Efficient Machine Learning for Big Data: A Review (1503.05296v1)

Abstract: With the emerging technologies and all associated devices, it is predicted that massive amount of data will be created in the next few years, in fact, as much as 90% of current data were created in the last couple of years,a trend that will continue for the foreseeable future. Sustainable computing studies the process by which computer engineer/scientist designs computers and associated subsystems efficiently and effectively with minimal impact on the environment. However, current intelligent machine-learning systems are performance driven, the focus is on the predictive/classification accuracy, based on known properties learned from the training samples. For instance, most machine-learning-based nonparametric models are known to require high computational cost in order to find the global optima. With the learning task in a large dataset, the number of hidden nodes within the network will therefore increase significantly, which eventually leads to an exponential rise in computational complexity. This paper thus reviews the theoretical and experimental data-modeling literature, in large-scale data-intensive fields, relating to: (1) model efficiency, including computational requirements in learning, and data-intensive areas structure and design, and introduces (2) new algorithmic approaches with the least memory requirements and processing to minimize computational cost, while maintaining/improving its predictive/classification accuracy and stability.

• The paper introduces sustainable data modeling techniques that optimize energy use and computational efficiency for handling large-scale data.
• It presents novel algorithmic innovations that integrate ensemble methods and deep learning to reduce memory and processing demands.
• The research establishes a foundation for future work on implementing energy-efficient strategies in distributed, big data environments.

Efficient Machine Learning for Big Data: A Review

The reviewed paper addresses the pressing challenge of creating sustainable machine learning models capable of efficiently handling the burgeoning scale of big data. The authors, Al-Jarrah et al., explore both the theoretical frameworks and practical implementations relevant to this issue, particularly in energy-intensive and data-rich environments.

Context and Motivation

The paper identifies the exponential growth of data across various scientific domains such as climatology, bioinformatics, and astronomy. It situates this within the broader context of the global ICT industry's environmental footprint, particularly energy consumption. The need for sustainable computing thus arises as a critical factor, aiming to balance high performance with minimal environmental impact.

Core Contributions

1. Model Efficiency and Computational Cost: The authors focus on reducing computational complexity in data-intensive machine learning models. They argue that existing nonparametric models incur a high computational cost to achieve global optima, which hampers their scalability.
2. Algorithmic Innovations: The paper highlights novel algorithmic solutions that minimize memory requirements and processing demands without sacrificing predictive accuracy or stability. These innovations are positioned as essential for enabling scalability in large datasets.
3. Sustainable Data Modeling: The paper proposes sustainable data modeling as a methodology to maximize learning accuracy while minimizing computational expenditure. This involves techniques like ensemble models and local learning strategies, which the authors claim enhance performance efficiency.
4. Deep Learning and Big Data Computing: The review extends to discuss how modern deep learning architectures, like DNNs and DBNs, can be optimized through semiparametric approaches to reduce computational overhead and address scalability issues. Moreover, the integration of deep learning with parallel computing frameworks such as Hadoop is presented as a promising avenue for big data analytics.

Implications and Future Directions

The paper implies several practical and theoretical implications. Practically, it suggests that these sustainable modeling techniques can significantly reduce the energy footprint of large-scale data processing tasks. Theoretically, it encourages adopting a paradigm shift towards incorporating energy efficiency as a core objective in algorithm design.

Future research is likely to evolve in two main directions: enhancing the algorithmic capabilities to further lower the energy cost per computational decision and integrating these methodologies into increasingly complex, distributed computing environments. As the paper suggests, there is also a potential for expanding the application of these models in various e-sciences contexts, likely leading to more specialized and domain-specific algorithmic improvements.

In conclusion, the paper provides a comprehensive overview of the current landscape in energy-efficient machine learning for big data, offering valuable insights into both ongoing challenges and potential solutions. It serves as a guiding resource for researchers looking to balance performance considerations with an increasing emphasis on environmental sustainability.