# Analyzing Data Reduction Techniques: An Experimental Perspective
## 10 Conclusions and Future Work  
This study addresses the requirements for data reduction techniques within the current data production landscape. The proposed taxonomy is a valuable tool for identifying the categories of the evaluated algorithms.  
This work provides researchers with a foundation for further exploration by covering multiple data reduction techniques and specific use cases and applications. In addition to the state of the art, this study allows the categorization of different data reduction techniques while providing an overview of their main characteristics and experimental results for distinct data types.  
The evaluation of the data reduction algorithms was based on specific criteria such as the amount of data reduced, the processing speed of the algorithm, and the integrity of data. Regarding numeric data, where processing speed is a priority, Random Projection is a good choice, while BZip2 is preferable for data reduction and integrity. Thus, the algorithm selection must consider the specific data type.  
It is crucial to carefully evaluate the specific needs and trade-offs of the dataset, and the requirements of the final application, to select the most appropriate algorithm to achieve optimal results. Linear regression stood out as the best-performing lossy data reduction algorithm, with the highest percentage variance reaching a maximum of 80.63%. On the other hand, the best lossless compression algorithm was BZip2, with a percentage variance of \(-\)98.63%.  
The detailed analysis and discussion within each algorithm class reveals the performance of different techniques on different data types, providing a deep understanding of their capabilities and limitations. This research contributes to the field by providing decision makers with insights into the appropriate selection of data reduction strategies. Finally, this research highlights key considerations for data reduction algorithms, especially in IoT environments, where performance requirements necessitate careful curation of algorithms to meet application-specific needs and comply with device constraints.  
In future work, we intend to investigate additional data reduction techniques, such as autoencoders and other supervised methods. We also plan to explore additional domains, such as audio, image, and video, to promote a comprehensive understanding of the multiple dimensions of the data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Data Type**} & \multirow{2}{*}{**Dataset Name**} & **Algorithm with the Best** & \multirow{2}{*}{**Algorithm with the Best** & \multirow{2}{*}{**Processing Time per MB**} \\  & & **Percentage** & **Variance** & \\ \hline \multirow{2}{*}{Numeric} & Accelerometer & Linear Regression & Random Projection \\  & Smoke Detection & Linear Regression & Random Projection \\ \hline Temporal, Text & IoT Temperatures & Linear Regression & Linear Regression \\ \hline \hline \end{tabular}
\end{table}
Table 14: _Cont._  
**Author Contributions:** Conceptualization, G.C., V.P. and J.B.; Methodology, V.F. and V.P.; Software, V.F.; Formal analysis, G.C.; Investigation, V.F.; Writing--original draft, V.F.; Writing--review & editing, G.C., V.P. and J.B.; Supervision, V.P. and J.B.; Project administration, J.B.; Funding acquisition, J.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available in the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.