# Data Harmonization for Heterogeneous Datasets: A Systematic Literature Review
## 1 Introduction  
Big Data play a vital role in the assessment of massive data produced every second by real-world applications, using tools and algorithms [1]. Some of real-life application domains of Big Data are healthcare, telecommunication, financial firms, retail, law enforcement, marketing, new product development, banking, energy and utilities, insurance, education, agriculture, and urban planning, as discussed in Reference [2]. Nowadays, data are being produced in various formats, ranging from structured and semi-structured to unstructured (SSU) generated from heterogeneous resources [3, 4]. The disparate nature of data cannot be processed with simple tools and techniques [2, 5], and this creates a challenge for decision-makers to make decisions based on the scattered data. Emergingtechnologies, such as the Internet of Things (IoT), Industry 4.0 (I4.0), and extended reality (XR), produce distinct kinds of information via heterogeneous sources and real-world applications that create heterogeneity issues [6], in IoT integration, security, analytics challenges, and computational time [7; 8; 9]. Among them, data harmonization (DH), which describes the uniform representation of heterogeneous data, was proposed in References [10; 11]. IoT is a system that deals with interrelated computing objects, such as unique tags, RFID, or machine interactions, and that can transfer data without human and machine involvement [12]. As technology evolves, the IoT has further grown into the Industrial IoT (IIoT), which deals with heterogeneous data produced by real-world applications, industrial products, and devices, such as privacy authentication logs of IIoT devices [13], business architecture devices data [14], and heterogeneous IIoT devices data [6]. In addition, I4.0 deals with IoT-based automation, technologies, and decision-making that help decision-makers to make decisions based on the disparate nature of data produced [15]. Applications of I4.0 in higher education, predictive maintenance [16; 17], food logistics [18], knowledge management [19], business [20], and supply chain [21]. The main problem faced by these applications is related to managing the heterogeneous data produced in bulk by employing I4.0 and IIoT. The data produced by industries include digital data for manufacturing purposes, unstructured data for predictive maintenance, customer data for food logistics, customer reviews for knowledge management, business data for the supply chain, and manufacturing data for the supply chain. XR deals with the real and virtual environment with the help of a machine and human interaction [22]. XR is improving heterogeneous manufacturing data in the digital world. The tools must be advanced so that user acceptance and better usability of products are achieved [23]. AI can be effectively used to address the disparate nature of manufacturing data to deliver the best appearance to the XR industry [24]. To resolve the problems mentioned earlier, the disparity of data needs to be reviewed in detail, so that data harmonization models, tools, techniques, algorithms, and their performance can be evaluated for extensive heterogeneous textual information. Although related work was carried out in multimodalities for text, image, audio, and video [25; 26; 27], there were no such studies highlighting the work associated with textual data, data harmonization core techniques, and performance measurement. Multiple studies have been conducted which deal with applications such as sentiment analysis, text similarity, word embedding, and emotion recognition in conjunction with the help of classification and clustering techniques. Therefore, solving real-world application problems, such as those of a medical and healthcare nature, needs data to be harmonized and uniformly presented, so that decisions can be carried out efficiently.