Low-Code Versus Code-Based Software Development: Which Wins the Productivity Game?

Trigo, António; Varajão, João; Dias, Marta Ferreira

doi:10.1109/mitp.2022.3189880

Cited by 5 publications

(1 citation statement)

References 16 publications

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“…Productivity theory provides insights into how to increase output per unit of time within a system or organization (Munyai, Makinde, Mbohwa, & Ramatsetse, 2019). Generally, the concept of productivity is a comparison between output and input over a certain period of time (Trigo, Varajao, & Almeida, 2022). This theory considers several factors that influence productivity overall: skills and knowledge of workers play a crucial role in enhancing productivity (Zhenjing, Chupradit, Ku, Nassani, & Haffar, 2022).…”

Section: Productivity Theorymentioning

confidence: 99%

Utilization of Data Analytics to Enhance Operational Efficiency in Manufacturing Companies

Aprijal,

Siregar,

Siahaan

et al. 2024

CNAHPC

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In the digital era, manufacturing industries confront challenges like heightened global competition and intricate production processes, urging them to boost efficiency and productivity. Amidst these circumstances, Big Data emerges as a pivotal opportunity to enhance manufacturing performance. Big Data, characterized by vast volumes of data, utilizes advanced data mining to machine learning techniques for analysis. Data analytics, an interdisciplinary field, profoundly impacts manufacturing operations, enabling deeper insights into production processes. By analyzing production data, companies identify inefficiencies, streamline workflows, and enhance operational efficiency and productivity. Predictive maintenance through sensor data analysis prevents machine failures, while logistics data analysis optimizes supply chains and inventory management, reducing costs and enhancing competitiveness. However, implementing Big Data analytics presents challenges such as rapid data growth, diverse data sources, real-time insights, skill shortages, and data fragmentation. Overcoming these hurdles requires robust technology, skilled personnel, and effective data management strategies. Examples of Big Data analytics applications include customer behavior analysis by Amazon and Netflix, fraud detection in insurance, and urban mobility optimization. Success factors in data analytics implementation include effective data-driven communication, technology integration, and skill enhancement. In conclusion, implementing Big Data Analytics in manufacturing promises significant benefits in operational efficiency, product quality, and competitiveness. Overcoming challenges necessitates robust strategies and consideration of ethical and security issues, ensuring responsible data usage. With a deep understanding of Big Data Analytics, manufacturing companies can leverage this technology to achieve higher efficiency and competitiveness in the global market.

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Section: Productivity Theorymentioning

confidence: 99%

Utilization of Data Analytics to Enhance Operational Efficiency in Manufacturing Companies

Aprijal,

Siregar,

Siahaan

et al. 2024

CNAHPC

View full text Add to dashboard Cite

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Visual Low-Code Language for Orchestrating Large-Scale Distributed Computing

et al. 2023

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Distributed, large-scale computing is typically performed using textual general-purpose programming languages. This requires significant programming skills associated with the parallelisation and distribution of computations. In this paper, we present a visual (graphical) programming language called the Computation Application Language (CAL) to raise abstraction in distributed computing. CAL programs define computation workflows by visualising data flowing between computation units. The goal is to reduce the amount of traditional code needed and thus facilitate development even by non-professional programmers. The language follows the low-code paradigm, i.e. its implementation (the editor and the runtime system) is available online. We formalise the language by defining its syntax using a metamodel and specifying its semantics using a two-step approach. We define a translation of CAL into an intermediate language which is then defined using an operational approach. This formalisation was used to develop a programming and execution environment. The environment orchestrates computations by interpreting the intermediate language and managing the instantiation of computation modules using data tokens. We also present an explanatory case-study example that shows a practical application of the language.

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Investigation on Integration of Machine Learning Techniques into LC/NC Platforms for Code Review, Quality Assessment, and Error Detection Automation

Choudhury,

Gupta

2024

2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO

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Low-Code Versus Code-Based Software Development: Which Wins the Productivity Game?

Cited by 5 publications

References 16 publications

Utilization of Data Analytics to Enhance Operational Efficiency in Manufacturing Companies

Utilization of Data Analytics to Enhance Operational Efficiency in Manufacturing Companies

Visual Low-Code Language for Orchestrating Large-Scale Distributed Computing

Investigation on Integration of Machine Learning Techniques into LC/NC Platforms for Code Review, Quality Assessment, and Error Detection Automation

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