There are also quite a few research works which address some challenges in big data analytics with keywords like "huge data," "large scale dataset," and "high speed streaming data,", but no "big data".
To handle large-scale datasets and applications that feature high dimensions, the development of distributed algorithms is central.
6, we apply the method along with its improvements to some large-scale datasets and present its performance in terms of accuracy and compression and decompression speeds on datasets obtained from turbulent combustion simulations and land-ice modeling.
In this review, we present an overview of the state-of-the-art single-cell DNA sequencing methods, technical errors that are inherent in the resulting large-scale datasets, and computational methods to overcome these errors.
This happens first through employing HAFNI framework to handle the big data matrix for each subject and second by utilizing a database to store large-scale datasets, and then using a scheduling engine to distribute analyzing tasks to multiple machines and process multiple subjects simultaneously.
The first is where a tag number within a specific window is very large, e.g. over 1000, which is often observed in large-scale datasets and in a human histone modification database developed by Zhang et al. [63].
Manually curated data are supplemented by automatic gene annotation and large-scale datasets, and information about additional sequence feature types.
Therefore, it is essential to develop a novel computational approach that can effectively integrate all available large-scale datasets and prioritize potential anti-cancer drug targets.
Thus, both large-scale datasets and novel computational approaches are fundamental to decipher the histone code that controls the functionality of chromatin by modulating nucleosomal structure [ 17].
The acceleration capabilities offered by modern processors (many-core and multicore) enables performing all-to-all PSC using different methods on large-scale datasets and compare results.
