# A Framework for Enhancing Big Data Integration in Biological Domain Using Distributed Processing
###### Abstract  
Massive heterogeneous big data residing at different sites with various types and formats need to be integrated into a single unified view before starting data mining processes. Furthermore, in most of applications and research, a single big data source is not enough to complete the analysis and achieve goals. Unfortunately, there is no general or standardized integration process; the nature of an integration process depends on the data type, domain, and integration purpose. Based on these parameters, we proposed, implemented, and tested a big data integration framework that integrates big data in the biology domain, based on the domain ontology and using distributed processing. The integration resulted in the same result as that obtained from the local integration. The results are equivalent in terms of the ontology size before the integration; in the number of added items, skipped items, and overlapped items; in the ontology size after the integration; and in the number of edges, vertices, and roots. The results also do not violate any logical consistency rules, passing all the logical consistency tests, such as Jena Ontology API, HermiT, and Pellet reasoners. The integration result is a new big data source that combines big data from several critical sources in the biology domain and transforms it into one unified format to help researchers and specialists use it for further research and analysis. big data; big data integration; biological big data; ontology integration; distributed integration 2020 ac  
data. Source diversity means data are machine-generated, human-generated, or process-generated. Finally, processing diversity represents the data processing types, namely batch processing, stream processing, interactive processing, or graph processing. Data integration is the combination of data from several different resources to build a united data view [5]. There are several data integration architectures; most systems fall in between data warehousing (DW) and virtual data integration (VDI) [6]. In DW, data from several sources are collected and stored in a single physical data source where queries are answered. In VDI, data remain in their sources and are accessed at query time. Traditional data warehouses are not efficient for big data integration (BDI) [7] due to big data characteristics; it has an enormous number of datasets, which are heterogeneous, dynamic, and have different qualities [8]. Big data integration can be in batch integration or real-time integration. Batch data integration is used when data is grouped by the source and transformed periodically to the target. Real-time data integration is used if data should be sent immediately from the source to complete a particular task [9]. Few studies so far have used the upper layer ontology or domain ontology to improve the semantic integration that is essential to make big data standardized, reusable, and scalable. Still, they have some drawbacks in their solutions that have affected the quality of big data integration. To accomplish the integration process using ontologies, previous research used different methodologies, including semantic rule-based integration, standard semantic similarity measures, or other approaches. Accordingly, we proposed a new semantic big data integration framework that uses the domain ontology based on the distributed processing system to integrate big data on the biology domain. The main goal from the integration and distributed processing is to serve the research community with a new unified source of big data in the biology domain. In addition, to be able to calculate the semantic similarity measures (SSM) of gene pairs from different data sources by the best semantic similarity measures, which only worked easily on a single ontology. In our proposed distributed processing approach, there is no need for very high-performance computers to load the global ontology and calculate the similarity between any gene pairs.