# Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics
## 2 Background and Related Work
### Decision-Making in Emerging Technologies for Big Data Analytics  
Various sectors--marketing, healthcare, supply chain management, and manufacturing--are being remodeled through emerging technology in big data analytics-based decision-making processes. Advanced analytics tools and methodologies permit organizations to leverage gigantic datasets to extract actionable insights, thereby making better inferences. \begin{table}
\begin{tabular}{c l l l l} \hline \hline
**Study** & **Methodology** & **Strengths** & **Limitations** & **Advancements in This Study** \\ \hline \multirow{2}{*}{[14]} & SLA-based & Significant & Limited real-time & \multirow{2}{*}{\begin{tabular}{l} \end{tabular} } \\  & memory optimization & memory savings & adaptability & \\ \hline \multirow{2}{*}{[13]} & Workflow & Effective for & \multirow{2}{*}{\begin{tabular}{l} Inefficient for \\ large-scale \\ MLlib tasks \\ \end{tabular} } & & Customized for MLlib \\  & & & large-scale & \\  & & MLlib tasks & systems \\ \hline \multirow{2}{*}{[16]} & ML-based & Improved average & Limited to static & \multirow{2}{*}{\
\begin{tabular}{l} Dynamically adjusts to \\ workload variations \\ \end{tabular} } \\  & parameter tuning & performance by 36\% & configurations & \\ \hline \multirow{2}{*}{[18]} & GPU acceleration & Significant speed-ups & Not generalized for & \multirow{2}{*}{
\begin{tabular}{l} Applies predictive modeling to \\ diverse workloads \\ \end{tabular} } \\  & for Spark & for specific tasks & MLlib operators & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of prior research on Apache Spark optimization. Some research studies offer insight into how new technologies are changing access to decision-making in numerous sectors:  
* **Impact of Emerging Technologies on Freight Transportation**: In this study, the authors review how technologies like artificial intelligence (AI), blockchain, and big data analytics are affecting logistics and decision-making on transportation [21; 22; 23; 24]. Since stakeholders in logistics management would benefit from it, it analyzes nine key technologies that are currently in use and the prospects for their future. * **Big Data Analytics for Business Growth**: [PERSON] et al. investigate how big data analytics can be leveraged to create a competitive advantage for businesses [25]. In their book, they focus on the financial value of big data, providing tools and the best practices for implementation, and the economic importance of big data as a force behind the redefinition of markets and the improvement in profits. * **AI and ML in Supply Chain Planning**: [PERSON] et al. discuss the integration of AI, ML, and big data analytics into supply chain management in their research [26], emphasizing the need for the modernization of supply chain planning for decision-making, forecasting, and agility improvement. * **Intelligent Big Data Analytics**: [PERSON] et al. present a manager framework based on intelligent big data analytics for better business decision-making through planning, organizing, leading, and controlling [27]. * **Multimedia Big Data Analytics**: [PERSON] et al. present a comprehensive view of multimedia big data analytics, considering problems and opportunities in multimedia data management and analytics on a large scale [28]. * **Big Data in Healthcare**: One study reviewed privacy and ethical considerations for big data analytics in healthcare decision-making; [PERSON] et al. describe the opportunities and challenges of big data in healthcare [29]. * **Perspectives on Big Data Analytics**: [PERSON] et al. 's article emphasizes the value of using large datasets for making strategic decisions, asserting that sometimes better algorithms do not give better results, while more data often does [30]. They further highlight the problems with processing large data volumes and their advantages [30]. * **Big Data Analytics Process Architecture**: [PERSON] et al.