Query Languages for Graph Databases

2019 ◽  
pp. 645-659
Author(s):  
Kornelije Rabuzin
Keyword(s):  
Relational Databases ◽  
Query Language ◽  
Main Idea ◽  
Query Languages ◽  
End Users ◽  
The Other ◽  
Graph Database ◽  
Graph Databases ◽  
Nosql Databases ◽  
The Past

In the past few years, many NoSQL databases have emerged, including graph databases. NoSQL databases have certain advantages and they can be used in certain domains as an alternative to relational databases. In order to use graph databases, one needs to be familiar with specific languages like Cypher Query Language (CQL) or Gremlin. However, some statements in CQL can be considered too complex for end users as it is shown later on. Because of that, the main idea of this chapter is to explore two other languages for graph databases. One of them is new and it is used to pose queries visually. Since CQL does not support recursion, views, etc., the other language is used to show how to use recursion and views on a graph database.

Query Languages for Graph Databases

2018 ◽  
pp. 2031-2042
Author(s):  
Kornelije Rabuzin
Keyword(s):  
Relational Databases ◽  
Query Language ◽  
Main Idea ◽  
Query Languages ◽  
End Users ◽  
The Other ◽  
Graph Database ◽  
Graph Databases ◽  
Nosql Databases ◽  
The Past

In the past few years many NoSQL databases have emerged, including graph databases. NoSQL databases have certain advantages and they can be used in certain domains as an alternative to relational databases. In order to use graph databases, one needs to be familiar with specific languages like Cypher Query Language (CQL) or Gremlin. However, some statements in CQL can be considered too complex for end users as it is shown later on. Because of that the main idea of this paper is to explore two other languages for graph databases. One of them is new and it is used to pose queries visually. Since CQL does not support recursion, views, etc., the other language is used to show how to use recursion and views on a graph database.

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.

scholarly journals Applying graph database technology for analyzing perturbed co-expression networks in cancer

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Claire M Simpson ◽  
Florian Gnad
Keyword(s):  
Relational Databases ◽  
Molecular Mechanisms ◽  
Biological Data ◽  
Database Management System ◽  
Graph Database ◽  
Graph Databases ◽  
Graph Representations ◽  
Rnaseq Data ◽  
Database Technology ◽  
Speed Accuracy

Abstract Graph representations provide an elegant solution to capture and analyze complex molecular mechanisms in the cell. Co-expression networks are undirected graph representations of transcriptional co-behavior indicating (co-)regulations, functional modules or even physical interactions between the corresponding gene products. The growing avalanche of available RNA sequencing (RNAseq) data fuels the construction of such networks, which are usually stored in relational databases like most other biological data. Inferring linkage by recursive multiple-join statements, however, is computationally expensive and complex to design in relational databases. In contrast, graph databases store and represent complex interconnected data as nodes, edges and properties, making it fast and intuitive to query and analyze relationships. While graph-based database technologies are on their way from a fringe domain to going mainstream, there are only a few studies reporting their application to biological data. We used the graph database management system Neo4j to store and analyze co-expression networks derived from RNAseq data from The Cancer Genome Atlas. Comparing co-expression in tumors versus healthy tissues in six cancer types revealed significant perturbation tracing back to erroneous or rewired gene regulation. Applying centrality, community detection and pathfinding graph algorithms uncovered the destruction or creation of central nodes, modules and relationships in co-expression networks of tumors. Given the speed, accuracy and straightforwardness of managing these densely connected networks, we conclude that graph databases are ready for entering the arena of biological data.

scholarly journals Exploiting NoSQL Graph Databases and in Memory Architectures for Extracting Graph Structural Data Summaries

2017 ◽  
Vol 25 (01) ◽  
pp. 81-109 ◽  
Author(s):  
Arnaud Castelltort ◽  
Anne Laurent
Keyword(s):  
Relational Databases ◽  
Structural Data ◽  
Scientific Data ◽  
Graph Database ◽  
Graph Databases ◽  
Fuzzy Operators ◽  
Data Volume ◽  
Novel Method ◽  
Memory Architectures ◽  
Data Summaries

NoSQL graph databases have been introduced in recent years for dealing with large collections of graph-based data. Scientific data and social networks are among the best examples of the dramatic increase of the use of such structures. NoSQL repositories allow the management of large amounts of data in order to store and query them. Such data are not structured with a predefined schema as relational databases could be. They are rather composed by nodes and relationships of a certain type. For instance, a node can represent a Person and a relationship Friendship. Retrieving the structure of the graph database is thus of great help to users, for example when they must know how to query the data or to identify relevant data sources for recommender systems. For this reason, this paper introduces methods to retrieve structural summaries. Such structural summaries are extracted at different levels of information from the NoSQL graph database. The expression of the mining queries is facilitated by the use of two frame-works: Fuzzy4S allowing to define fuzzy operators and operations with Scala; Cypherf allowing the use of fuzzy operators and operations in the declarative queries over NoSQL graph databases. We show that extracting such summaries can be impossible with the NoSQL query engines because of the data volume and the complexity of the task of automatic knowledge extraction. A novel method based on in memory architectures is thus introduced. This paper provides the definitions of the summaries with the methods to automatically extract them from NoSQL graph databases only and with the help of in-memory architectures. The benefit of our proposition is demonstrated by experimental results.

scholarly journals Automated Generation of Personal Data Reports from Relational Databases

2011 ◽  
Vol 10 (02) ◽  
pp. 193-208 ◽  
Author(s):  
Georgios John Fakas ◽  
Ben Cawley ◽  
Zhi Cai
Keyword(s):  
Prior Knowledge ◽  
Data Protection ◽  
Relational Databases ◽  
Query Language ◽  
Personal Data ◽  
Query Languages ◽  
Hash Tables ◽  
Automated Generation ◽  
Novel Approach ◽  
Performance Results

This paper presents a novel approach for extracting personal data and automatically generating Personal Data Reports (PDRs) from relational databases. Such PDRs can be used among other purposes for compliance with Subject Access Requests of Data Protection Acts. Two methodologies with different usability characteristics are introduced: (1) the GDSBased Method and (2) the By Schema Browsing Method. The proposed methdologies combine the use of graphs and query languages for the construction of PDRs. The novelty of these methodologies is that they do not require any prior knowledge of either the database schema or of any query language by the users. An optimisation algorithm is proposed that employs Hash Tables and reuses already found data. We conducted several queries on two standard benchmark databases (i.e. TPC-H and Microsoft Northwind) and we present the performance results.

Query Languages in NoSQL Databases

2015 ◽  
pp. 415-437 ◽  
Author(s):  
Maristela Holanda ◽  
Jane Adriana Souza
Keyword(s):  
Query Language ◽  
Data Retrieval ◽  
Query Languages ◽  
Nosql Databases ◽  
Different Types ◽  
Application Codes

This chapter aims to investigate how NoSQL (Not Only SQL) databases provide query language and data retrieval mechanisms. Users attest to many advantages in using the NoSQL databases for specific applications, however, they also report that querying and retrieving data easily continues to be a problem. The NoSQL operations require that, during the project, the queries must be thought of as built-in application codes. The authors intend to contribute to the investigation of querying, considering different types of NoSQL databases.

Query Languages for Document Retrieval — An Experimental Analysis

1983 ◽  
Vol 27 (7) ◽  
pp. 652-652
Author(s):  
John Van Praag ◽  
David Gilfoil ◽  
J. Thomas Murray
Keyword(s):  
Query Language ◽  
Response Times ◽  
Document Retrieval ◽  
Query Languages ◽  
Learning Curves ◽  
Document Management ◽  
The Other ◽  
Flat File ◽  
Regular User ◽  
Preliminary Model

As automation of office functions becomes more widespread and longstanding, large on-line databases of documents will commonly be accessed by personnel unschooled in database logic. It is necessary, therefore, to design query languages for document retrieval that are intuitive, simple, and fast. It is likely that effective query languages will be those that systematically provide the user with powerful memory cues, with highly interactive structures guiding the formation of queries, and with a very simple and learnable syntax. No one query language will suit every potential user; a means of optimizing the design of query languages, therefore, is to fit each language to the requirements of a specific population of users. At Exxon Office Systems we have, in the past year, designed two query languages, one aimed at occasional users, the other at regular users. These languages are attempts to provide convenient document management facilities of the kind alluded to above. A study was implemented to test the learnability and usability of these languages, using flat file search as a baseline. Sessions involved retrieval by subjects of documents from large (500 member) artificially generated databases of documents; subjects were run over multiple sessions. The raw data consisted of full keystroke capture of each session. By replacing the database mid-experiment, effects of acquisition of the query languages were separated from effects of memorization of the database. Learnability and usability were evaluated by statistical analysis of learning curves, command usage, response times, and individual differences. Both query methods appeared far preferable to flat file search, especially as users gained facility with them. One method was more easy to use but less powerful–and appeared to be appropriate for casual users; the other method was slightly harder to use, but provides the regular user with a more flexible and powerful tool for document management. Another outcome of the study was a preliminary model of the document management process–a model whose parameters depend on features of the query language, the database, and the population of users. By observing how features of a query language affect the parameters of the model it should be possible to refine the language so as to improve its effectiveness for a given population of users.

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

scholarly journals Improving Performance using Relational and Graph Database

2019 ◽  
Vol 8 (2) ◽  
pp. 1722-1726
Keyword(s):  
Relational Databases ◽  
Structured Data ◽  
Graph Database ◽  
Graph Databases ◽  
Enterprise Information ◽  
Relational Schema ◽  
Promising Alternative ◽  
Enterprise Information Systems ◽  
Nosql Database ◽  
Efficient Performance

Paper Relational database model (also called SQL databases) are one of the prevalent databases that are used with structured data. Currently news demands are arising owing to the magnitude with which the internet and social networks are getting used which brought importance to graph-structured data. Graph database (a nosql database) deal more naturally with highly connected data and are thus becoming popular and efficient choice. Due to limitations faced by relational databases in handling relationships (highly connected data), enterprise information systems find graph database as a promising alternative. According to the form of queries and property of data both relational and graph databases have vitality and flaws. Since most of the data is available in relational schema in this context, the conversion of an application from a relational to a graph format is very beneficial. Thus, this paper develops a dual database system through migration, which unifies the strengths of both relational databases and graph databases. Experimental results have shown that, this hybrid system has efficient performance.

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