Big Data: state-of-the-art concepts, techniques, technologies, modeling approaches and research challenges

• Autor(a) principal: Costa, Carlos
• Data de Publicação: 2017
• Outros Autores: Santos, Maribel Yasmina
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/1822/46855
• topic: Big Data; NoSQL; Hadoop; Challenges; Modelling; Review; State-of-the-art; Techniques; Technologies; Engenharia e Tecnologia::Outras Engenharias e Tecnologias

Current advancements in Information Technologies (IT) lead organizations to pursue high business value and competitive advantages through the collection, storage, processing and analysis of huge amounts of heterogonous data, generated at ever increasing rates. Data- driven organizations are often seen as environments wherein the analysis and understanding of products, people and transactions are of major relevance. Big Data, mainly defined as data with high volume, variety and velocity, creating severe limitations in traditional technologies, promises to leverage smarter insights based on challenging and more granular data sources, increasingly demanding emergent skills from data scientists to revolutionize business products, processes and services. The concept gained significant notoriety during the last years, since many business areas can benefit from this phenomenon, such as healthcare, public sector, retail, manufacturing and modern cities. Big Data as a research topic faces innumerous challenges, from the ambiguity and lack of common approaches to the need of significant organizational changes. Therefore, research on Big Data is relevant to assure that organizations have rigorously justified proofs that emergent techniques and technologies can help them making progress in data-driven business contexts. This work presents a state-of-the-art literature review in Big Data, including its current relevance, definition, techniques and technologies, while highlighting several research challenges in this field. Furthermore, this work also provides relevant rules for modelling databases in Big Data environments, which can be used to convert relational data models into column-oriented data models.