WITHDRAWN: Integration patterns of MongoDB GridFS for advanced data science and big data processing
| Autor: | Janga Vijay kumar, G. Ashmitha, C. Madan Kumar, Syed Abdul Moeed |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
010302 applied physics
business.industry Computer science Scale (chemistry) Big data Volume (computing) Unstructured data 02 engineering and technology 021001 nanoscience & nanotechnology 01 natural sciences Data science Variety (cybernetics) Data processing system Set (abstract data type) Knowledge extraction 0103 physical sciences 0210 nano-technology business |
| Zdroj: | Materials Today: Proceedings. |
| ISSN: | 2214-7853 |
| DOI: | 10.1016/j.matpr.2021.03.357 |
| Popis: | The volume of unstructured data is increasing rapidly with velocity and variety generating enormous large scale datasets which are very difficult in terms of analysis and further extraction of knowledge. Such issues related to the storage, processing and knowledge discovery from huge amount of unstructured data is treated under big data analytics. There are various applications and cases where the volume, velocity and variety of data are increasing very frequently and a significant research work is going on to cope up with the aspects of Big Data processing. Big data is still data, but is of immense scale. Big Data is a concept used to describe an incredibly large volume set of data that is exponentially increasing over time. In short, these files are so massive and complex that no standard data processing systems can store or handle them effectively. In current scenarios, there is need to handle big data with advanced data science approaches and algorithms. In this manuscript, the integration patterns and implementation scenarios of MongoDB GridFS is presented for the advanced applications of data science with big data processing. |
| Databáze: | OpenAIRE |
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