Therefore, online data analysis for big data reduction remains challenging in streaming big data environments.
A detailed gap analysis for the articulation of limitations and future research challenges for data reduction in big data environments is presented.
The assessment of events co-occurrence and their mutual influences is the key challenge for big data reduction.
Despite the objective measurement of exercise time and intensity, there are challenges related to compliance, data reduction and interpretation in long-term continuous measurements [ 15].
From the first microarray-based studies of cancer, a fundamental challenge has been that of data reduction.
Compressed sensing (CS) has emerged as a promising framework to address these challenges because of its energy-efficient data reduction procedure.
In this section, we thoroughly discussed the open issues, research challenges, the limitations of proposed methods for big data reduction and presented some future research directions.
The underlying focus of the following challenges is to achieve the lowest possible overhead and latency: Data reduction: CPRI transports raw I/Q samples.
Although the application of these methods is convenient for data reduction, the trade-off between energy consumptions in local processing with raw data transmission is a key challenge that is needed to be considered.
Accelerometer use in physical activity research has become increasingly popular, but it is challenging among teenagers and it is prone to problems with missing data, which complicate the data reduction and analysis process.
