Name
Abstract | ||
|---|---|---|
With the explosion of big data technology, healthcare data is continuously and rapidly growing, with abundant and various values. There are wide varieties of data and heterogeneous healthcare data (images, text, video, raw sensor data, etc.) that are generated and required to be effectively stored, processed, queried, indexed and analyzed. These datasets differ widely in their volume, variety, velocity and value, including patient-oriented data such as electronic medical records (EMR), public-oriented data such as public health data, and knowledge-oriented data such as drug-to-drug, drug-to-disease, and disease-to-disease interaction registries. Big data in healthcare brings great challenges but plays an important role in healthcare transformation. The traditional techniques do not compromise end-users’ Quality of Service (QoS) in terms of data availability, data response delay, etc. It is urgent to develop software tools and techniques that support rapid query processing and speed-up data analytics, which provide awareness and knowledge in real-time. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ACCESS.2018.2870417 | IEEE ACCESS |
Field | DocType | Volume |
Data science,Health care,Data availability,Data analysis,Computer science,Quality of service,Computer network,Software,Medical record,Compromise,Big data | Journal | 6 |
ISSN | Citations | PageRank |
2169-3536 | 1 | 0.35 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yin Zhang | 1 | 760 | 40.11 |
| Haiyang Wang | 2 | 573 | 73.18 |
| Min Chen | 3 | 1121 | 62.51 |
| Jiafu Wan | 4 | 1866 | 100.02 |
| Iztok Humar | 5 | 204 | 16.26 |