Big Data technologies: A survey
By Ahmed Oussous, Fatima-Zahra Benjelloun, Ayoub Ait Lahcen, Samir Belfkih. Journal of King Saud University: Computer and Information Sciences, 2017
862 citations recorded by the source corpus.
Developing Big Data applications has become increasingly important in the last few years. In fact, several organizations from different sectors depend increasingly on knowledge extracted from huge volumes of data. However, in Big Data context, traditional data techniques and platforms are less efficient. They show a slow responsiveness and lack of scalability, performance and accuracy. To face the complex Big Data challenges, much work has been carried out. As a result, various types of distributions and technologies have been developed. This paper is a review that survey recent technologies developed for Big Data. It aims to help to select and adopt the right combination of different Big Data technologies according to their technological needs and specific applications’ requirements. It provides not only a global view of main Big Data technologies but also comparisons according to different system layers such as Data Storage Layer, Data Processing Layer, Data Querying Layer, Data Access Layer and Management Layer. It categorizes and discusses main technologies features, advantages, limits and usages.
Subfields: Health Information Management, Information Systems and Management, Management Information Systems
View Paper: https://www.semanticscholar.org/paper/ae27c78d63e56c2b7610bd94fd704fa1b5dcdd29
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In Health Information Management
Citations in the corpus
- A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, methodology): “The application of DL in the design of WSNs were discussed in [28].”
- Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2019, neutral): “[406] A.”
- Multi-document Summarization via Deep Learning Techniques: A Survey (2020, background): “In this era of rapidly advancing technology, the exponential increase of data availability makes analyzing and understanding text files a tedious, labor-intensive, and time-consuming task [65, 120].”
- A Survey on Blockchain for Big Data: Approaches, Opportunities, and Future Directions (2020, background): “ve a number of technological challenges and issues of big data, for example, big data management, data cleansing, imbalanced system capacities, imbalanced data, data analytics, and learning from data [53]. For more details, we refer the interested reader to the survey in [53] and the references ther”