Graph Database Management Systems

2021 ◽  
pp. 778-790
Author(s):  
Kornelije Rabuzin ◽  
Martina Šestak
Keyword(s):  
Social Networks ◽  
Data Storage ◽  
Data Model ◽  
Influential Factors ◽  
Network Data ◽  
Future Trends ◽  
Graph Data ◽  
Relational Data Model ◽  
Storage Solutions ◽  
Insight Into

Nowadays, the increased amount and complexity of connected data stimulated by the appearance of social networks has shed a new light on the importance of managing such data, especially handling information about the connections. The most natural way of representing connected data is to represent them as nodes connected with relationships forming a graph. The idea of storing data as a set of nodes and edges comprising a graph was implemented in various forms in data models used in the past. The network data model, developed in late 1960s, can be considered as the first data model, which most accurately incorporated this idea. However, it was not long before the relational data model appeared, and took over the entire database market for years, which it dominates even nowadays. Therefore, the objective of this article is to give a timeline overview of developed graph data storage solutions in order to gain insight into past, present and future trends of GDBMSs. Additionally, the most influential factors and reasons for changes in trends in GDBMSs' usage will be analyzed.

scholarly journals Storing Hypergraph-Based Data Models in Non-hypergraph Data Storage and Applications for Information Systems

2021 ◽  
pp. 1-21
Author(s):  
Bálint Molnár ◽  
András Béleczki ◽  
Bence Sarkadi-Nagy
Keyword(s):  
Information Systems ◽  
Data Storage ◽  
Data Model ◽  
Relational Data ◽  
Complex Data ◽  
Graph Representations ◽  
Relational Data Model ◽  
Rich Information ◽  
The Relationship

Data structures and especially the relationship among the data entities have changed in the last couple of years. The network-like graph representations of data-model are becoming more and more common nowadays, since they are more suitable to depict these, than the well-established relational data-model. The graphs can describe large and complex networks — like social networks — but also capable of storing rich information about complex data. This was mostly of relational data-model trait before. This also can be achieved with the use of the knowledge representation tool called “hypergraphs”. To utilize the possibilities of this model, we need a practical way to store and process hypergraphs. In this paper, we propose a way by which we can store hypergraphs model in the SAP HANA in-memory database system which has a “Graph Core” engine besides the relational data model. Graph Core has many graph algorithms by default however it is not capable to store or to work with hypergraphs neither are any of these algorithms specifically tailored for hypergraphs either. Hence in this paper, besides the case study of the two information systems, we also propose pseudo-code level algorithms to accommodate hypergraph semantics to process our IS model.

scholarly journals Growth of relational model: Interdependence and complementary to big data

2021 ◽  
Vol 11 (2) ◽  
pp. 1780
Author(s):  
Sucharitha Shetty ◽  
B. Dinesh Rao ◽  
Srikanth Prabhu
Keyword(s):  
Data Storage ◽  
Data Model ◽  
Concurrency Control ◽  
Management System ◽  
Database Management ◽  
Data Retrieval ◽  
Database Management System ◽  
Relational Model ◽  
Complete Model ◽  
Relational Data Model

A database management system is a constant application of science that provides a platform for the creation, movement, and use of voluminous data. The area has witnessed a series of developments and technological advancements from its conventional structured database to the recent buzzword, bigdata. This paper aims to provide a complete model of a relational database that is still being widely used because of its well known ACID properties namely, atomicity, consistency, integrity and durability. Specifically, the objective of this paper is to highlight the adoption of relational model approaches by bigdata techniques. Towards addressing the reason for this in corporation, this paper qualitatively studied the advancements done over a while on the relational data model. First, the variations in the data storage layout are illustrated based on the needs of the application. Second, quick data retrieval techniques like indexing, query processing and concurrency control methods are revealed. The paper provides vital insights to appraise the efficiency of the structured database in the unstructured environment, particularly when both consistency and scalability become an issue in the working of the hybrid transactional and analytical database management system.

Design of a Data Model for Social Networks Applications

2009 ◽  
pp. 360-385
Author(s):  
Susanta Mitra ◽  
Aditya Bagchi ◽  
A. K. Bandyopadhyay
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Data Model ◽  
Generic Model ◽  
Social Community ◽  
Graph Data ◽  
Related Data ◽  
Network Applications ◽  
Object Relational ◽  
The Common

A social network defines the structure of a social community like an organization or institution, covering its members and their inter-relationships. Social relationships among the members of a community can be of different types like friendship, kinship, professional, academic etc. Traditionally, a social network is represented by a directed graph. Analysis of graph structure representing a social network is done by the sociologists to study a community. Hardly any effort has been made to design a data model to store and retrieve a social network related data. In this paper, an object-relational graph data model has been proposed for modeling a social network. The objective is to illustrate the power of this generic model to represent the common structural and node-based properties of different social network applications. A novel multi-paradigm architecture has been proposed to efficiently manage the system. New structural operators have been defined in the paper and the application of these operators has been illustrated through query examples. The completeness and the minimality of the operators have also been shown.

Software including the functions of automated analysis of electric power consumption in pipeline oil transportation

2011 ◽  
Vol 8 (1) ◽  
pp. 233-238
Author(s):  
R.M. Bogdanov ◽  
S.V. Lukin
Keyword(s):  
Power Consumption ◽  
Electric Power ◽  
Data Model ◽  
Automated Analysis ◽  
Petroleum Products ◽  
Electric Power Consumption ◽  
Scientific Center ◽  
Relational Data Model ◽  
Oil Transportation ◽  
Academy Of Sciences

Oil and petroleum products transportation is characterized by a significant cost of electric power. Correct oil and petroleum products accounting and forecasting requires knowledge of many factors. The software for norms of electric power consumption analysis for the planned period was developed at the Ufa Scientific Center of the Russian Academy of Sciences. Based on the principles of the relational data model, a schematic diagram/arrangement for the main oil transportation objects was developed, which allows to hold the initial data and calculated parameters in a structured manner.

scholarly journals The switching mechanisms of social network densification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Teruyoshi Kobayashi ◽  
Mathieu Génois
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Context ◽  
Social Interactions ◽  
Regime Switching ◽  
Face To Face ◽  
The Social ◽  
Switching Property ◽  
Distinctive Type ◽  
Insight Into

AbstractDensification and sparsification of social networks are attributed to two fundamental mechanisms: a change in the population in the system, and/or a change in the chances that people in the system are connected. In theory, each of these mechanisms generates a distinctive type of densification scaling, but in reality both types are generally mixed. Here, we develop a Bayesian statistical method to identify the extent to which each of these mechanisms is at play at a given point in time, taking the mixed densification scaling as input. We apply the method to networks of face-to-face interactions of individuals and reveal that the main mechanism that causes densification and sparsification occasionally switches, the frequency of which depending on the social context. The proposed method uncovers an inherent regime-switching property of network dynamics, which will provide a new insight into the mechanics behind evolving social interactions.

MapTap and Cornelia Slow Digital Art History and Formal Art Historical Social Network Research

2016 ◽  
Vol 79 (3) ◽  
pp. 315-330 ◽  
Author(s):  
Koenraad Brosens ◽  
Klara Alen ◽  
Astrid Slegten ◽  
Fred Truyen
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Early Modern ◽  
Data Model ◽  
Art History ◽  
Digital Art ◽  
Unique Data ◽  
Network Research ◽  
Custom Made

Abstract The essay introduces MapTap, a research project that zooms in on the ever-changing social networks underpinning Flemish tapestry (1620 – 1720). MapTap develops the young and still slightly amorphous field of Formal Art Historical Social Network Research (FAHSNR) and is fueled by Cornelia, a custom-made database. Cornelia’s unique data model allows researchers to organize attribution and relational data from a wide array of sources in such a way that the complex multiplex and multimode networks emerging from the data can be transformed into partial unimode networks that enable proper FAHSNR. A case study revealing the key roles played by women in the tapestry landscape shows how this kind of slow digital art history can further our understanding of early modern creative communities and industries.

scholarly journals Anopheles gambiae Genome Conservation as a Resource for Rational Gene Drive Target Site Selection

Insects ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 97
Author(s):  
Nace Kranjc ◽  
Andrea Crisanti ◽  
Tony Nolan ◽  
Federica Bernardini
Keyword(s):  
Anopheles Gambiae ◽  
Data Storage ◽  
Genomic Variation ◽  
Malaria Vectors ◽  
Anopheles Coluzzii ◽  
Structural Constraints ◽  
Gene Drive ◽  
Genetic Traits ◽  
A Genome ◽  
Insight Into

The increase in molecular tools for the genetic engineering of insect pests and disease vectors, such as Anopheles mosquitoes that transmit malaria, has led to an unprecedented investigation of the genomic landscape of these organisms. The understanding of genome variability in wild mosquito populations is of primary importance for vector control strategies. This is particularly the case for gene drive systems, which look to introduce genetic traits into a population by targeting specific genomic regions. Gene drive targets with functional or structural constraints are highly desirable as they are less likely to tolerate mutations that prevent targeting by the gene drive and consequent failure of the technology. In this study we describe a bioinformatic pipeline that allows the analysis of whole genome data for the identification of highly conserved regions that can point at potential functional or structural constraints. The analysis was conducted across the genomes of 22 insect species separated by more than hundred million years of evolution and includes the observed genomic variation within field caught samples of Anopheles gambiae and Anopheles coluzzii, the two most dominant malaria vectors. This study offers insight into the level of conservation at a genome-wide scale as well as at per base-pair resolution. The results of this analysis are gathered in a data storage system that allows for flexible extraction and bioinformatic manipulation. Furthermore, it represents a valuable resource that could provide insight into population structure and dynamics of the species in the complex and benefit the development and implementation of genetic strategies to tackle malaria.

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