Orclever Proceedings of Research and Development

Dijital Cüzdan: Güvenli ve Maliyet Etkin Bir Altyapı Yaklaşımı

Ferhat Musa Uysal

Turkcell Ödeme ve Elektronik Para Hizmetleri A.Ş. (Paycell Ar-Ge Merkezi)

https://orcid.org/0009-0009-1479-0967

DOI: https://doi.org/10.56038/oprd.v3i1.362

Keywords: Dijital Varlık Yönetimi, Dijital Cüzdan, Gerçek Zamanlı Veri Analizi, Finansal Teknolojiler

---

Abstract

Bu çalışma, dijital cüzdan için yenilikçi bir altyapı geliştirme hedefini taşımaktadır. Projede, hem kurumsal hem de bireysel kullanıcıların ihtiyaçlarına yönelik bir çözüm geliştirilmesi amaçlanmaktadır. Ana odak, dijital bir cüzdan geliştirerek dijital varlıkların yönetiminin ve saklanmasının kolaylaştırılması üzerinedir. Bu bağlamda, projenin temel hedefleri arasında, yüksek işlem ücretlerine alternatif olarak yerli çözümler sunmak, dijital cüzdan ile varlıkların yerli bulut sistemleri üzerinde güvenle saklanmasını sağlamak ve Paycell ile iş ortaklarının ihtiyaçlarını karşılayacak uygulama geliştirmek bulunmaktadır. Projede, kullanıcıların dijital varlık yönetim yazılımları üzerindeki etkileşimlerinin gerçek zamanlı olarak izlenmesi ve bu verilerin analizi yoluyla anlık eylemler üretmek de bu çalışmanın kapsamı içerisindedir. Böylece, dijital varlık işlemlerinin güvenliğini, verimliliğini ve maliyet etkinliğini artırmak ve genel olarak dijital cüzdan kullanımında kullanıcı deneyimini iyileştirmek amaçlanmaktadır. Sonuç olarak, bu proje, dijital varlık yönetimi ve saklama alanında, güvenlik, kullanılabilirlik ve maliyet etkinliği gibi önemli avantajlar sunmayı hedefleyen, kapsamlı bir çözüm sunmayı amaçlamaktadır. Bu girişimin, dijital cüzdan ekosisteminin genişlemesine ve kullanıcıların dijital varlık yönetimindeki deneyimlerine önemli katkılar sağlaması beklenmektedir.

---

References

Aktaş, M.S. (2018). Hybrid cloud computing monitoring software architecture, Concurrency and Computation – Practice and Experience, Vol.:30, Issue:21, Nov. 2018. DOI: https://doi.org/10.1002/cpe.4694

Baeth, M. J., & Aktaş, M.S. (2019). An approach to custom privacy policy violation detection problems using big social provenance data, Concurrency and Computation – Practice and Experience, Vol.:30, Issue: 21, Nov. 2018. DOI: https://doi.org/10.1002/cpe.4690

Aktaş, M.S., & Astekin, M. (2019). Provenance aware run-time verification of things for self-healing Internet of Things applications, DOI: 10.1002/cpe.4263, Published Online, Concurrency Computat: Pract Exper., Jan. 2019.

Aktaş, M.S. (2019). Detecting Complex Events With Real Time Monitoring Infrastructure On Event-Based Systems, Pamukkale Univ Muh Bilim Derg. 2019; 25(2): 199-207 | DOI: 10.5505/pajes.2018.28044, 2019.

Yildiz, B.. "Optimizing bitmap index encoding for high performance queries." Concurrency and Computation: Practice and Experience 33, no. 18 (2021): e5943. https://doi.org/10.1002/cpe.5943 DOI: https://doi.org/10.1002/cpe.5943

Yildiz B., Wu K., Byna, S. and Shoshani, A. “Parallel membership queries on very large scientific data sets using bitmap indexes,” Concurrency and Computation: Practice and Experience, 31(15), e5157, 2019. DOI: 10.1002/cpe.5157 DOI: https://doi.org/10.1002/cpe.5157

Yildiz B. and Fox, G.C. “Toward a modular and efficient distribution for web service handlers,” Concurrency and Computation: Practice and Experience, 25(2), pp. 410-426, 2013. DOI: 10.1002/cpe.2854. DOI: https://doi.org/10.1002/cpe.2854

Yildiz, B., 2022, September. Enhancing Image Resolution with Generative Adversarial Networks. In 2022 7th International Conference on Computer Science and Engineering (UBMK) (pp. 104-109). IEEE. DOI: https://doi.org/10.1109/UBMK55850.2022.9919520

Saad, A.M.S.E. and Yildiz, B., 2022, September. Reinforcement Learning for Intrusion Detection. In International Conference on Computing, Intelligence and Data Analytics (pp. 230-243). Cham: Springer International Publishing. DOI: https://doi.org/10.1007/978-3-031-27099-4_18

Aktas, M.S., Detecting Complex Events With Real Time Monitoring Infrastructure On Event-Based Systems, Pamukkale Univ Muh Bilim Derg. 2019; 25(2): 199-207 | DOI: 10.5505/pajes.2018.28044, 2019. DOI: https://doi.org/10.5505/pajes.2018.28044

Yildiz, B., "Reinforcement learning using fully connected, attention, and transformer models in knapsack problem solving." Concurrency and Computation: Practice and Experience 34, no. 9 (2022): e6509. DOI: 10.1002/cpe.6509 DOI: https://doi.org/10.1002/cpe.6509

Yildiz, B. and Tezgider M. “Improving word embedding quality with innovative automated approaches to hyperparameters,” Concurrency and Computation: Practice and Experience, 33(18), e6091, 2021. DOI: 10.1002/cpe.6091. DOI: https://doi.org/10.1002/cpe.6091

Aktas, M.S., et al. "Information services for dynamically assembled semantic grids", The First International Conference on Semantics Knowledge and Grid (SKG 2005) Beijing China, 2005. DOI: https://doi.org/10.1109/SKG.2005.83

Aktas, M.S. et al., "Information services for grid/web service oriented architecture (soa) based geospatial applications", The First International Conference on Semantics Knowledge and Grid (SKG 2005) Beijing China, 2005

Aktas, M.S., Fox, G.C., Pierce, M., Managing dynamic metadata as context, The 2005 Istanbul International Computational Science and Engineering Conference (ICCSE2005), Istanbul, Turkey, 2005.

Aktas, M.S., et al., Implementing geographical information system grid services to support computational geophysics in a service-oriented environment. NASAEarth-Sun System Technology Conference, University of Maryland, Adelphi, Maryland, 2005.

Baloglu, A., Aktas, M. S., BlogMiner: Web blog mining application for classification of movie reviews, 2010 Fifth International Conference on Internet and Web Applications and Services, 2010. DOI: https://doi.org/10.1109/ICIW.2010.19

Uygun, Y., et al., On the Large-scale Graph Data Processing for User Interface Testing in Big Data Science Projects, 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA, 2020, pp. 2049-2056, doi: 10.1109/BigData50022.2020.9378153. DOI: https://doi.org/10.1109/BigData50022.2020.9378153

Olmezogullari, E.; Aktas, M. S., Pattern2Vec: Representation of clickstream data sequences for learning user navigational behavior. Concurrency and Computation: Practice and Experience 34 (9), 2022. DOI: https://doi.org/10.1002/cpe.6546

Olmezogullari, E.; Aktas, M. S., Representation of Click-Stream DataSequences for Learning User Navigational Behavior by Using Embeddings. 2020 IEEE International Conference on Big Data (Big Data), 3173-3179, 2020. DOI: https://doi.org/10.1109/BigData50022.2020.9378437

Sahinoglu, M. et al., Mobile Application Verification: A Systematic Mapping Study. In: , et al. Computational Science and Its Applications – ICCSA 2015. ICCSA 2015. Lecture Notes in Computer Science, vol 9159. Springer, Cham. https://doi.org/10.1007/978-3-319-21413-9 11 DOI: https://doi.org/10.1007/978-3-319-21413-9_11

Kapdan, M. et al., On the Structural Code Clone Detection Problem: A Survey and Software Metric Based Approach. In: , et al. Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8583. Springer, Cham. https://doi.org/10.1007/978-3-319-09156-3 35. DOI: https://doi.org/10.1007/978-3-319-09156-3_35

Tufek, A., et al., Provenance Collection Platform for the Weather Research and Forecasting Model, 2018 14th International Conference on Semantics, Knowledge and Grids (SKG), Guangzhou, China, 2018, pp. 17-24, doi: 10.1109/SKG.2018.00009. DOI: https://doi.org/10.1109/SKG.2018.00009

Dundar, B. et al., A Big Data Processing Framework for Self-Healing Internet of Things Applications, 2016 12th International Conference on Semantics, Knowledge and Grids (SKG), Beijing, China, 2016, pp. 62-68, doi: 10.1109/SKG.2016.017. DOI: https://doi.org/10.1109/SKG.2016.017

Baeth, M. J. et al., Detecting Misinformation in Social Networks Using Provenance Data, 2017 13th International Conference on Semantics, Knowledge and Grids (SKG), Beijing, China, 2017, pp. 85-89, doi: 10.1109/SKG.2017.00022. DOI: https://doi.org/10.1109/SKG.2017.00022

Abeykoon, V., et al., "Streaming Machine Learning Algorithms with Big Data Systems," 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 2019, pp. 5661-5666, doi: 10.1109/BigData47090.2019.9006337. DOI: https://doi.org/10.1109/BigData47090.2019.9006337

Güner, N., et al., "Predicting academically at-risk engineering students: A soft computing application." Acta Polytechnica Hungarica 11, no. 5 (2014): 199-216. DOI: https://doi.org/10.12700/APH.11.05.2014.05.12

Abeykoon, V., et al. Stochastic gradient descent-based support vector machines training optimization on Big Data and HPC frameworks. Concurrency Computat Pract Exper. 2022; 34:e6292. https://doi.org/10.1002/cpe.6292 DOI: https://doi.org/10.1002/cpe.6292

Widanage, C., et al., "High performance data engineering everywhere." In 2020 IEEE International Conference on Smart Data Services (SMDS), pp. 122-132. IEEE, 2020. DOI: https://doi.org/10.1109/SMDS49396.2020.00022

Wickramasinghe, P., et al., "Twister2: TSet High-Performance Iterative Dataflow," 2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS), Shenzhen, China, 2019, pp. 55-60, doi: 10.1109/HPBDIS.2019.8735495. DOI: https://doi.org/10.1109/HPBDIS.2019.8735495

Kamburugamuve, S., et al., "Twister:Net - Communication Library for Big Data Processing in HPC and Cloud Environments," 2018 IEEE 11th International Conference on Cloud Computing (CLOUD), San Francisco, CA, USA, 2018, pp. 383-391, doi: 10.1109/CLOUD.2018.00055. DOI: https://doi.org/10.1109/CLOUD.2018.00055

Wickramasinghe, P., et al. High-performance iterative dataflow abstractions in Twister2:TSet. Concurrency Computat Pract Exper. 2022; 34:e5998. https://doi.org/10.1002/cpe.5998. DOI: https://doi.org/10.1002/cpe.5998