Both the adoption of digital apps and monitoring devices, and use of analytics on big data from diverse sources are considered the key aspects for improving healthcare and increasing cost-efficiencies (WEF 2016).
In fact, big data is diverse and unstructured, therefore it requires special tools and techniques that go beyond traditional information system and relational databases solutions.
The integration of data of diverse origins raises a big challenge, since the level of confidence associated with individual objects varies considerably depending on the type of evidence and methodology used.
The HSTSM modelling approach incorporates a number of soft computing techniques such as: deep belief networks, auto-encoders, agglomerative hierarchical clustering and temporal sequence processing, in order to address the computational challenges arising from analysing and processing large volumes of diverse data to provide an effective big data analytics tool for diverse application areas.
With the transformative role, big data has played in diverse settings, there is a lot of interest in harnessing the power of big data for development and social good.
To handle diverse measurements of big data in terms of volume, velocity, and variety, there is need to design efficient and effective frameworks to process expansive measure of data arriving at very high speed from various sources.
Big data refers to large, diverse, complex, longitudinal, and distributed data sets.
Security analytics is more than just big data – it's also diverse data.
The new methodology of Big Data analysis [35] enables the analysis of diverse data types where protein, nucleotide, structure, and functional data can be analyzed in combination.
We illustrate our approach for deriving the big data architectures of different well-known big data systems.
