Akademik Verilerin Neo4j Çizge Veritabanı ile Modellenmesi

Öz

Her geçen gün gelişen teknoloji ile, üretilen veri miktarı devasa boyutlara ulaşmaktadır. Veri, diğer verilerle olan ilişkisiyle birlikte en güçlü ve anlamlı bilgiyi oluşturur. Veriler arasındaki ilişkinin verinin kendisinden daha önemli olması dikkat çeken noktalardan biridir. Bu ilişkiler, İlişkisel veritabanları tarafından verimli bir şekilde ele alınır. Ancak büyük miktarda veriyi ilişkileriyle birlikte ele almak sıkıcı bir süreçtir. Çizge Veritabanları, İlişkisel veritabanlarındaki varlıklar ve ilişkileri, çizge veritabanlarındaki düğümler ve ilişkiler ile daha basite indirgemektedir. Bu makale, Türkiye’deki üniversiteler ve akademisyenler hakkında kapsamlı bir veri görselleştirme aracı geliştirmek için Neo4j graf veritabanının kullanımını ele almaktadır. Yükseköğretim Kurulu (YÖK) tarafından sağlanan veriler kullanılarak, üniversiteler, akademik birimler ve akademisyenler arasındaki ilişkiler görselleştirilmiştir. Çalışma, akademik ağların daha iyi anlaşılmasını sağlamayı ve araştırmacılar, eğitim yöneticileri ve politika yapıcılar için değerli içgörüler sunmayı amaçlamaktadır. Uygulamanın geliştirilme süreci, veri toplama yöntemleri, kullanılan teknik araçlar ve analiz yöntemleri detaylı olarak sunulmaktadır. Elde edilen bulgular, Türkiye’deki akademik yapının ve araştırma işbirliklerinin daha etkili bir şekilde analiz edilmesine olanak tanımaktadır. Sonuç olarak, bu çalışma Neo4j’nin eğitim ve araştırma verilerinin görselleştirilmesi için güçlü bir araç olduğunu ortaya koymaktadır.

Anahtar Kelimeler

• Neo4j
• Çizge Modelleme
• Akademik ağlar
• Çizge ağlar
• Çizge veritabanı

Modeling Academic Data with Neo4j Graph Database

Öz

The amount of data generated by the technology that is evolving every day is reaching gigantic pro- portions. Data, together with its relationship to other data, forms the most powerful and meaningful information. One of the remarkable points is that the relationship between data is more important than the data itself. These relationships are handled efficiently by relational databases. However, handling large amounts of data with their relationships is a tedious process. Graph databases simplify the entities and relationships of relational databases with the nodes and relationships of graph databases. This study deals with the use of Neo4j graph database in order to make a comprehensive data visualisation about existing universities and academics in Turkey. With the data obtained from the Council of Higher Education (YÖK), 2191 nodes and 2124 relationships between nodes are created and the relationships between universities, academic units and academics are visualised. The study aims to provide a better understanding of academic networks. In the study, the development process of the application, data collection methods, technical tools used and analysis methods are presented in detail. The findings allow for a more effective analysis and visual modelling of the academic structure in Turkey. In conclusion, this study demonstrates that Neo4j is a powerful tool for visualising educational and research data.

Anahtar Kelimeler

• Neo4j
• Graph networks
• Graph modeling
• Graph database
• Academic networks.

Kaynakça

1. Sagiroglu S, Sinanc D, Big data: A review, 2013 International Conference on Collaboration Technologies and Systems (CTS), 2013; 42-47. doi: 10.1109/CTS.2013.6567202.
2. Kumar P, Huang HH, Graphone: A data store for real-time analytics on evolving graphs, ACM Transactions on Storage (TOS), 2020; 15(4): 1-40.
3. Besta M, Gerstenberger R, Peter E, Fischer M, Podstawski M, Barthels C, Alonso G, Hoefler T, Demystifying graph databases: Analysis and taxonomy of data organization, system designs, and graph queries, ACM Computing Surveys, 2023; 56(2): 1-40.
4. Turgay S, Eren K, Graph database for agent based emergency response model, The 2014 International Conference on Advances in Big Data Analytics (ABDA) & ICOMP, 2014.
5. Miller JJ, Graph database applications and concepts with Neo4j, Proceedings of the Southern Association for Information Systems Conference, 2013; 2324(36): 141-147.
6. Afandi MI, Wahyuni ED, University research graph database for efficient multi-perspective data analysis using Neo4j, 2020 6th Information Technology International Seminar (ITIS), 2020; 286-290.
7. Bürhan Y, Daş R, Akademik veritabanlarından yazar-makale bağlantı tahmini, Politeknik Dergisi, 2017; 20(4): 787-800.
8. Hodler AE, Needham M, Graph data science using Neo4j, in: Massive Graph Analytics, Chapman and Hall/CRC, 2022; 433-457.

9. İnce K, Karcı A, Akademik işbirliklerinin yeni bir çizge olarak modellenmesi ve istatistiki analizi, Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 2019; 34(1): 439-460.
10. Soylu M, Soylu A, Daş R, A new approach to recognizing the use of attitude markers by authors of academic journal articles, Expert Systems with Applications, 2023; 230: 120538. doi: 10.1016/j.eswa.2023.120538.
11. Sholeh M, Rachmawati RR Y, Susanti E, Pemodelan basis data graph dengan Neo4j (Studi Kasus: Basis Data Sistem Informasi Penjualan pada UMKM), Jurnal Teknologi Informasi dan Terapan, 2020; 7(1): 25-32. doi: 10.25047/jtit.v7i1.129.
12. Aung TT, Nyunt TTS, Community detection in scientific co-authorship networks using Neo4j, 2020 IEEE Conference on Computer Applications (ICCA), 2020; 1-6. doi: 10.1109/ICCA49400.2020.9022826.
13. Xu Z, Xu T, Zhang F, Construction of chinese sports knowledge graph based on Neo4j, 2020 IEEE 2nd International Conference on Civil Aviation Safety and Information Technology (ICCASIT), 2020; 561-564. doi: 10.1109/ICCASIT50869.2020.9368851.
14. Liu P, Huang Y, Wang P, Zhao Q, Nie J, Tang Y, Sun L, Wang H, Wu X, Li W, Construction of typhoon disaster knowledge graph based on graph database Neo4j, 2020 Chinese Control And Decision Conference (CCDC), 2020; 3612-3616. doi: 10.1109/CCDC49329.2020.9164384.
15. Değerli A, Ağ toplumu yaklaşımı ile akademik bir sosyal ağ modeli için graf veri tabanı önerisi, Beykoz Akademi Dergisi, 2021; 9(1): 68-88. doi: 10.14514/BYK.m.26515393.2021.9/1.68-88.
16. Vágner A, Route planning on GTFS using Neo4j, Annales Mathematicae et Informaticae, 2021; 54: 163-179. doi: 10.33039/ami.2021.07.001.
17. Kim DH, Im HS, Hyeon JH, Jwa JW, Development of the rule-based smart tourism chatbot using Neo4J graph database, International Journal of Internet, Broadcasting and Communication, 2021; 13(2): 179–186.
18. Tuck D, A cancer graph: a lung cancer property graph database in Neo4j, BMC Research Notes, 2022; 15(1): 45. doi: 10.1186/s13104-022-05912-9.
19. Sülü M, Daş R, Graph visualization of cyber threat intelligence data for analysis of cyber attacks, Balkan Journal of Electrical and Computer Engineering, 2022; 10(3): 300-306. doi: 10.17694/bajece.1090145.
20. Monteiro J, Sá F, Bernardino J, Experimental evaluation of graph databases: JanusGraph, Nebula Graph, Neo4j, and TigerGraph, Applied Sciences, 2023; 13(9): 5770. doi: 10.3390/app13095770.
21. Pelofske E, Liebrock LM, Urias V, Cybersecurity threat hunting and vulnerability analysis using a Neo4j graph database of open source intelligence, 2023; doi: 10.48550/arXiv.2301.12013.
22. Yuan D, Zhou K, Yang C, Architecture and application of traffic safety management knowledge graph based on Neo4j, Sustainability, 2023; 15(12): 9786. doi: 10.3390/su15129786.
23. Singh SP, Khan AA, Souissi R, Yusuf SA, Leveraging Neo4j and deep learning for traffic congestion simulation & optimization, 2023; doi: 10.48550/arXiv.2304.00192.
24. Kumar N, Mukhtar S, Building protein–protein interaction graph database using Neo4j, Protein-Protein Interactions: Methods and Protocols, Springer US, New York, NY, 2023; 469-479. doi: 10.1007/978-1-0716-3327-4-36.
25. Das R, Soylu M, A key review on graph data science: The power of graphs in scientific studies, Chemometrics and Intelligent Laboratory Systems, 2023; 240: 104896. doi: 10.1016/j.chemolab.2023.104896.
26. Gricourt G, Duigou T, Dérozier S, Faulon JL, neo4jsbml: import systems biology markup language data into the graph database Neo4j, PeerJ, 2024; 12: e16726. doi: 10.7717/peerj.16726.
27. Dermawan F, Kwang CH, Adijanto MD, Rakhmawati NA, Basara NR, Product recommendations through Neo4j by analyzing patterns in customer purchases, 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), 2024; 1-4. doi: 10.1109/ICETSIS61505.2024.10459357.
28. Tangalay Dalgın G, Daş R, Sinema verilerinin Neo4j çizge veritabanı ile modellenmesi ve analizi, DÜMF Mühendislik Dergisi, 2023. doi: 10.24012/dumf.
29. Cook J, Rehman S, Khan MA, Cryptanalysis of the SIMON cypher using Neo4j, 2024. doi: 10.48550/arXiv.2405.04735.
30. Değerli A, Ağ Toplumu bağlamında Bilginin paylaşımına yönelik akademik yayın ve referans sistemleri: Neo4j platformunda graf veritabanı uygulaması, PQDT - Global, 2014; 285. doi: 9798515213640.
31. Zhu Z, Zhou X, Shao K, A novel approach based on Neo4j for multi-constrained flexible job shop scheduling problem, Computers & Industrial Engineering, 2019; 130: 671-686. doi: 10.1016/j.cie.2019.03.022.
32. Yi N, Li C, Feng X, Shi M, Design and implementation of movie recommender system based on graph database, 2017 14th Web Information Systems and Applications Conference (WISA), 2017; 132-135. doi: 10.1109/WISA.2017.34.
33. Huang H, Dong Z, Research on architecture and query performance based on distributed graph database Neo4j, 2013 3rd International Conference on Consumer Electronics, Communications and Networks, 2013; 533-536. doi: 10.1109/CECNet.2013.6703379.
34. Zaniewicz N, Salamończyk A, Comparison of MongoDB, Neo4j and ArangoDB databases using the developed data generator for NoSQL databases, Studia Informatica. System and Information Technology, 2022; 26(1): 61-72.
35. Meiling L, Benchmarking Multi-model Databases with ArangoDB and OrientDB, Helsingfors universitet, 2017.
36. Fernandes D, Bernardino J, Graph databases comparison: AllegroGraph, ArangoDB, InfiniteGraph, Neo4J, and OrientDB, 7th International Conference on Data Science, Technology and Applications, Porto, Portugal, 2018, pp. 373-380. doi: 10.5220/0006910203730380.
37. Bebee BR, Choi D, Gupta A, Gutmans A, Khandelwal A, Kiran Y, Mallidi S, McGaughy B, Personick M, Rajan K, et al., Amazon Neptune: Graph data management in the cloud, ISWC (P&D/Industry/BlueSky), 2018.
38. Lopes A, Rodrigues D, Saraiva J, Abbasi M, Martins P, Wanzeller C, Scalability and performance evaluation of graph database systems: a comparative study of Neo4j, JanusGraph, Memgraph, NebulaGraph, and TigerGraph, 2023 Second International Conference On Smart Technologies For Smart Nation (SmartTechCon), 2023, ss. 537–542.
39. Sandell J, Asplund E, Ayele WY, Duneld M, Performance comparison analysis of ArangoDB, My-SQL, and Neo4j: an experimental study of querying connected data, 2024.