A Scalable and Semantic Data as a Service Marketplace for Enhancing Cloud-Based Applications

Psomakelis, Evangelos; Nikolakopoulos, Anastasios; Marinakis, Achilleas; Psychas, Alexandros; Moulos, Vrettos; Varvarigou, Theodora; Christou, Andreas

doi:10.3390/fi12050077

Cited by 10 publications

(3 citation statements)

References 21 publications

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“…External experts can provide valuable insights, best practices, and assistance in overcoming specific challenges related to BI implementation in the era of Big Data. Invest in scalable infrastructure, such as cloud-based solutions, to accommodate the growing volume of Big Data (Psomakelis et al,2020). Scalability ensures that the BI system can handle increasing data loads without compromising performance.…”

Section: Challenges In Implementing Bi In the Era Of Big Datamentioning

confidence: 99%

Business Intelligence in the Era of Big Data: A Review of Analytical Tools and Competitive Advantage

Adebunmi Okechukwu Adewusi,

Ugochukwu Ikechukwu Okoli,

Ejuma Adaga

et al. 2024

Comput. sci. IT res. j.

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In the contemporary business landscape, the proliferation of Big Data has revolutionized the way organizations gather, process, and utilize information for strategic decision-making. This paper provides a comprehensive overview of the evolving role of Business Intelligence (BI) in harnessing the potential of Big Data and the subsequent impact on gaining a competitive advantage. The review delves into the arsenal of analytical tools that have emerged to handle the vast volumes of data generated in the digital age. From traditional reporting and querying to advanced analytics, machine learning, and predictive modeling, organizations now have a myriad of options to extract valuable insights from their data reservoirs. This paper investigates the efficiency, scalability, and adaptability of these tools in the context of Big Data, emphasizing their role in transforming raw data into actionable intelligence. Furthermore, the paper explores how the integration of BI and Big Data analytics contributes to the development of a competitive edge for businesses. The ability to harness insights from diverse data sources provides organizations with a holistic view of market trends, consumer behavior, and operational efficiency. This, in turn, empowers decision-makers to make informed and timely choices, enhancing overall organizational agility and responsiveness to market dynamics. The study also highlights the challenges associated with implementing BI in the era of Big Data, including issues related to data quality, security, and the need for skilled professionals. Effective solutions to these challenges are discussed, emphasizing the importance of a robust data governance framework and continuous investment in talent development. This paper underscores the pivotal role of Business Intelligence in leveraging the potential of Big Data for gaining a competitive advantage. As organizations navigate the complexities of the modern business landscape, the judicious use of analytical tools and the integration of BI with Big Data analytics stand as key drivers for informed decision-making and sustainable success. Keywords: Business Intelligence, Big Data, Analytical Tool, Business, AI, Review.

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Section: Challenges In Implementing Bi In the Era Of Big Datamentioning

confidence: 99%

Business Intelligence in the Era of Big Data: A Review of Analytical Tools and Competitive Advantage

Adebunmi Okechukwu Adewusi,

Ugochukwu Ikechukwu Okoli,

Ejuma Adaga

et al. 2024

Comput. sci. IT res. j.

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“…In software engineering, Meyer (2001) proposed software as a product and a service. Sidi et al (2012) and Huang et al (2015) viewed data as a product, whereas Psomakelis et al (2020) viewed data as a service. Whether they are a product or a service, in this paper, a product is defined as a broader concept, including physical and digital products/services/data regardless of their attributes.…”

Section: Definitions Of Different Development Modelsmentioning

confidence: 99%

Continuous cycles of data-enabled design: reimagining the IoT development process

Lee

Cooper

Hands

et al. 2022

AIEDAM

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With the emergence of Internet of Things (IoT) as a new source of “big” data and value creation, businesses encounter novel opportunities as well as challenges in IoT design. Although recent research argues that digital technology can enable new kinds of development processes that are distinctive from their counterparts in the 20th century, minimal attention has been focused on the IoT design process. In order to contextualize New Product Development (NPD) processes for IoT, this paper comprehensively interrogates existing, and emerging development approaches for products, services, software, and integrated products, and several factors that affect designing IoT. This discussion includes the generic development process, the commonalities and differences of different development approaches, and processes. The paper demonstrates that only a few existing approaches reflect vital characteristics of networked artifacts or the integration of data science within the development model, which is one of the key attributes of IoT design. From these investigations, we propose “The Mobius Strip Model of IoT Development ProcessI,” a conceptual process for IoT design, which is distinctive to others. The continuous loops of the IoT design integrate the attributes and phases of different processes and consist of two different development approaches and strategies. Understanding the particular attributes of the IoT NPD process can help novice and experienced researchers in both feeding and drawing insight from the broader design discourse.

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“…However, to achieve efficient collaboration and benefit from AI-based technology, a new integrative environment is needed. The EU-funded DataPorts project will design an industrial data platform [35]. The Cognitive Ports Data Platform will connect existing digital infrastructures of seaports and their systems, establish rules for secure and reliable data exchange and trade, and provide powerful data analysis services.…”

Section: Fig 2 Main Indicators On Which To Act Towards the Digitalization Of The Spanish Port Systemmentioning

confidence: 99%

Federated Learning for Spanish Ports as an Aid to Digitization

2021

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The Spanish Port System is immersed in the process of digital transformation towards the concept of Ports 4.0. This entails new regulatory and connectivity requirements, making it necessary to implement the new technologies offered by the market towards digitalization. The digitalization of the individual processes in a first step helps the exchange of digital information between the members of the port community. The next step will mean that the information flow between the participants of a port community is done in a reliable, efficient, paperless way, and thanks to technologies. However, for the Spanish port sector, data exchange has a competitive disadvantage. That is why Federated Learning is proposed. This approach allows several organizations in the port sector to collaborate in the development of models, but without the need to directly share sensitive port data among themselves. Instead of gathering data on a single server, the data remains locked on your server, and the algorithms and predictive models travel between them. The goal of this approach is to benefit from a large set of data, which contributes to increased Machine Learning performance while respecting data ownership and privacy. Through an Inter-institution or “Cross-silo FL” model, different institutions contribute to the training with their local datasets in which different companies collaborate in training a learning machine for the discovery of patterns in private datasets of high sensitivity and high content. This environment is characterized by a smaller number of participants than the mobile case, with typically better bandwidth and less intermittency.

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A Scalable and Semantic Data as a Service Marketplace for Enhancing Cloud-Based Applications

Cited by 10 publications

References 21 publications

Business Intelligence in the Era of Big Data: A Review of Analytical Tools and Competitive Advantage

Business Intelligence in the Era of Big Data: A Review of Analytical Tools and Competitive Advantage

Continuous cycles of data-enabled design: reimagining the IoT development process

Federated Learning for Spanish Ports as an Aid to Digitization

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