzed using both relational and non-relational databases, and the performance of these databases is then compared using specific queries, to reveal the model which performs better and more efficiently. Three different database models were designed and implemented for the system; a relational database, a document database, and a graph database. As a result of the experiments, there is no single model that is superior to the others as all three databases have their advantages and disadvantages. Therefore, the database selection should be decided based on the application domain.

The article presents a study of the tasks that determine the features of power-loaded networks, and tasks that are solved using graph models for power-loaded networks. The tasks that determine the features of power-loaded networks are communication check, isolation of network components and components, combinatorial tasks on graphs, calculation of flow distribution, graph simplification, optimal impact on the power grid, and determination of power proportions. This article presents the high-level architecture of Neo4j. Neo4j is an open-source graph database management system implemented in Java and is considered the most comprehensive and promising graph database management system. In comparison with modelling a graph database using a relational database management system, this approach allows applying additional optimization in the case of data with a more complex and loaded structure. The article proposes a model of an energy-loaded network, because it is a system of interaction of a large number of loads or consumers, active sources, communication lines. The declarative query language for graph databases Cypher was used for implementation. A graph database of an electrical network with a description of analytical data processing using a mathematical algorithm and software specifics of the work of queries in a multi-relational database environment has been built. When a query is executed for a user-specified network segment, a connectivity component is allocated; then for the selected elements of the network graph, correspondence is made with the elements of the network itself.

The Next Generation Linkage Management System (NGLMS) was designed around keeping all data in a graph database. However, this constraint, while easily achievable for greenfield projects and/or new data linkage units, may not be easily met where legacy data exists.
 Objectives and ApproachThe NGLMS was extended to encompassed system where data was held partially or even completely in a relational database. By grouping the data managed by the NGLMS into system metadata, record link data and record data and allowing system metadata and record data to be stored separately and independently in either a relational or a graph database, the NGLMS allows hybrid installations of mixed graph and relational data and, with some loss of functionality, purely relational installations.
 ResultsThe functionality of the NGLMS was expanded to allow review of existing legacy data stored in a relational database system. Through minor changes to the server used by the NGLMS Clerical Review tool (NGLMS-CR) the review tools was able to present the same interface and allow the same integration as project stored completely within the graph database.
 Hybrid projects where link information was stored in a graph could be accommodated with no loss of functionality.Relational-only projects allowed clerical review of identified clusters in a manner identical to the graph-only NGLMS but involved some curtailment of the advanced clustering, time-slicing, compositional and concurrent functionality of the NGLMS due to the loss of the functionality provided by a graph database. They do provide an upgrade pathway to hybrid projects and then graph-only projects.
 Conclusion / ImplicationsAllowing the NGLMS to be configured as a hybrid system enables a gradual adoption of the NGLMS toolset and software.
 
 purely relational data still allows the use of the NGLMS-CR with customisable workpools and workflows
 a hybrid relational/graph system where record data is still kept in a relational store but cluster and linkage information is kept in the graph store allows the use of legacy data without major disruption and provides a pathway to full adoption

Graph databases such as neo4j are designed to handle and integrate big data from heterogeneous sources. For flexibility and performance they do not ensure data quality through schemata but leave it to the application level. In this paper, we present a model-driven approach for data integration through graph databases with data sources in relational databases. We model query and update operations in neo4j by triple graph grammars and map these to Gremlin code for execution. In this way we provide a model-based approach to data integration that is both visual and formal while providing the data quality assurances of a schema-based solution.

Today we have to deal with large amounts of data, and for large amounts of interconnected data, graph databases represent an excellent choice. However, some concepts that are used in relational databases are still not supported in graph databases, and this could hinder their wider use. In this article, how relational database concepts have been successfully transferred into graph databases, including integrity constraint support, inheritance, trigger specification, new visual query languages, etc., is demonstrated.

Assets are critical working tools in any organisation; their technical nature makes them significant for the effective outcome of strategic designs in today’s fast-growing technological age; hence, proper management of AIS is important for an increase in organisational performance. Such conventional approaches as relational databases or data warehousing are not entirely suitable for dealing with the problem intricacy, size and volatility of today’s connected data. This paper focuses on the assessment of the traditional and novel perspectives of data relationship management in AIS. It also compares traditional approaches like Relational and Object-oriented databases with new/Emerging technologies like Graph databases which optimise data complexity and semantic web technologies for better integration of the data. Also, the integration of advanced computational technologies such as machine learning and artificial intelligence improves predictive analysis and data mining to promote decision-making and organisational performance. Advanced technologies, such as blockchain and cloud applications, advance asset management even more because of their enhanced security, clarity, and expansiveness in approaching relevant databases. Blockchain enhances the features of decentralised record-keeping and immutability, while cloud solutions are scalable storage and computational resources. This paper considers problems like integration, data quality, data scalability and security and also provides some recommendations for further studies to solve these problems. Through examining these developments, the wish for the paper is to contribute to the improvement of asset management as well as decisions in this sphere in the context of growing concern and popularity of technologies.

Cellular regulation is inherently complex, and one particular cellular function is often controlled by a cascade of different types of regulatory interactions. For example, the activity of a transcription factor (TF), which regulates the expression level of downstream genes through transcriptional regulation, can be regulated by small molecules through compound–protein interactions. To identify such complex regulatory cascades, traditional relational databases require ineffective additional operations and are computationally expensive. In contrast, graph databases are purposefully developed to execute such deep searches efficiently. Here, we present ERMer (E. coli Regulation Miner), the first cloud platform for mining the regulatory landscape of Escherichia coli based on graph databases. Combining the AWS Neptune graph database, AWS lambda function, and G6 graph visualization engine enables quick search and visualization of complex regulatory cascades/patterns. Users can also interactively navigate the E. coli regulatory landscape through ERMer. Furthermore, a Q&A module is included to showcase the power of graph databases in answering complex biological questions through simple queries. The backend graph model can be easily extended as new data become available. In addition, the framework implemented in ERMer can be easily migrated to other applications or organisms. ERMer is available at https://ermer.biodesign.ac.cn/.

The paper presents the comparison study of relational, document and graph databases. A social networking web application supporting all three types of database was developed. The analysis consists in data models analysis and performance tests. Data models analysis discusses requirements meeting and difficulties of different implementation. Performance tests present results of obtained for five different tasks. Each of analyzed solutions revealed their strengths and weaknesses. The analysis showed that the graph model the most accurately models the reality. Document database queries occur to be the simplest in use. In terms of performance, PostgreSQL occurred to be the best.

A graph database is a type of NoSQL database that uses graph structure for semantic queries with nodes, edges, and properties to represent and store data. It has been applied in many fields, such as education, health, business, and social network, with many famous applications such as Google, Facebook, and eBay. One of the main advantages of the graph database is its effective performance in data queries. This paper presents a comprehensive comparison of the performance based on the execution time of a NoSQL graph database named Neo4J with a popular relational database system, MySQL, which is used as the underlying technology in developing a software system. Query types are categorized into four groups: selection/ search, recursion, aggregation, and pattern matching. We examined representative questions for each group and executed them on Neo4j and MySQL using a real-life dataset named Career Village. The results show that Neo4j’s data query performance is better than MySQL in most results.

M2M (Machine-to-Machine) technologies provide a number of solutions in logistics and transport, such as fleet management solutions, asset tracking systems, parking space management and payment, road tolls, traffic volume monitoring, connected road signs, traffic lights and cameras, connected navigation, passenger services, etc. The data obtained is huge and need high performance to be usable. NoSQL data systems are the answer to it. When compared to relational databases, many NoSQL systems share several key characteristics including a more flexible data model, higher scalability, and a superior performance. Although there are dozens of NoSQL databases, they primarily fall into one of the following three categories: document databases, graph databases and databases based on key-value and wide column model. MongoDB is an open-source document database. In this paper the possibilities of using MongoDB databases in innovative applications of Big Data in the transportation industry will be explored.

In contrast to heavy-handed ER-style data models in relational databases, knowledge graphs (or graph databases) capture entity semantics in terms of entity relationships and properties following a simple collect-as-you-go model. While this allows for a more flexible and dynamically adaptable knowledge representation, it comes at the price of more complex querying: with varying degrees of information sparsity, it will gradually become more difficult to figure out what an entity actually represents. Thus, matching the intended schema as specified by a query against actually occurring entity patterns in the graph database needs severe attention on a conceptual level. In this article, we analyze graph patterns as schema information from a graph pattern matching perspective. We argue that every query consists of a mixture of conceptual information (how entities are structured) together with evaluation information (further dependencies and constraints on data) and that this mixture is not always easy to divide. To arrive at truly schema-aware graph query processing, we propose several matching mechanisms, each mandating a specific semantic meaning of the graph pattern, and discuss their practical applicability.