scholarly journals DB2SNA: An All-in-One Tool for Extraction and Aggregation of Underlying Social Networks from Relational Databases

2012 ◽  
pp. 521-545 ◽  
Author(s):  
Rania Soussi ◽  
Etienne Cuvelier ◽  
Marie-Aude Aufaure ◽  
Amine Louati ◽  
Yves Lechevallier
Keyword(s):  
Social Networks ◽  
Relational Databases

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 A Knowledge Graph Approach for the Detection of Digital Human Profiles in Big Data

2021 ◽  
Vol 19 (6.2) ◽  
pp. 6
Author(s):  
Cu Kim Long ◽  
Ha Quoc Trung ◽  
Tran Ngoc Thang ◽  
Nguyen Tien Dong ◽  
Pham Van Hai
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Real Time ◽  
Relational Databases ◽  
Large Data ◽  
Large Datasets ◽  
Knowledge Graph ◽  
Graph Databases ◽  
Proposed Model ◽  
Digital Human

Digital transformation is a long process that changes the managing human profiles in both offline and online approaches. This generates the amount of huge data stored in both relational databases and many others like social networks or graph databases. To exploit effectively big data, several measures and algorithms in Picture Fuzzy Graph (PFG) are applied to solve many complex problems in the real-world problems. The paper has presented a novel approach using a knowledge graph to find a human profile including the detection of humans in large data. In the proposed model, digital human profiles are collected from conventional databases combination with social networks in real-time, and a knowledge graph is created to represent complex-relational user attributes of human profile in large datasets. PFG is applied to quantify the degree centrality of nodes. Furthermore, techniques and algorithms on the graph are used to classify the nodes. The experiments in the knowledge graph implemented to illustrate the proposed model. The main contribution in this paper is to identify the right persons among complex-relational groups, locations in real-time based on large datasets on the social networks, relational databases and graph databases.

scholarly journals APPLICATION OF NON-RELATIONAL DATABASES IN THE SOCIAL NETWORKS

2019 ◽  
Vol 10 (4) ◽  
pp. 56-72
Author(s):  
Miroslav Dechev ◽  
Miroslav Galabov ◽  
Tsvetanka Georgieva-Trifonova
Keyword(s):  
Social Networks ◽  
Relational Databases ◽  
The Social

Comparative Study Between the MySQL Relational Database and the MongoDB NoSQL Database

2021 ◽  
Vol 13 (3) ◽  
pp. 38-63
Author(s):  
Houcine Matallah ◽  
Ghalem Belalem ◽  
Karim Bouamrane
Keyword(s):  
Social Networks ◽  
Cloud Computing ◽  
Comparative Study ◽  
Electronic Commerce ◽  
Relational Database ◽  
Relational Databases ◽  
Database Management System ◽  
Nosql Databases ◽  
Nosql Database ◽  
Change Of Scale

NoSQL databases are new architectures developed to remedy the various weaknesses that have affected relational databases in highly distributed systems such as cloud computing, social networks, electronic commerce. Several companies loyal to traditional relational SQL databases for several decades seek to switch to the new “NoSQL” databases to meet the new requirements related to the change of scale in data volumetry, the load increases, the diversity of types of data handled, and geographic distribution. This paper develops a comparative study in which the authors will evaluate the performance of two databases very widespread in the field: MySQL as a relational database and MongoDB as a NoSQL database. To accomplish this confrontation, this research uses the Yahoo! Cloud Serving Benchmark (YCSB). This contribution is to provide some answers to choose the appropriate database management system for the type of data used and the type of processing performed on that data.

Multilayer Social Networks

2016 ◽  
Author(s):  
Mark E. Dickison ◽  
Matteo Magnani ◽  
Luca Rossi
Keyword(s):  
Social Networks ◽  
Multilayer Social Networks

Hostility and Perceptions of Support in Ambiguous Social Interactions

2006 ◽  
Vol 27 (2) ◽  
pp. 108-115 ◽  
Author(s):  
Ana-Maria Vranceanu ◽  
Linda C. Gallo ◽  
Laura M. Bogart
Keyword(s):  
Social Support ◽  
Social Networks ◽  
Information Processing ◽  
Social Interactions ◽  
Social Information Processing ◽  
Perceived Social Support ◽  
Social Information ◽  
Inverse Association ◽  
Processing Bias ◽  
Information Processing Bias

The present study investigated whether a social information processing bias contributes to the inverse association between trait hostility and perceived social support. A sample of 104 undergraduates (50 men) completed a measure of hostility and rated videotaped interactions in which a speaker disclosed a problem while a listener reacted ambiguously. Results showed that hostile persons rated listeners as less friendly and socially supportive across six conversations, although the nature of the hostility effect varied by sex, target rated, and manner in which support was assessed. Hostility and target interactively impacted ratings of support and affiliation only for men. At least in part, a social information processing bias could contribute to hostile persons' perceptions of their social networks.

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