# Data Harmonization for Heterogeneous Datasets: A Systematic Literature Review
## 3 Results
### Which of the Domains Are Mainly Focused on Researching Heterogeneity?
In addition, the prominent authors [PERSON] and [PERSON] [5] contributed with their work by proposing that the handling of large and complex data creates the problem of heterogeneity and is solved by using different BD techniques and text-mining algorithms. The conversion of unstructured into structured format performance is calculated in time. Moreover, real-time healthcare data are generated from sensors and gadgets in unstructured formats, which is not possible in structured formats with simple tools. [PERSON], [PERSON], and [PERSON] [40] stated in their work that healthcare data need to be monitored, visualized, and predicted time by time for these BD techniques, such as Hadoop and MapReduce, used for developing the EHR. Moreover, for the development of heterogeneity, [PERSON] et al. [41] highlighted the use of large-scale urban multisource heterogeneous data integrated by using tensor decomposition to solve the issues of urban town and to make it a smart town. Additionally, [PERSON], [PERSON], and [PERSON] [42] believe that the heterogeneity and complexity are two main issues highlighted for the solution and integration of clinical data. The data are generated in different formats, such as RDF, XML, and images. The issues were solved by using ontology and semantic techniques. Recently, the authors [PERSON] and [PERSON] [3] emphasized that the various frameworks of heterogeneity and heterogeneous datasets can be used for solving the issues of heterogeneity, and it was shown that heterogeneity is always due to the unusual format of data and lack of integration resources, expertise, and techniques. Table 6 shows the advantages and disadvantages of studies selected for Research Question 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study Reference** & **Domain** & **Advantages** & **Disadvantages** \\ \hline
[31] & Healthcare & Millions of E HR combined & Limited data access to selected smallest patient populations \\
[32] & General Purpose & diverse, massive, and complex data & Requires new norm of integration \\
[33] & Text similarity & Helps in classifying similar objects. & It is considered inconsistent and ad-hoc. \\
[34] & General Purpose & The natural property of BD. & variety of inconsistent data create problems \\
[35] & InformationRetrieval & Utilize data from a variety of sources. & Loss of information due to semantic, syntactic, and schematic difference \\
[36] & Healthcare & Electronic health record became unique & Difficult to manage different form \\
[37] & General Purpose & DL helps in solving the integration of heterogeneous data & Conflicting information shared by resources \\
[38] & Information Retrieval & Efficient data extraction & Meaningful data extraction from the \\
[39] & Education & Helps in distribution across clusters & Need More memory for processing \\
[5] & Healthcare & Need to manage data efficiently and & Need to be distributed and parallel \\
[40] & Healthcare & collaborative way & computing systems and database \\
[41] & Healthcare & Multiple different sources data with a unified view & Data processing Speed and quality of data analytics \\
[41] & Infrastructure & Extremely large multisource infrastructure & Some models are only indirectly relevant to \\  & containing vehicles, residents, and smart card & a particular phenomenon of interest \\
[42] & Healthcare & Semantic-based integration and & Clinical records without linguistic standard \\
[3] & General Purpose & Heterogeneous data can be solved by using & Performance of all heterogeneous data is \\  & & RDBMS, concept lattice, and MapReduce & not calculated \\ \hline \hline \end{tabular}
\end{table} 
Table 6: RQ1 domains’ advantages and disadvantages.