# Data Harmonization for Heterogeneous Datasets: A Systematic Literature Review
## 3 Results
### Which Techniques Are Being Used for Solving the Harmonization Issue for Large Textual Datasets? In previous studies, SSU heterogeneous data were used in the form of text, images, audio, video, and social media formats. The BD and BDA literature reviews proposed so many models and frameworks for data harmonization or integration. Among them, textual data play an important role in semantic, syntactic, and schematic data from large datasets. In different industries, different approaches are used by BD analysts to meet the demands of users and owners. In this section, 16 studies were selected that highlight the core techniques and their contributions in terms of performance, time, and accuracy in data harmonization, data integration, and data fusion. The details of each study are discussed below. At first, [PERSON] [66] found that, in the entertainment industry, the feedback given by the audience in form of large sentences and getting semantic meaning from XML documents is very challenging. Additionally, [PERSON], [PERSON], and [PERSON] [67] pointed out that, by using business intelligence tool sentence-similarity retrieved, the technique proposed for the IT Ecosystem has been adopted by business firms. Apart from that, in the health sector, data are also important for harmonization, as noted by [PERSON] et al. [68]. They also discussed how the dataset contains many clinical codes and how it is difficult to get information and text classification to solve the issue. NLP techniques, such as N-Gram, Jaccard Similarity, Word2 Vec, and different DL approaches, are used to create a paraphrasing dataset from clinical data. Similarly, the research team of [PERSON] et al. [69] revealed, in a clinical-text-classification review that the approaches for textual data play an important role, especially in supervised ML techniques. Likewise, a medical prescription is a document of proof about a patient's health history recorded during the diagnosis, but sometimes it is difficult to understand the semantics of prescribed medicines was presented by [PERSON] et al. [70]. In this study, the Mayo Clinic dataset was utilized with the help of NLP techniques to find the semantic and similarity scores of medical texts. On the contrary, a study was proposed by [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [71] that states that the healthcare communities manage healthcare data on web-based portals but are not available to all medical practitioners. For the prediction of chronic diseases, ML classification algorithms, such as CNN, NB, KNN, and DT, are used for analysis. Besides that, the authors [PERSON] and [PERSON] [72] focused on open-source files-based heterogeneous datasets developed by using Modified IDF cosine similarity for information retrieval. A very detailed and descriptive survey was carried out by the authors [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [73]. In this survey, different datasets of open-source NLP tasks, using different DL methods on BERT models, were discussed to summarize text and word embedding. In addition, [PERSON], [PERSON], and [PERSON] [74] proposed that phrases of NLP models be vectorized by using the phrase2 Vec model to overcome the issues of BoW and preprocessing. In the same way, the authors [PERSON] et al. [75] stated that patient data are very critical and sharing them is possible with high-security algorithms. By using NoSQL, MongoDB, and NLP techniques on XLS, CSV, and TXT files, data acquisition and simulation are possible. Similarly, [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [76] also emphasized that, with the use of advanced technology, the health sector can be upgraded. Furthermore,