In this section, we analyze the relationships between machine learning and SP techniques for big data processing from four perspectives: (1) statistical learning for big data analysis, (2) convex optimization for big data analytics, (3) stochastic approximation for big data analytics, and (4) outlying sequence detection for big data.
To support big data processing, different platforms incorporate scaling in different forms.
The strategy combines internet based semi-structured data source, big data processing procedure, and different estimation methods of regression discontinuity design to carry out the analysis.
In this context, it seems that Big Data processing must profit from available computing resources by applying parallel execution models, thereby achieving results in "acceptable" times.
In [11, 12], the authors proposed various big data processing models and algorithms from the data mining perspective.
Adaptability for the different data sizes, critical to big data processing, is realized by the ability to shrink or expand the whole architecture to fit different deeply learned feature dimensions.
Hadoop is a popular technology for big data processing on cloud to offload computing burden from terminals.
To support semantic-based multimedia retrieval in big data environment, this paper presents an optimized algorithm called semantic ontology retrieval (SOR), which uses big data processing tools to store and retrieve ontologies from heterogeneous multimedia data.
The proposed approach can well satisfy all critical parameters such as scalability, partial failure support, extensibility as expected from next-generation big data processing systems.
Apple has also made dozens of smaller and bigger acquisitions in the last several years that speak to it picking up more talent and IP in the quest to build out its AI muscle across different areas, from augmented reality and computer vision through to big data processing at the back end.
