The chapter deals with the big data in biology. The largest collection of biological data maintenance paves the way for big data analytics and big data mining due to its inefficiency in finding noisy and voluminous data from normal database management systems. This provides the domains such as bioinformatics, image informatics, clinical informatics, public health informatics, etc. for big data analytics to achieve better results with higher efficiency and accuracy in clustering, classification and association mining. The complexity measures of the health care data leads to EHR (Evidence-based HealthcaRe) technology for maintenance. EHR includes major challenges such as patient details in structured and unstructured format, medical image data mining, genome analysis and patient communications analysis through sensors-biomarkers, etc. The big biological data have many complications in their data management and maintenance especially after completing the latest genome sequencing technology, next generation sequencing which provides large data in zettabyte size.
|Title of host publication||Modern Technologies for Big Data Classification and Clustering|
|Publisher||IGI Global Publishing|
|Number of pages||16|
|ISBN (Print)||1522528059, 9781522528050|
|Publication status||Published - 12-07-2017|
All Science Journal Classification (ASJC) codes
- Computer Science(all)