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 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.

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.

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.

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 Modelling of Graph Databases

2017 ◽  
Vol 1 (1) ◽  
pp. 04 ◽  
Author(s):  
Jaroslav Pokorny
Keyword(s):  
Relational Databases ◽  
Original Work ◽  
Integrity Constraints ◽  
Graph Database ◽  
Graph Databases ◽  
Conceptual Level ◽  
Functional Dependencies ◽  
Creative Commons ◽  
Er Model ◽  
Open Access Article

Comparing graph databases with traditional,e.g., relational databases, some important database features are often missing there. Particularly, a graph database schema including integrity constraints is mostly not explicitly defined, also a conceptual modelling is not used. It is hard to check a consistency of the graph database, because almost no integrity constraints are defined or only their very simple representatives can be specified. In the paper, we discuss these issues and present current possibilities and challenges in graph database modelling. We focus also on integrity constraints modelling and propose functional dependencies between entity types, which reminds modelling functional dependencies known from relational databases. We show a number of examples of often cited GDBMSs and their approach to database schemas and ICs specification. Also a conceptual level of a graph database design is considered. We propose a sufficient conceptual model based on a binary variant of the ER model and show its relationship to a graph database model, i.e. a mapping conceptual schemas to database schemas. An alternative based on the conceptual functions called attributes is presented.  This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

scholarly journals Using Graph Databases

2020 ◽  
Vol 245 ◽  
pp. 04004
Author(s):  
Julius Hřivnáč
Keyword(s):  
Relational Databases ◽  
High Energy Physics ◽  
High Energy ◽  
Complex Data ◽  
Graph Database ◽  
Graph Databases ◽  
Basic Principles ◽  
Atlas Experiment ◽  
Internal Schema ◽  
Energy Physics

Data in High Energy Physics (HEP) usually consist of complext complex data structures stored in relational databases and files with internal schema. Such architecture exhibits many shortcomings, which could be fixed by migrating into Graph Database storage. The paper describes basic principles of the Graph Database together with an overview of existing standards and implementations. The usefulness and usability are demonstrated using the concrete example of the Event Index of the ATLAS experiment at LHC in two approaches as the full storage (all data are in the Graph Database) and meta-storage (a layer of schema-less graph-like data implemented on top of more traditional storage). The usability, the interfaces with the surrounding framework and the performance of those solutions are discussed. The possible more general usefulness for generic experiments’ storage is also discussed.

scholarly journals Advantages of using graph databases to explore chromatin conformation capture experiments

2021 ◽  
Vol 22 (S2) ◽  
Author(s):  
Daniele D’Agostino ◽  
Pietro Liò ◽  
Marco Aldinucci ◽  
Ivan Merelli
Keyword(s):  
Web Application ◽  
High Throughput Sequencing ◽  
Cell Types ◽  
Graph Database ◽  
Graph Databases ◽  
Sources Of Information ◽  
Chromosome Conformation ◽  
Wide Scale ◽  
User Friendly ◽  
Different Cell Types

Abstract Background High-throughput sequencing Chromosome Conformation Capture (Hi-C) allows the study of DNA interactions and 3D chromosome folding at the genome-wide scale. Usually, these data are represented as matrices describing the binary contacts among the different chromosome regions. On the other hand, a graph-based representation can be advantageous to describe the complex topology achieved by the DNA in the nucleus of eukaryotic cells. Methods Here we discuss the use of a graph database for storing and analysing data achieved by performing Hi-C experiments. The main issue is the size of the produced data and, working with a graph-based representation, the consequent necessity of adequately managing a large number of edges (contacts) connecting nodes (genes), which represents the sources of information. For this, currently available graph visualisation tools and libraries fall short with Hi-C data. The use of graph databases, instead, supports both the analysis and the visualisation of the spatial pattern present in Hi-C data, in particular for comparing different experiments or for re-mapping omics data in a space-aware context efficiently. In particular, the possibility of describing graphs through statistical indicators and, even more, the capability of correlating them through statistical distributions allows highlighting similarities and differences among different Hi-C experiments, in different cell conditions or different cell types. Results These concepts have been implemented in NeoHiC, an open-source and user-friendly web application for the progressive visualisation and analysis of Hi-C networks based on the use of the Neo4j graph database (version 3.5). Conclusion With the accumulation of more experiments, the tool will provide invaluable support to compare neighbours of genes across experiments and conditions, helping in highlighting changes in functional domains and identifying new co-organised genomic compartments.

scholarly journals Antiviral Resistance against Viral Mutation: Praxis and Policy for SARS-CoV-2

2021 ◽  
Vol 9 (1) ◽  
pp. 81-89
Author(s):  
Robert Penner
Keyword(s):  
Free Energy ◽  
Vaccine Development ◽  
A Priori ◽  
Structural Data ◽  
Medical Sciences ◽  
Spike Glycoprotein ◽  
Mutagenic Potential ◽  
Viral Mutation ◽  
Novel Method ◽  
Antiviral Targets

Abstract Tools developed by Moderna, BioNTech/Pfizer, and Oxford/Astrazeneca, among others, provide universal solutions to previously problematic aspects of drug or vaccine delivery, uptake and toxicity, portending new tools across the medical sciences. A novel method is presented based on estimating protein backbone free energy via geometry to predict effective antiviral targets, antigens and vaccine cargos that are resistant to viral mutation. This method is reviewed and reformulated in light of the recent proliferation of structural data on the SARS-CoV-2 spike glycoprotein and its mutations in multiple lineages. Key findings include: collections of mutagenic residues reoccur across strains, suggesting cooperative convergent evolution; most mutagenic residues do not participate in backbone hydrogen bonds; metastability of the glyco-protein limits the change of free energy through mutation thereby constraining selective pressure; and there are mRNA or virus-vector cargos targeting low free energy peptides proximal to conserved high free energy peptides providing specific recipes for vaccines with greater specificity than the full-spike approach. These results serve to limit peptides in the spike glycoprotein with high mutagenic potential and thereby provide a priori constraints on viral and attendant vaccine evolution. Scientific and regulatory challenges to nucleic acid therapeutic and vaccine development and deployment are finally discussed.

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