Un enfoque de toma de decisiones multicriterio aplicado a la estrategia de transformación digital de las organizaciones por medio de la inteligencia artificial responsable en la nube de las organizaciones. Estudio de caso en el sector de salud

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Estudios Estadísticos, leída el 08-02-2023Organisations are committed to understanding both the needs of their customers and the capabilities and plans of their competitors and partners, through the processes of acquiring and evaluating market information in a systematic and anticipatory manner. On the other hand, most organisations in the last few years have defined that one of their main strategic objectives for the next few years is to become a truly data-driven organisation in the current Big Data and Artificial Intelligence (AI) context (Moreno et al., 2019). They are willing to invest heavily in Data and AI Strategy and build enterprise data and AI platforms that will enable this Market-Oriented vision (Moreno et al., 2019). In this thesis, it is presented a Multicriteria Decision Making (MCDM) model (Saaty, 1988), an AI Digital Cloud Transformation Strategy and a cloud conceptual architecture to help AI leaders and organisations with their Responsible AI journey, capable of helping global organisations to move from the use of data from descriptive to prescriptive and leveraging existing cloud services to deliver true Market-Oriented in a much shorter time (compared with traditional approaches)...Las organizaciones se comprometen a comprender tanto las necesidades de sus clientes como las capacidades y planes de sus competidores y socios, a través de procesos de adquisición y evaluación de información de mercado de manera sistemática y anticipatoria. Por otro lado, la mayoría de las organizaciones en los últimos años han definido que uno de sus principales objetivos estratégicos para los próximos años es convertirse en una organización verdaderamente orientada a los datos (data-driven) en el contexto actual de Big Data e Inteligencia Artificial (IA) (Moreno et al. al., 2019). Están dispuestos a invertir fuertemente en datos y estrategia de inteligencia artificial y construir plataformas de datos empresariales e inteligencia artificial que permitan esta visión orientada al mercado (Moreno et al., 2019). En esta tesis, se presenta un modelo de toma de decisiones multicriterio (MCDM) (Saaty, 1988), una estrategia de transformación digital de IA de la nube y una arquitectura conceptual de nube para ayudar a los líderes y organizaciones de IA en su viaje de IA responsable, capaz de ayudar a las organizaciones globales a pasar del uso de datos descriptivos a prescriptivos y aprovechar los servicios en la nube existentes para ofrecer una verdadera orientación al mercado en un tiempo mucho más corto (en comparación con los enfoques tradicionales)...Fac. de Estudios EstadísticosTRUEunpu

An infrastructure service recommendation system for cloud applications with real-time QoS requirement constraints

The proliferation of cloud computing has revolutionized the hosting and delivery of Internet-based application services. However, with the constant launch of new cloud services and capabilities almost every month by both big (e.g., Amazon Web Service and Microsoft Azure) and small companies (e.g., Rackspace and Ninefold), decision makers (e.g., application developers and chief information officers) are likely to be overwhelmed by choices available. The decision-making problem is further complicated due to heterogeneous service configurations and application provisioning QoS constraints. To address this hard challenge, in our previous work, we developed a semiautomated, extensible, and ontology-based approach to infrastructure service discovery and selection only based on design-time constraints (e.g., the renting cost, the data center location, the service feature, etc.). In this paper, we extend our approach to include the real-time (run-time) QoS (the end-to-end message latency and the end-to-end message throughput) in the decision-making process. The hosting of next-generation applications in the domain of online interactive gaming, large-scale sensor analytics, and real-time mobile applications on cloud services necessitates the optimization of such real-time QoS constraints for meeting service-level agreements. To this end, we present a real-time QoS-aware multicriteria decision-making technique that builds over the well-known analytic hierarchy process method. The proposed technique is applicable to selecting Infrastructure as a Service (IaaS) cloud offers, and it allows users to define multiple design-time and real-time QoS constraints or requirements. These requirements are then matched against our knowledge base to compute the possible best fit combinations of cloud services at the IaaS layer. We conducted extensive experiments to prove the feasibility of our approach

A framework to evaluate big data fabric tools

A huge growth in data and information needs has led organizations to search for the most appropriate data integration tools for different types of business. The management of a large dataset requires the exploitation of appropriate resources, new methods, as well as the possession of powerful technologies. That led the surge of numerous ideas, technologies, and tools offered by different suppliers. For this reason, it is important to understand the key factors that determine the need to invest in a big data project and then categorize these technologies to simplify the choice that best fits the context of their problem. The objective of this study is to create a model that will serve as a basis for evaluating the different alternatives and solutions capable of overcoming the major challenges of data integration. Finally, a brief analysis of three major data fabric solutions available on the market is also carried out, including Talend Data Fabric, IBM Infosphere, and Informatica Platform

Open innovation using satellite imagery for initial site assessment of solar photovoltaic projects

One of the responses to the fight against climate change by the developing world has been the large-scale adoption of solar energy. The adoption of solar energy in countries like India is propagating mainly through the development of energy producing photovoltaic farms. The realization of solar energy producing sites involves complex decisions and processes in the selection of sites whose knowhow may not rest with all the stakeholders supporting (e.g., banks financing the project) the industry value chain. In this article, we use the region of Bangalore in India as the case study to present how open innovation using satellite imagery can provide the necessary granularity to specifically aid in an independent initial assessment of the solar photovoltaic sites. We utilize the established analytical hierarchy process over the information extracted from open satellite data to calculate an overall site suitability index. The index takes into account the topographical, climatic, and environmental factors. Our results explain how the intervention of satellite imagery-based big data analytics can help in buying the confidence of investors in the solar industry value chain. Our study also demonstrates that open innovation using satellites can act as a platform for social product development

Digitally Enabled Multi-Criteria Decision Making For Energy Efficiency Projects

Multi-criteria decision-making (MCDM) is a complex process that evaluates and prioritizes options based on multiple criteria, often involving conflicting objectives. In energy efficiency projects, MCDM can be applied to balance cost, environmental impact, and energy savings, enabling more informed and sustainable choices. For portfolio optimization, it assists in selecting a mix of projects that maximize returns while managing risk and resource constraints. Essential for implementing MCDM are digital tools like data analytics and decision support systems. The paper's theoretical framework examines the relationship between project portfolio optimization and MCDM, enabling a comprehensive project evaluation based on cost, risk, environmental impact, and energy efficiency. This approach is particularly appropriate for energy efficiency projects with specific requirements such as sustainability, long-term cost savings, and compliance with environmental regulations. Digital tools facilitate this process by providing advanced data analytics and decision support, which is crucial for balancing these complex, often conflicting criteria. This synergy between portfolio optimization and MCDM, especially in energy efficiency, emphasizes the theoretical model's practical relevance and applicability. In the empirical section of the study, we test our theoretical considerations by analyzing case studies from platforms like SCOPUS. These cases can provide concrete evidence of how MCDM, coupled with digital tools, effectively optimizes project portfolios, especially in energy efficiency projects. The analysis of diverse real-world scenarios demonstrates the practical effectiveness of MCDM in meeting specific project requirements and realizing strategic portfolio goals. In conclusion, this paper aims to bridge the theoretical and empirical realms, demonstrating the effectiveness of MCDM in project portfolio optimization, particularly for energy efficiency projects. The theoretical framework, emphasizing the synergy between MCDM and digital tools, is supported by the empirical evidence from case studies, highlighting MCDM's pivotal role in guiding strategic decisions and optimizing portfolios to meet both efficiency and sustainability goals