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3.6 ARCHITECTURAL FRAMEWORK AND DIFFERENT TOOLS 51

3.6 ARCHITECTURAL FRAMEWORK AND DIFFERENT TOOLS FOR BIG DATA
ANALYTICS IN HEALTHCARE BIG DATA
This section is subdivided into the following two subsections.

3.6.1 ARCHITECTURAL FRAMEWORK
An architectural framework is an important requirement for any knowledge discovery process. When it
is about big data analytics in healthcare data then it is obligatory for an accurate analysis and idea gen-
eration. A conceptual model provides the guidelines during the whole analysis process from healthcare
data acquisition to the high level of data analysis. Some very good conceptual models on big data an-
alytics in healthcare data can be found in [35] and [36]. A modification of the same is presented below.
A simple and easy to understand framework is needed for an optimal study. Firstly, a level 0 architec-
tural framework for big data analytics in healthcare data is presented (Fig. 3.4). Secondly, a level 1
architectural framework is presented in Fig. 3.5 which is an elaborated version of Fig. 3.4.
In the above Fig. 3.5, the external data sources may be web and social media, machine to ma-
chine data such as reading from different sensors etc. and biometric data, human-generated data
such as that obtained from electronic medical records, doctor’s or physician’s notes etc. Every
day these sources are generating a huge amount of data leading to the formation of big data. Also,
the sources of this data may be situated in different geolocations and the data can have different

FIG. 3.4
Level 0 architectural framework for big data analytics in healthcare data.