## 2 Data-Cleaning Applications  
The volume of data collected nowadays is vastly increasing, and since most data acquired are polluted, the dependability of the data is declining. Various data-cleaning methodologies are available to rectify this issue, but data cleansing remains difficult when working with large data requirements. Data cleaning, also known as data cleansing, is no longer a recent area of research. It aims to increase data quality by detecting and eliminating errors and inconsistencies [11]. As of now, there are two classifications of data cleansing: traditional data cleansing and data cleansing for big data. Traditional data cleansing techniques are so called because it is not used to manage massive volumes of data, such as Potter's Wheel and Intelliclean [12]. Meanwhile, the techniques in Table 1 such as Cleanix [13], SCARE [14], KATARA [15], and BigDansing [16] are developed specifically for big data. Regarding the emerging trends in data-cleaning techniques, one of the new challenges that researchers are about to face is scalability [17]. One of the perennial problems in data analytics is identifying and restoring dirty data, and failure to do so will result in faulty analytics and unreliable decisions. New abstractions and scalability are among the various facets of this issue and are considered when developing data-cleaning methods to cope with the amount and diversity of data [13; 14; 16]--see Table 2. Given the significant amount of data, it needs time to be processed to be suitable for big data analysis and decision making. The data's volume, veracity, and velocity must also be considered when analysing the proposed approaches; however, the researchers mentioned that \"Data analytics is not about having the information known, but about discovering the predictive power behind the data collected\" [12]. Cleanix, SCARE, and BigDansing focus on the scalability issue in the data cleansing process. Moreover, SCARE and BigDansing do not require any human-domain expert in the cleansing process. SCARE needs an extensive set of rules to update the dataset; however, no expert is present in the process. Nevertheless, the process is expensive, and if the authorities fail to identify correct fixes for the dirty dataset, it will result in redundancy of the training data and a threshold machine learning parameter that is hard to set precisely [15]. Furthermore, BigDansing also requires a set of data-quality rules for optimisation of the cleansing process that requires too many regulations to calibrate and put into place before the start of the cleaning process, as shown in Table 2 below; however, it needs no human-domain expertise to monitor the whole process, although adjusting such parameters is crucial in maintaining the essence of the information in the datasets [12]. These data-cleansing techniques support various data-cleaning tasks such as abnormal value detection and correction, incomplete data filling, de-duplication and conflict resolution (Cleanix), value modification (SCARED), the identification of correct and incorrect data, and the generation of top-k possible repairs for inaccurate data (KATARA) and rules into a series of transformations that enable distributed computations and several optimisations (BigDansing). There are two kinds of dirty data: erroneous and noisy--see Figure 1. The current big-data-cleansing techniques--Cleanix [13], SCARE [14], KATARA [15], and BigDansing [16]--are focused mostly on erroneous dirty data such as a solution in duplicate entries, missing values, wrong values, and wrong formats. In medical applications, these big-data-cleansing techniques play a critical role in medical record management in medical facilities; however, they do not address noisy data acquired via biomedical sensors.