A Comparative Review Of Data Representation And Data Types In Modern Programming Languages

References

[1] Alaria, Satish Kumar, et al. “Comparative Study of Java and Python: A Review.” Industrial Engineering Journal, vol. 52, no. 04, 2023, pp. 2698–708. https:// -2708.

[2] Badaro, Gilbert, et al. “Transformers for Tabular Data Representation: A Survey of Models and Applications.” Transactions of the Association for Computational Linguistics, vol. 11, Mar. 2023, pp. 227–49. (Crossref), https://

[3] Batdalov, Ruslan, et al. “Extensible Model for Comparison of Expressiveness of Object- Oriented Programming Languages.” Applied Computer Systems, vol. 20, no. 1, Dec. 2016, pp. 27–35. https://

[4] Chavez, Brethel B., et al. “C Programming Language: A Review.” Journal of Universal Computer Science, vol. 7, no. 1, 2021.

[5] Durnová, Helena, and Gerard Alberts. “Was Algol 60 the First Algorithmic Language?” IEEE Annals of the History of Computing, vol. 36, no. 4, Oct. 2014, pp. 104–104. https://

[6] Franke, Björn, et al. “Collection Skeletons: Declarative Abstractions for Data Collections.” Proceedings of the 15th ACM SIGPLAN International Conference on Software Language Engineering [Auckland New Zealand], 2022, pp. 189–201. (Crossref), https://

[7] Gaonkar, Apeksha, et al. “A Comprehensive Survey on Multimodal Data Representation and Information Fusion Algorithms.” 2021 International Conference on Intelligent Technologies (CONIT), 2021. sci-hub.africa, https:// 98415.

[8] Haddaway, Neal R., et al. “PRISMA2020 : An R Package and Shiny App for Producing PRISMA 2020‐compliant Flow Diagrams, with Interactivity for Optimised Digital Transparency and Open Synthesis.” Campbell Systematic Reviews, vol. 18, no. 2, June 2022, p. e1230. https://

[9] Harris, Charles R., et al. “Array Programming with NumPy.” Nature, vol. 585, no. 7825, Sept. 2020, pp. 357–62. https://

[10] Jung, Ralf, et al. “RustBelt: Securing the Foundations of the Rust Programming Language.” Proceedings of the ACM on Programming Languages, vol. 2, no. POPL, Jan. 2018, pp. 1–34. https://

[11] Liu, Jiyuan, et al. “Multiview Subspace Clustering via Co-Training Robust Data Representation.” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 10, Oct. 2022, pp. 5177–89. (Crossref), https:// .

[12] Liu, Qian, et al. “ReUseData: An R/Bioconductor Tool for Reusable and Reproducible Genomic Data Management.” BMC Bioinformatics, vol. 25, no. 1, Jan. 2024, p. 8. https://

[13] Martini, Simone. “Several Types of Types in Programming Languages.” IFIP Advances in Information and Communication Technology, 2016. sci -hub.africa, https:// 7_15.

[14] Nugroho, Agung Yuliyanto, and Nur Hamid Sutanto. Exploring the Code Foundation: A Literature Review of Data Structures in C++. 2024.

[15] Page, Matthew J., et al. “The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews.” BMJ, Mar. 2021, p. n71. https://

[16] Sieczkowski, Filip, et al. “The Essence of Generalized Algebraic Data Types.” Proc. ACM Program. Lang., vol. 8, no. POPL, Jan. 2024, p. 24:695-24:723. ACM Digital Library, https://

[17] Singh, Vidhan, et al. “Introduction to the Basic Data Types in Programming.” Tuijin Jishu/Journal of Propulsion Technology, vol. 43, no. 4, Nov. 2023, pp. 246–49. (Crossref), https://

[18] Spidlen, Josef, et al. “Data File Standard for Flow Cytometry, Version FCS 3.2.” Cytometry Part A, vol. 99, no. 1, Jan. 2021, pp. 100–02. https://

[19] Sterling, Jonathan, and Robert Harper. “Guarded Computational Type Theory.” Proceedings of the 33rd Annual ACM/IEEE Symposium on Logic in Computer Science [New York, NY, USA], LICS ’18, 2018, pp. 879–88. ACM Digital Library, https://

@article{1716842,
      author = {Victoria Michael Udoh, Onyekachukwu Christian Uzoigwe , Damilare Adefemiwa Quadry, Lolade Akinrolabu, Adetunji Philip Adewole },
      title = {A Comparative Review Of Data Representation And Data Types In Modern Programming Languages},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {10},
      pages = {3001-3010},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1716842.pdf},
      abstract = {The representation and classification of data remain central to the design, implementation, and evolution of modern programming languages. This study presents a comprehensive comparative review of data types and data representation across contemporary programming paradigms, situating current practices within their historical and theoretical foundations. Drawing from systematic literature spanning 2015-2025 and guided by PRISMA-based evidence synthesis, the review examines primitive, composite, and advanced data types, alongside the underlying type systems that govern their behavior. The analysis highlights the dual role of data types as both restrictive and enabling constructs, tracing their evolution from early languages such as FORTRAN and ALGOL to modern systems such as Python, Java, and Rust. Key distinctions between static and dynamic typing, as well as strong versus weak type enforcement, are critically evaluated, revealing inherent trade-offs between flexibility, safety, and performance. Furthermore, the review synthesizes recent advances in type system design, including Generalized Algebraic Data Types (GADTs), ownership-based memory models, and declarative collection abstractions, demonstrating their impact on program correctness and efficiency. The study also extends the discussion to modern data-intensive domains, where data representation frameworks underpin developments in array programming, multimodal data integration, and machine learning architectures. Despite noteworthy progress, the findings reveal persistent gaps between theoretical advances in type systems and their practical application in data science workflows. The divergence between safety-oriented systems programming and flexibility-driven data analytics environments shows the need for integrative approaches such as gradual typing, schema-aware type systems, and shape-polymorphic abstractions. This review contributes to the discourse by providing a unified perspective on data types and representation, offering insights that are critical for advancing programming language design and data-centric computing.},
      keywords = {Data Type, Types, Data Representation, Programming Languages, Composite Data},
      month = {April},
      doi = {https://doi.org/10.64388/IREV9I10-1716842}
  }