Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and...

Area of Science:

• Computational Science
• Data Science
• Artificial Intelligence

Background:

• Big Data technologies present challenges in efficient data management, retrieval, fusion, and processing.
• Bio-inspired computation offers principles of adaptability, intelligence, and robustness beneficial for Big Data.
• A synergistic relationship exists between Big Data and bio-inspired algorithms.

Purpose of the Study:

• To provide an overview of recent research achievements at the intersection of Big Data and bio-inspired computation.
• To identify emerging trends and open challenges in Big Data addressable by bio-inspired algorithms.
• To explore adaptations of bio-inspired algorithms for Big Data contexts, focusing on data fusion and mining.

Main Methods:

• Comprehensive literature analysis of recent advances in Big Data and bio-inspired computation.
• Identification and discussion of current trends and unsolved challenges in Big Data.
• Elaboration on the adaptation of bio-inspired algorithms for Big Data, emphasizing data fusion.
• Comparative analysis of existing approaches across different problems and domains.

Main Results:

• Recent literature shows significant progress in leveraging bio-inspired principles for Big Data.
• Key challenges in Big Data, such as handling heterogeneous data sources, can be addressed by adapted bio-inspired algorithms.
• Data fusion is identified as a critical step for processing and mining diverse Big Data sources effectively.
• The study identifies potential new applications and research niches in this interdisciplinary field.

Conclusions:

• The synergy between Big Data and bio-inspired computation is highly promising for efficient data management and analysis.
• Adaptation of bio-inspired algorithms, particularly for data fusion, is crucial for unlocking their full potential in Big Data.
• Further research is recommended to address open issues and explore novel applications in this rapidly evolving domain.