Despite its substantial economic power, the textile industry currently faces environmental and social challenges, such as continuous extraction of natural resources, extensive water consumption and contamination, greenhouse gas emissions, increasing generation of waste, and inadequate working conditions. In this context, the literature indicates that Big Data contributes to solving these challenges, enabling the extraction of insights and the improvement of decision-making processes from the volume, variety and velocity of data. However, there is still a gap in the literature regarding the directions of how Big Data must be applied by an organization to achieve this goal. Therefore, this article aims to explore this gap, presenting an analysis regarding the nexus between Big Data and sustainability challenges of the textile industry. To this end, a set of 12 textile industry challenges were extracted from an assessment of 108 case studies. These challenges were categorized and contextualized according to Big Data dimensions, and a discussion of the applicability of Big Data to solving each challenge was presented. From this approach, this article contributes to the textile industry by presenting a categorization of sustainable challenges of the industry and also by providing directions regarding the resolution of such challenges from a data-driven perspective.
Several environmental, economic and social challenges are currently being identified, requiring sustainable approaches that enable the preservation of the ecosystem and promote current and future well-being of society. Given this scenario, the circular economy proposal has been considered a potential resource to achieve such goals, generating changes in the production and consumption of products, from business models based on principles of ecodesign, reduction, recovery, reuse and recycling of products. However, it is identified that such business models must be supported by data-driven solutions and digital technologies, to be scalable, facilitate collaboration, and promote greater awareness. Therefore, this course has as main objective to show how big data and digital technologies, such as the internet of things, cloud computing and blockchain, play a key role in the transition to the circular economy, pointing out characteristics of these solutions and how they meet the needs of business models in the context of the circular economy, thus being able to contribute to the current sustainability challenges. ResumoDiversos desafios de cunho ambiental, econômico e social têm sido identificados atualmente, necessitando de abordagens sustentáveis que possibilitem a preservação do ecossistema e promovam o bem estar atual e futuro da sociedade. Diante desse cenário, a proposta de economia circular tem sido considerada um recurso potencial para se alcançar tais objetivos, gerando mudanças na produção e no consumo de produtos, a partir de modelos de negócios baseados em princípios de ecodesign, redução, recuperação, reuso e reciclagem de produtos. Entretanto, identifica-se que tais modelos de negócios devem ser apoiados por soluções orientadas a dados e tecnologias digitais para serem escaláveis,
Knowledge-based intelligent systems might be used in the banking sector to automate customer service. One of the ways to represent knowledge that is both understandable by humans and readable by machines is by using ontologies. Whenever a customer queries its bank regarding specific products or services, the existing knowledge modeled in an ontology might be used by a customer service chatbot to answer it in an automated way. The existing manual information retrieval process from banking specialists is laborious and time-consuming. Specialists use natural language, visual representations, and common sense, often overlooking details. It is a great challenge to make a specialist's knowledge explicit, formal, precise, and completely scalable, which is the format required by a customer service chatbot. We propose a semi-automatic approach to retrieving banking information in Brazilian Portuguese texts with minimal specialist support. By combining Natural Language Processing techniques (e.g., syntactic analysis to obtain the logical meaning of sentences based on rules and its structure) and an ontology constructor library, it was possible to build a tool that receives texts from the banking domain and constructs an ontology that knowledge-based intelligent systems can use. Specialist support is only needed in intermediate refinement steps, thus optimizing the banking specialist's time. The use cases for investments, opening a banking account, and the comparison of the proposed approach show how we reduced manual labor in the information retrieval process by a factor of 40%. Our approach can identify more information in each sentence compared to a similar method found in the literature. The resulting ontologies can be used in a chatbot that automates customer support for a large Brazilian bank.
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