Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14365/3656
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Uzunbayır, Serhat | - |
dc.date.accessioned | 2023-06-16T15:01:53Z | - |
dc.date.available | 2023-06-16T15:01:53Z | - |
dc.date.issued | 2022 | - |
dc.identifier.isbn | 9.78167E+12 | - |
dc.identifier.uri | https://doi.org/10.1109/UBMK55850.2022.9919589 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14365/3656 | - |
dc.description | 7th International Conference on Computer Science and Engineering, UBMK 2022 -- 14 September 2022 through 16 September 2022 -- 183844 | en_US |
dc.description.abstract | 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. © 2022 IEEE. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.relation.ispartof | Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022 | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | big data | en_US |
dc.subject | document database | en_US |
dc.subject | graph database | en_US |
dc.subject | nosql | en_US |
dc.subject | relational databases | en_US |
dc.subject | Big data | en_US |
dc.subject | Digital storage | en_US |
dc.subject | Query languages | en_US |
dc.subject | Big data applications | en_US |
dc.subject | Document database | en_US |
dc.subject | Graph database | en_US |
dc.subject | Health care education | en_US |
dc.subject | MongoDB | en_US |
dc.subject | Non-Relational Databases | en_US |
dc.subject | Nosql | en_US |
dc.subject | Performance | en_US |
dc.subject | Relational Database | en_US |
dc.subject | Social media | en_US |
dc.subject | Graph Databases | en_US |
dc.title | Relational Database and NoSQL Inspections using MongoDB and Neo4j on a Big Data Application | en_US |
dc.type | Conference Object | en_US |
dc.identifier.doi | 10.1109/UBMK55850.2022.9919589 | - |
dc.identifier.scopus | 2-s2.0-85141885837 | en_US |
dc.authorscopusid | 57205586949 | - |
dc.identifier.startpage | 148 | en_US |
dc.identifier.endpage | 153 | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopusquality | N/A | - |
dc.identifier.wosquality | N/A | - |
item.grantfulltext | reserved | - |
item.openairetype | Conference Object | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | With Fulltext | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 05.04. Software Engineering | - |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
Files in This Item:
File | Size | Format | |
---|---|---|---|
2741.pdf Restricted Access | 548.26 kB | Adobe PDF | View/Open Request a copy |
CORE Recommender
SCOPUSTM
Citations
3
checked on Nov 20, 2024
Page view(s)
136
checked on Nov 18, 2024
Download(s)
4
checked on Nov 18, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.