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Sinema verilerinin Neo4j çizge veritabanı ile modellenmesi ve analizi

Year 2024, Volume: 15 Issue: 1, 1 - 13, 29.03.2024
https://doi.org/10.24012/dumf.1343167

Abstract

Günümüzde kurum veya kuruluşlar için veri, önemli bir varlık haline gelmiştir. İnternet ortamındaki hızlı gelişmeler ile sinema verileri de hızla büyümekte ve bu veriler arasındaki ilişki giderek daha karmaşık hale gelmektedir. Çizge veri tabanı mimarisi, elde bulunan verilerin model tasarımında varlıklar arasındaki ilişkiyi vurgulayan, verileri modellemek için etkili bir araçtır. Geleneksel veri tabanlarına göre daha karmaşık verileri depolamakta ve daha hızlı bir şekilde sunmaktadır. Bu çalışmada; sinema verileri ve karmaşıklaşan ilişkiler incelenerek neo4j çizge veri tabanı ile modellenmektedir. Bu sayede veriler arası ilişkiler daha kolay bir şekilde görüntülenmekte, sorgulanabilmekte ve daha kolay bir öğrenme imkanı sunmaktadır. Çalışma ile Neo4j çizge veri tabanı karmaşık ve aralarında birden fazla ilişki bulunan verileri modellemede oldukça başarılı sonuçlar sunduğu görülmüştür. Sinema verilerini depolamak ve yönetmek için Neo4j veri tabanını kullanmak, bir sinema web sitesi için kullanıcının gereksinimlerini ve ilgi alanlarını karşılamasını kolaylaştırdığı görülmektedir. Sinema adı, yılı, yönetmeni, oyuncuları, sinema filminin hasılatı, seyirci sayısı ve sinemanın türü gibi değişkenler dikkate alınarak çizge modellemesi ve analizi yapılarak başarılı sonuçlar elde edilmiştir.

References

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  • [4] A. Chen, A novel graph methodology for analyzing disease risk factor distribution using synthetic patient data. Healthcare Analytics, 2022, 2: 100084.
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  • [8] H. Lu, Z. Hong and M. Shi, "Analysis of film data based on Neo4j", 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS), pp. 675-677, 2017.
  • [9] Q. Shuai and C. Zhang, "Question Answering system based on Knowledge Graph of Film Culture", 2020 International Conference on Culture-oriented Science & Technology (ICCST), pp. 150-153, 2020.
  • [10] N. Yi, C. Li, X. Feng and M. Shi, "Design and implementation of movie recommender system based on graph database", 2017 14thWeb Information Systems and Applications Conference (WISA), pp. 132-135, 2017.
  • [11] M. Goyani and N. Chaurasiya, "A Review of Movie Recommendation System", ELCVIA: electronic letters on computer vision and image analysis, vol. 19, no. 3, pp. 18-37, 2020.
  • [12] C. K. Raghavendra and K. C. Srikantaiah, "Similarity Based Collaborative Filtering Model for Movie Recommendation Systems", 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 1143-1147, 2021.
  • [13] R. Das and M. Soylu, “A key review on graph data science: The power of graphs in scientific studies”, Chemometrics and Intelligent Laboratory Systems, c. 240, sy 104896, Haz. 2023
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  • [15] A. Silvescu, D. Caragea and A. Atramentov, "Graph Database", Artificial Intelligence Research Laboratory Department of Computer Science Iowa State University, 2012,
  • [16] M. A. Rodriguez and P. Neubauer. The graph traversal pattern. Graph Data Management: Techniques and Applications, 2011.
  • [17] C. Berge, The theory of graphs. Courier Corporation, 2001.
  • [18] G. M. D'silva, S. Thakare and V. A. Bharadi, "Real-time processing of IoT events using a Software as a Service (SaaS) architecture with graph database. In: Computing Communication Control and automation (ICCUBEA)", 2016 International Conference on, pp. 1-6, 2016.
  • [19] Y. Wang, "A Comparative Study of Graph Database NEO4J and Relational Database", Modem Electronic Technology, vol. 35, no. 20, pp. 77-79, 2012.
  • [20] L. Wang, "Construction of Document Resources Association Network Based on graph database technology", Digital Library Forum, pp. 59-65, 2014.
  • [21] M. Soylu, A. Soylu, ve R. Das, “A new approach to recognizing the use of attitude markers by authors of academic journal articles”, Expert Systems with Applications, c. 230, sy 120538, Kas. 2023
Year 2024, Volume: 15 Issue: 1, 1 - 13, 29.03.2024
https://doi.org/10.24012/dumf.1343167

Abstract

References

  • [1] S. Sencer and K. Eren, "Graph Database for Agent Based Emergency Response Model", Proceedings of the 2014 international conference on advances in big data analytics, July 21-24, 2014.
  • [2] M.Sülü, R. Daş, “Graph visualization of cyber threat intelligence data for analysis of cyber attacks”, Balkan Journal of Electrical and Computer Engineering (BAJECE), (2022),10(3), 300-306.
  • [3] Z. Zhu X.Zhou, K. Shao, A novel approach based on Neo4j for multi-constrained flexible job shop scheduling problem. Computers & Industrial Engineering, 2019, 130: 671-686.
  • [4] A. Chen, A novel graph methodology for analyzing disease risk factor distribution using synthetic patient data. Healthcare Analytics, 2022, 2: 100084.
  • [5] H. Hu, M. Fang, Y. Zhang, L. Jing and F. Hu, "Dynamic lightning protection method of electric power systems based on the large data characteristics", Int. J. Elect. Power Energy Syst., vol. 128, no. 1, pp. 1-14, 2021.
  • [6] Y. Shi, et al. A knowledge graph constructed for job-related crimes. Procedia Computer Science, 2022, 199: 540-547.
  • [7] M. Kuhn, E. T. Kaminski and J. Franke, "Track and Trace: Integrating static and dynamic data in a hybrid graph based traceability model", Procedia CIRP, vol. 112, pp. 250-255, 2022.
  • [8] H. Lu, Z. Hong and M. Shi, "Analysis of film data based on Neo4j", 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS), pp. 675-677, 2017.
  • [9] Q. Shuai and C. Zhang, "Question Answering system based on Knowledge Graph of Film Culture", 2020 International Conference on Culture-oriented Science & Technology (ICCST), pp. 150-153, 2020.
  • [10] N. Yi, C. Li, X. Feng and M. Shi, "Design and implementation of movie recommender system based on graph database", 2017 14thWeb Information Systems and Applications Conference (WISA), pp. 132-135, 2017.
  • [11] M. Goyani and N. Chaurasiya, "A Review of Movie Recommendation System", ELCVIA: electronic letters on computer vision and image analysis, vol. 19, no. 3, pp. 18-37, 2020.
  • [12] C. K. Raghavendra and K. C. Srikantaiah, "Similarity Based Collaborative Filtering Model for Movie Recommendation Systems", 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 1143-1147, 2021.
  • [13] R. Das and M. Soylu, “A key review on graph data science: The power of graphs in scientific studies”, Chemometrics and Intelligent Laboratory Systems, c. 240, sy 104896, Haz. 2023
  • [14] M. Rodriguez, A. Marko and P. Neubauer, "Constructions from dots and lines", Bulletin of the American Society for Information Science and Technology, vol. 36, no. 6, pp. 35-41, 2010.
  • [15] A. Silvescu, D. Caragea and A. Atramentov, "Graph Database", Artificial Intelligence Research Laboratory Department of Computer Science Iowa State University, 2012,
  • [16] M. A. Rodriguez and P. Neubauer. The graph traversal pattern. Graph Data Management: Techniques and Applications, 2011.
  • [17] C. Berge, The theory of graphs. Courier Corporation, 2001.
  • [18] G. M. D'silva, S. Thakare and V. A. Bharadi, "Real-time processing of IoT events using a Software as a Service (SaaS) architecture with graph database. In: Computing Communication Control and automation (ICCUBEA)", 2016 International Conference on, pp. 1-6, 2016.
  • [19] Y. Wang, "A Comparative Study of Graph Database NEO4J and Relational Database", Modem Electronic Technology, vol. 35, no. 20, pp. 77-79, 2012.
  • [20] L. Wang, "Construction of Document Resources Association Network Based on graph database technology", Digital Library Forum, pp. 59-65, 2014.
  • [21] M. Soylu, A. Soylu, ve R. Das, “A new approach to recognizing the use of attitude markers by authors of academic journal articles”, Expert Systems with Applications, c. 230, sy 120538, Kas. 2023
There are 21 citations in total.

Details

Primary Language Turkish
Subjects Software Engineering (Other)
Journal Section Articles
Authors

Gizem Tangalay Dalgın 0009-0003-0285-5360

Resul Daş 0000-0002-6113-4649

Early Pub Date March 29, 2024
Publication Date March 29, 2024
Submission Date August 18, 2023
Published in Issue Year 2024 Volume: 15 Issue: 1

Cite

IEEE G. Tangalay Dalgın and R. Daş, “Sinema verilerinin Neo4j çizge veritabanı ile modellenmesi ve analizi”, DUJE, vol. 15, no. 1, pp. 1–13, 2024, doi: 10.24012/dumf.1343167.
DUJE tarafından yayınlanan tüm makaleler, Creative Commons Atıf 4.0 Uluslararası Lisansı ile lisanslanmıştır. Bu, orijinal eser ve kaynağın uygun şekilde belirtilmesi koşuluyla, herkesin eseri kopyalamasına, yeniden dağıtmasına, yeniden düzenlemesine, iletmesine ve uyarlamasına izin verir. 24456