Mobile Big Data Analytics Using Deep Learning and Apache Spark (1602.07031v1)

Abstract: The proliferation of mobile devices, such as smartphones and Internet of Things (IoT) gadgets, results in the recent mobile big data (MBD) era. Collecting MBD is unprofitable unless suitable analytics and learning methods are utilized for extracting meaningful information and hidden patterns from data. This article presents an overview and brief tutorial of deep learning in MBD analytics and discusses a scalable learning framework over Apache Spark. Specifically, a distributed deep learning is executed as an iterative MapReduce computing on many Spark workers. Each Spark worker learns a partial deep model on a partition of the overall MBD, and a master deep model is then built by averaging the parameters of all partial models. This Spark-based framework speeds up the learning of deep models consisting of many hidden layers and millions of parameters. We use a context-aware activity recognition application with a real-world dataset containing millions of samples to validate our framework and assess its speedup effectiveness.

• The paper proposes a novel framework that integrates deep learning and Apache Spark for distributed mobile big data analytics.
• It details how data partitioning and iterative refinement enable efficient model training across multiple nodes with a 4-fold speedup.
• Experimental results in activity recognition demonstrate significant accuracy improvements and highlight the method’s scalability for real-time applications.

Deep Learning and Spark: A Framework for Mobile Big Data Analytics

The integration of mobile devices into the fabric of daily life has culminated in the emergence of Mobile Big Data (MBD), characterized by the massive and continuous influx of data from sources such as smartphones and Internet of Things (IoT) devices. The paper "Mobile Big Data Analytics Using Deep Learning and Apache Spark" by Mohammad Abu Alsheikh, Dusit Niyato, Shaowei Lin, Hwee-Pink Tan, and Zhu Han examines the landscape of MBD analytics and proposes an innovative framework integrating deep learning (DL) with Apache Spark to address the computational challenges inherent in analyzing MBD.

Mobile devices, as pervasive data collection platforms, deliver a wealth of uninhibited data. However, the utility of MBD is contingent upon the deployment of potent data analytics methodologies. Deep learning, with its capacity to extract high-accuracy results and automated feature learning from raw data, stands out as a robust method for MBD analytics. Nonetheless, the computational expense of training deep models on voluminous MBD can be prohibitive, requiring novel approaches to enable timely and efficient analytics.

Framework Overview

The framework presented leverages Apache Spark's distributed computing capabilities to parallelize the training of deep learning models across multiple nodes in a high-performance computing (HPC) cluster. By utilizing a scalable MapReduce-based approach, this framework mitigates the significant computational burdens associated with MBD analytics.

1. Data Partitioning: The MBD is divided into numerous partitions stored in Resilient Distributed Datasets (RDDs), a Spark abstraction that supports distributed data operations. This division facilitates parallel processing across the cluster nodes.
2. Model Training: Each Spark worker independently trains a partial deep model using its assigned partition of data. These models are subsequently integrated by the master node, forming a consensus model through parameter averaging.
3. Iterative Refinement: The iterative process continues with parameters disseminated back to the worker nodes for further refinement until convergence criteria are met.

The framework's design, allowing for distributed gradient computation and parameter updates, ensures scalability and efficiency, effectively addressing MBD's "volume" and "velocity" challenges.

Experimental Validation

An empirical evaluation, involving a context-aware activity recognition application, solidifies the framework’s efficacy. The paper utilized a real-world dataset composed of over 38 million unlabeled samples and nearly 3 million labeled samples to train various deep models. Results underscore the proficiency of deep models over traditional machine learning techniques, achieving significant accuracy improvements—14.4% for deep models as opposed to 32.2% for multilayer perceptrons.

Further exploration showcases how increasing the number of computing nodes results in commensurate speedup, highlighting the framework's potential to accelerate model training significantly. The efficient parallelization achieved resulted in a 4-fold learning time reduction when employing multiple Spark workers, demonstrating the promise of this implementation for real-time MBD processing requirements.

Implications and Future Directions

The deployment of Apache Spark alongside deep learning for MBD analytics presents an avenue towards real-time, scalable analytics for mobile systems. While the framework delivers on the promise of efficient MBD processing, it lays the groundwork for future exploration into several key areas:

• Crowd Labeling: Methods for augmenting labeled data through crowd-sourced annotation, both subsidized and intrinsic, could mitigate current reliance on limited labeled datasets.
• Economics of MBD: Investigating business models for monetizing MBD and developing market structures for data exchange.
• Privacy Concerns: Addressing data privacy through anonymization and trust-building measures to encourage broader data sharing within the mobile ecosystem.

Conclusion

Through effectively integrating deep learning with distributed computing frameworks like Apache Spark, this research provides a viable solution for the computational demands of Mobile Big Data analytics. In doing so, it paves the way for more advanced mobile applications that can process and react to data in real time, fundamentally enhancing mobile system capabilities. The groundwork laid herein opens pathways for further research aimed at refining these techniques and expanding their applicability across a range of mobile data analytics scenarios.