# Analyzing Data Reduction Techniques: An Experimental Perspective
## 3 Related Work  
Data reduction is a major topic of research in a variety of areas, including Big Data and the Internet of Things (IoT). The fundamental purpose of data reduction strategies is to reduce the storage footprint of a dataset, and this section discusses and summarizes some of the existing work in this area. [PERSON], [PERSON], and [PERSON] [6] explored a solution in the IoT gateways that converts time series domains to frequency domains to extract patterns or trends in the stream data. The solution presented in the paper could reduce cloud storage and bandwidth consumption and prevent I/O bottlenecks. However, the paper did not present the accuracy and reduction percentage, and there is not enough data to calculate it. [PERSON], [PERSON], and [PERSON] [7] introduced a domain-independent IoT-based spatiotemporal data reduction approach for IoT-structured data, which consists of treating spatial data with the K-Means algorithm to preserve location information and using a data similarity technique on a time basis to preserve temporal data information. The presented solution achieves an average data reduction of 54% with an average accuracy of 95%. [PERSON], [PERSON], and [PERSON] [8] presented a data reduction technique based on data compression. To reduce the energy consumption when transmitting data to the gateway, the authors proposed an approach in the sensor nodes. The proposed approach consists of two stages: the first stage involves a lossy symbolic aggregate approximation (SAX) quantization stage to minimize the dynamic range of the sensor readings. The final stage is a lossless LZW compression to compress the output of the first stage. The results obtained are a reduction of more than 90% of the produced data, with only 4.74% not reaching the gateway in the worst case. It remains an open possibility to propose a dynamic compression algorithm that can convert from lossless to lossy-based parameters. [PERSON] et al. [9] present a set of algorithms and techniques to reduce high-volume and high-velocity data in Big Data environments. This survey shows some dimension-reduction approaches to reduce the heterogeneity and massively variable data into manageable data and indicates deduplication techniques and redundancy techniques. It presents a taxonomy of the best and most-used data compression algorithms in terms of decompression overhead. The authors conclude that many efficient data reduction techniques achieve good performance in reduction and accuracy, but they still need more focus from researchers. [PERSON], [PERSON], and [PERSON] [10] analyzed several prediction-based data reduction approaches to decide which technique is the best to reduce energy consumption in wireless sensor networks. The main contribution was to emphasize not only the introduction of the prediction techniques, but also the methods used in data reduction solutions for WSNs. The authors conclude that the IoT path will depend on the scalability of the sensor networks and their ability to self-access the wireless medium. In addition, the reduction in transmissions based on prediction-based data reduction algorithms depends on the sensed phenomena, the user requirements, and the architecture used to make the predictions. [PERSON] et al. [11] proposed a taxonomy to show the different data dimensionality reduction techniques. The results gathered by the authors with feature extraction techniques show that the best algorithm may vary from data singularities. Principal Component Analysis (PCA), spectral embedding (SE), and uniform manifold approximation and projection (UMAP) were the extraction techniques that gathered the best results in more than one dataset. Among the feature selection techniques, Random Forest and backward feature elimination achieved a reduction percentage of 60%, and the best feature selection technique is forward feature selection, with a reduction percentage of 68%.