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Item type:Publication, A robust fuzzy stochastic programming for sustainable procurement and logistics under hybrid uncertainty using big data(Elsevier, 2020-06-10)Today, in many organizations, the debate about the difference in core capabilities has become an important factor for market competition. Companies, based on the field of activity, decide to strengthen some of their capabilities, capacities, and expertise. Therefore, the focus of an organization on the strengths and efforts to develop its sustainability will lead to a competitive advantage in the marketplace. Due to changes in environmental factors, organizations have focused on carbon emissions in procurement and transportation that have the highest carbon footprint. This paper proposes a multi-objective, eco-sustainability model for a supply chain. The objectives are to minimize overall costs, maximize the efficiency of transportation vehicles and minimize information fraud in the process of information sharing within supply chain elements. Big data is considered in the amount of information exchanged between customers and other elements of the proposed supply chain; since there are frauds in information sharing then using big data 5Vs the model is adapted to control the cost of information loss leading to customer dissatisfaction. Since uncertainty is inevitable in the real environments, in this research hybrid uncertainty is considered. Because two sources of uncertainty are considered in most of the parameters, thus it is necessary to robustify the decision-making process. The model is a mixed integer nonlinear program including big data for an optimal sustainable procurement and transportation decision. A heuristic method is used to solve the big data problem that makes use of a robust fuzzy stochastic programming approach. The proposed model can prevent disturbances by using a scenario-based stochastic programming approach. An effective hybrid robust fuzzy stochastic method is also employed for controlling uncertainty in parameters and risk taking out of outbound decisions. To solve the multi-objective model, augmented ε-constraint method is utilized. The model performance is investigated in a comprehensive computational study. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A microgrid energy management system based on chance-constrained stochastic optimization and big data analytics(Elsevier, 2020-05-01)A Microgrid (MG) is a promising distributed technology to solve todays energy challenges. They are changing how electricity is produced, transmitted, and distributed, enabling to capture massive amounts of data from sensors, and other electrical infrastructures. However, recent advances in modeling and optimization of MG neither integrate the use of big data technologies aggressively nor focus on developing an optimal operational strategy for a single building. To bridge this gap, this research proposes to use Apache Spark to enhance the performance of a scalable stochastic optimization model for an MG for multiple buildings, and to ensure that a significant portion of the wind power output will be utilized. The decision model is formulated as a chance constraint two-stage optimization problem to obtain operation decisions for a behind-the-meter topology. The comparison between the current practice of using historical data and integrating Apache Spark technologies demonstrates the superiority of the streaming data as energy management strategy. Experiments under different settings show that using big data strategy, the model can (1) achieve more cost savings of the total system, (2) increase resiliency to power disturbances, and (3) build a data analytics framework to enhance the decision-making process. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Characteristics and trends in big data for service operations management research: A blend of descriptive statistics and bibliometric analysis(Springer Science and Business Media Deutschland GmbH, 2022-01-01)The field of service operations management has a plethora of research opportunities to capitalise on, which are nowadays heightened by the presence of big data. In this research, we review and analyse the current state-of-the-art of the literature on big data for service operations management. To this aim, we use the Scopus database and the VOSviewer visualisation software for bibliometric analysis to highlight developments in research and application. Our analysis reveals patterns in scientific outputs and serves as a guide for global research trends in big data for service operations management. Some exciting directions for the future include research on building big data-driven analytical models which are deployable in the Cloud, as well as more interdisciplinary research that integrates traditional modes of enquiry with for example, behavioural approaches, with a blend of analytical and empirical methods. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An efficient outlier detection with deep learning-based financial crisis prediction model in big data environment(Hindawi Limited, 2022-01-01)As Big Data, Internet of Things (IoT), cloud computing (CC), and other ideas and technologies are combined for social interactions. Big data technologies improve the treatment of financial data for businesses. At present, an effective tool can be used to forecast the financial failures and crises of small and medium-sized enterprises. Financial crisis prediction (FCP) plays a major role in the country's economic phenomenon. Accurate forecasting of the number and probability of failure is an indication of the development and strength of national economies. Normally, distinct approaches are planned for an effective FCP. Conversely, classifier efficiency and predictive accuracy and data legality could not be optimal for practical application. In this view, this study develops an oppositional ant lion optimizer-based feature selection with a machine learning-enabled classification (OALOFS-MLC) model for FCP in a big data environment. For big data management in the financial sector, the Hadoop MapReduce tool is used. In addition, the presented OALOFS-MLC model designs a new OALOFS algorithm to choose an optimal subset of features which helps to achieve improved classification results. In addition, the deep random vector functional links network (DRVFLN) model is used to perform the grading process. Experimental validation of the OALOFS-MLC approach was conducted using a baseline dataset and the results demonstrated the supremacy of the OALOFS-MLC algorithm over recent approaches. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Transformation of International Trade and Marketing: A Review of the Impact of Artificial Intelligence and Automation(World Scientific, 2026-02-01)This paper examines the current and future trends in integrating Artificial Intelligence (AI) and automation in marketing and international trade. The main objective is understanding how these technologies transform these fields, identifying the predominant discussions, methodologies employed, and reported results since 2010. To this end, a rigorous and systematic scoping review was conducted using the Scopus and Google Scholar databases. A total of 35 studies published between 2010 and 2023 were selected following a multi-phase screening process based on thematic alignment, methodological rigor, and publication quality. Only peer-reviewed journal articles and conference proceedings published in English were considered. Studies included had to explicitly address AI and automation in marketing or international trade, while descriptive reports or articles lacking analytical depth were excluded. The findings show a trend towards qualitative approaches in studies, emphasising theoretical and conceptual understanding over generating new data. Key themes identified include efficiency in cross-border e-commerce, user knowledge, adaptation to the era of big data and its effects on logistics, and regulatory challenges in the face of AI. These findings underscore the growing importance of AI and automation in marketing and international trade, highlighting opportunities and challenges for businesses and governments in a globalised environment.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Exploring the Intersection of Big Data and Open Innovation: Synergies and Challenges in Emerging Contexts(University of Porto, 2025-04-01)This study explores the symbiotic relationship between Big Data and Open Innovation. They work together to accelerate innovation and generate value for organizations. Different sectors and organizations go through decision-making processes that range from operational and routine aspects to managerial and strategic decisions, such as the implementation of a new product or the purchase of a new production unit. All these decisions require data and information that can mitigate risks and support choices. This study draws insights from real-world case studies, emphasizing the transformative potential of Big Data for open innovation. It analyzes evidence of the use of Big Data in Open Innovation practices in a group of Peruvian software companies. The results show the gap in the use of Big Data to support collaborative practices in the sample. In fact, often, those companies do not see a direct relationship between Big Data experience and the adoption of Open Innovation practices. The findings underscore the need for integrated approaches and suggest that bridging the gap between Big Data and Open Innovation offers a promising avenue for thriving in an information-rich environment.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Low-Resourced Peruvian Language Identification Model(CEUR-WS, 2017)Due to the linguistic revitalization in Peru´ through the last years, there is a growing interest to reinforce the bilingual education in the country and to increase the research focused in its native languages. From the computer science perspective, one of the first steps to support the languages study is the implementation of an automatic language identification tool using machine learning methods. Therefore, this work focuses in two steps: (1) the building of a digital and annotated corpus for 16 Peruvian native languages extracted from documents in web repositories, and (2) the fit of a supervised learning model for the language identification task using features identified from related studies in the state of the art, such as ngrams. The obtained results were promising (97% in average precision), and it is expected to take advantage of the corpus and the model for more complex tasks in the future10