# Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics
## 7 Future Work  
Several potential improvements can be built upon the findings of this study. The first is that a clear opportunity exists to see if more advanced machine learning models than what we tested can predict memory usage better, such as Word2 Vec. Additional techniques such as deep learning, neural networks, or ensemble may be better at capturing the nonlinearity caused by such unstructured data processing.  
Further research could also be performed to improve the data preprocessing and feature extraction techniques used in this study. If we make the model harder--using word frequency distributions or text complexity metrics for Word2 Vec, for instance--perhaps we can better predict memory usage. Other techniques for dimensionality reduction, or feature selection, would also be candidates to help improve the generalization of predictive models.  
Future work may also evaluate the scaling of developed models over larger, more heterogeneous datasets. Running these predictive models in different distributed environments and measuring in what ways they are robust and applicable to real-world problems would give valuable insights. Further, the direct incorporation of such predictive models within the Apache Spark framework may obtain substantial performance improvements. By allowing Spark to know in advance how long it will take to execute and how much memory it needs based on dataset characteristics, scheduling and resource management could be dramatically improved, eliminating computational overhead and reducing the waste of machine time and resources. Below are some future recommendations.  
* Deep Learning for Word2 Vec Memory Prediction: Future research should explore advanced machine learning models, such as Recurrent Neural Networks (RNNs) or Transformers, to improve memory usage forecasting for algorithms handling unstructured data. These models are designed to capture complex dependencies and nonlinear relationships inherent in text-based workloads.
* Feature Engineering Enhancements: Introducing advanced features like word frequency distributions, text complexity metrics, and hierarchical data representations could enhance the predictive accuracy for memory usage. Dimensionality reduction and feature selection techniques should also be investigated to improve generalization across datasets.
* Scalability Analysis: Testing the developed models on larger, more heterogeneous datasets across various distributed environments can provide valuable insights into their scalability and adaptability to real-world big data scenarios.
* Integration into Apache Spark: Embedding predictive models directly into Apache Spark could optimize job scheduling and resource management. By enabling the framework to anticipate computational demands based on dataset characteristics, significant reductions in execution overhead and resource wastage could be achieved.
* Extension to Other Operators: Extending the predictive modeling approach to other Spark MLlib operators or alternative distributed frameworks would generalize the findings, offering broader applicability for resource-efficient big data processing.  
Finally future investigations may explore how other operators in Spark MLlib or other distributed frameworks can apply predictive modeling. Extending the utility of predictive modeling would further enhance the development of resource-efficient big data processing systems by investigating how general these models can be applied to other machine learning tasks, like classification or deep learning-based operators.  
[PERSON], [PERSON] and [PERSON] conceived of the idea, designed and constructed the experiments, drafted the initial manuscript, and revised the final manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.