# Analyzing Data Reduction Techniques: An Experimental Perspective
## 4 Proposed Taxonomy  
Data reduction techniques cover a range of approaches that can vary in their data loss characteristics. For some organizations, the amount of data loss can be a critical factor in their algorithm selection and decision-making processes. We believe that the taxonomy we present in this section fills a gap in the reviewed literature, namely the lack of a taxonomy that explicitly considers data loss in data reduction strategies [19; 20]. The proposed taxonomy, illustrated in Figure 1, provides a structured framework for categorizing data reduction techniques. There are two fundamental levels: the first level focuses on the existence of data loss after the application of data reduction algorithms, and the second level focuses on algorithmic properties. This taxonomy provides a methodological approach to organize and understand the various data reduction techniques. The visual representation of the taxonomy makes it easier for researchers and practitioners to navigate and understand the techniques' different categories and subcategories. Ultimately, this taxonomy improves the clarity and accessibility of data reduction techniques, facilitating their evaluation and selection for specific data management and analysis purposes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **Dataset Size** &
\begin{tabular}{c} **Reduction** \\ **Percentage** \\ \end{tabular} & **Reference** \\ \hline Subtractive Clustering Algorithm & NA 1  & NA 1  & NA 1  & \(\sim\)97\% & [6] \\ Spatiotemporal Data Reduction & 95\% & 843 KB, 960 KB & \(\sim\)54\% & [7] \\ Perceptually Important Points, & 76.3\% to & & & \\ Sampling, Piecewise & 93.8\% & NA 1  & \(\sim\)66\% & [13] \\ Approximation & & & & \\ Run Length Encoding and Delta & NA 1  & NA 1  & \(\sim\)52\% & [14] \\ lossless compression & NA 1  & NA 1  & NA 1  & \(\sim\)280 lines & NA 1  & [15] \\ DCT and mixing operation of & 100\% & NA 1  & 44.16\% & [16] \\ scrambled image & 100\% & NA 1  & 12.19\% & [17] \\ CCSDS and Scalable encryption & 100\% & NA 1  & 12.19\% & [17] \\ scheme & & & \\ JPEG XR compression and Block & & & \\ Scrambling & 100\% & NA 1  & 60.77\% & [18] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related work data reduction techniques: comparison. In the proposed taxonomy, data reduction approaches are divided into two main categories:  
* **Lossless data reduction techniques**[14; 21; 22]: these focus on identifying redundancies, patterns, and other inherent characteristics within the data to eliminate or minimize unnecessary and repetitive information. Reducing the data size without any loss is remarkable. However, these algorithms often depend on the type of data they are analyzing. These perform better on repetitive single-sensor data, such as temperature readings. For large-scale datasets, these techniques tend to require a significant processing time to complete the compression, which can be challenging in real-time environments. When time is a primary feature and the workload is enormous and complex, using lossy data reduction algorithms is a viable option. These algorithms include various methods for reducing the data size by selectively discarding or approximating information from the original dataset. Their main advantages include faster processing speeds and the ability to handle a variety of data formats. All these lossless algorithms compress data and can decode the reduced algorithm back to its original size. Some examples of lossless algorithms include Delta Encoding, LZ77, LZ78, Huffman coding, and Run Length Encoding [23; 24]. * **Lossy data reduction techniques**[10; 25; 26; 27]: these focus on reducing data by discarding some details considered less relevant to the analysis or less noticeable to human perception, and thus achieve higher compression ratios than other methods. Some examples of these techniques are transform encoding, Discrete Cosine Transform, Random Projection, Bzip2, or Fractal Compression [28; 29; 30].