Publicação
Foreword to the thematic track: Quality aspects in big data systems
| Resumo: | [Excerpt] Recent studies have shown that poor quality data is predominant in many Big Data systems containing a variety of sources such as linked data, mobile data, social media data, Internet of Things data, and many others. The fourth "V" of big data (veracity) directly refers to uncertainty and data quality problems. With the variety of Big Data sources, new frameworks and methods are needed for quality assessment, management and improvement due to the sheer volume and velocity of data. Although significant progresses have been made, mainly in what concerns technologies for processing Big Data, several challenges still remain, including distributed and streaming discovery of data quality, crowdsourced data cleaning, and tools/data validators. In this thematic track, the focus is novel contributions for addressing Quality Aspects in Big Data Systems, ranging from conceptual frameworks to case studies, from design to implementation, from data collection to data analytics, or from data cleansing to data integration. [...] |
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| Autores principais: | Santos, Maribel Yasmina |
| Ano: | 2017 |
| País: | Portugal |
| Tipo de documento: | outro |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | [Excerpt] Recent studies have shown that poor quality data is predominant in many Big Data systems containing a variety of sources such as linked data, mobile data, social media data, Internet of Things data, and many others. The fourth "V" of big data (veracity) directly refers to uncertainty and data quality problems. With the variety of Big Data sources, new frameworks and methods are needed for quality assessment, management and improvement due to the sheer volume and velocity of data. Although significant progresses have been made, mainly in what concerns technologies for processing Big Data, several challenges still remain, including distributed and streaming discovery of data quality, crowdsourced data cleaning, and tools/data validators. In this thematic track, the focus is novel contributions for addressing Quality Aspects in Big Data Systems, ranging from conceptual frameworks to case studies, from design to implementation, from data collection to data analytics, or from data cleansing to data integration. [...] |
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