# Data Harmonization for Heterogeneous Datasets: A Systematic Literature Review
## 3 Results
### How Does the Data Harmonization Resolve the Issues of Heterogeneity? In this section, 25 studies were selected which discuss data harmonization, data integration, and data fusion. The details of each study are discussed below. Initially, heterogeneous oil and gas data are unorganized, which is difficult to manage. For that data harmonization was proposed by [PERSON] and [PERSON] [43], using semantic web and BD tools. Where performance of the precision, recall, and F-score found better than existing techniques. In addition, agriculture data are stored in clusters, and it is difficult to handle heterogeneous data. Therefore, a uniform format was reported by ([PERSON], [PERSON], and [PERSON] [44], using Couchbase and NoSQL, and it was found that the time duration for fetching records is fast. Apart from this, different frameworks have been developed by different organizations to make decisions, but no framework has been proposed for value creation. In this study, [PERSON] and [PERSON] [45] created a framework for value creation from SSU data also in-depth issues of heterogeneity, harmonization, and BD techniques were highlighted. It shows the importance of data integration for industrial data, decisions, product reviews, and visualization of future strategies. Artificial intelligence, ML, and cloud computing will be helpful for BD Analysts. Moreover, [PERSON], [PERSON], and [PERSON] [46] summarized that the heterogeneous data in industry are produced easily but are difficult to store, manage, and audit. In this study, the issue of heterogeneity of large firms was solved using a NoSQL-based data integration model. Furthermore, health data are very important for patient treatment, monitoring, and satisfaction. Health data are generated by all institutes by using open-source web data, but no such online module has been proposed for integration of all web-based centralized. In their study, [PERSON], [PERSON], et al. [47] revealed a Web-based FHIR visualization tool, using a standard structured format API. Again, [PERSON], [PERSON], and [PERSON] [48] described that the file sharing between users was difficult for heterogeneous data. Therefore, a real-time integration and interoperability model was developed by using PostgreSQL to facilitate different users. In addition, [PERSON], [PERSON], [PERSON], and [PERSON] [49] mentioned that the child-patient disorder/condition data were complex and unmanageable due to manual work and human involvement. To overcome this issue, different preprocessing, NLP, and ML tools are used to create patient data in digital form and without any biases. The performance of the autism spectrum is calculated using precision and recall. Furthermore, [PERSON] [50] also emphasized on the issues and challenges faced by educational institutes and researchers are highlighted, such as data integration and sharing between campuses and branches. Besides this, text-free or unstructured data in healthcare data create issues for managing and storing. Therefore, data fusion was suggested by the [PERSON] et al. [10] to manage the heterogeneous data. Moreover, in an online learning system, data need to be integrated and efficient for smart educational systems. Data processing and storage of audio, video, images, and text formats was developed by [PERSON], [PERSON], [PERSON], and [PERSON] [51] with the help of Hadoop, MapReduce, and Spark. As a result, it helps in taking a smart decision within seconds. Additionally, [PERSON] and [PERSON] [52] explained the various issues of data harmonization in this survey. Before that, data warehousing and OLAP were used, which do not support huge datasets of open source and unstructured formats. In the end, different BD and ML techniques are suggested for dealing with huge data. Consequently, in the oil and gas industry, data are generated in operational formats from different clusters at a time, which needs data integration to collect data in a centralized place for making timely decisions identified by [PERSON], [PERSON], and [PERSON] [53].