| Chapter title |
Relational Databases and Biomedical Big Data
|
|---|---|
| Chapter number | 5 |
| Book title |
Bioinformatics in MicroRNA Research
|
| Published in |
Methods in molecular biology, May 2017
|
| DOI | 10.1007/978-1-4939-7046-9_5 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7044-5, 978-1-4939-7046-9
|
| Authors |
de Silva, N. H. Nisansa D., N. H. Nisansa D. de Silva |
| Editors |
Jingshan Huang, Glen M. Borchert, Dejing Dou, Jun (Luke) Huan, Wenjun Lan, Ming Tan, Bin Wu |
| Abstract |
In various biomedical applications that collect, handle, and manipulate data, the amounts of data tend to build up and venture into the range identified as bigdata. In such occurrences, a design decision has to be taken as to what type of database would be used to handle this data. More often than not, the default and classical solution to this in the biomedical domain according to past research is relational databases. While this used to be the norm for a long while, it is evident that there is a trend to move away from relational databases in favor of other types and paradigms of databases. However, it still has paramount importance to understand the interrelation that exists between biomedical big data and relational databases. This chapter will review the pros and cons of using relational databases to store biomedical big data that previous researches have discussed and used. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| South Africa | 1 | 13% |
| Sri Lanka | 1 | 13% |
| United States | 1 | 13% |
| Spain | 1 | 13% |
| France | 1 | 13% |
| Unknown | 3 | 38% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 88% |
| Scientists | 1 | 13% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 22 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 23% |
| Student > Bachelor | 4 | 18% |
| Student > Master | 3 | 14% |
| Student > Doctoral Student | 2 | 9% |
| Researcher | 2 | 9% |
| Other | 2 | 9% |
| Unknown | 4 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 6 | 27% |
| Computer Science | 5 | 23% |
| Medicine and Dentistry | 3 | 14% |
| Engineering | 2 | 9% |
| Psychology | 1 | 5% |
| Other | 1 | 5% |
| Unknown | 4 | 18% |