Page 31 - Big Data Analytics for Intelligent Healthcare Management

22 CHAPTER 2 BIG DATA ANALYTICS CHALLENGES AND SOLUTIONS

2.1.5 STATISTICS PRESERVATION-DEMANDING SITUATIONS
Because great fitness facts include extensive collections of datasets, it is difficult to effectively shop
and preserve the records in an unmarried robust force using traditional information control systems
including relational databases [12]. Additionally, it is a heavy cost and time burden for IT within a
small organization or lab.

2.1.6 INFORMATION INTEGRATION CHALLENGES
This stage entails integrating and reworking information into the right layout for subsequent statistics
evaluation. Combining unstructured statistics is the primary mission for big data analytics (BDA). Re-
gardless of established electronic health-care record (EHR) data integration, there are numerous trou-
bles [13], as shown in Fig. 2.3.
The problem arises when fixed health information saved in an Oracle database in machine X is
transferred to a MySQL database in machine Y. The Oracle database and the MySQL database use
statistics structures to keep statistics [14]. Also, machine X would possibly use the “quantity” data type
to store patients’ sexuality statistics, whereas system Y may use the “CHAR” data type. Metadata on
record describes the characteristics of a resource [15]. Within the relational database version, the col-
umn names are used as metadata to explain the traits of the stored statistics.

FIG. 2.3
EHR data incorporation challenges.