Ontology for Data Analytics

Abstract

Nowadays, with the development of technology, storage, processing, and information extraction of data have become important. Thus, while a system is generated, it should be designed in such a way that works in harmony with each of these data processing steps. Data analytics is one of the methods used to develop such a system. Data analytics applications are used in many different areas such as increasing market shares of a firm, customer behavior analysis, predicting the life of an electronic device, detection of the anomaly on a network, social network analysis, healthcare systems, chemical component interactions, and bank operations. These data analytics applications can obtain data from different sources, and these sources must interact with each other. It is not always easy to design this interactive architecture. These difficulties can be overcome by using ontologies. For data analytics, ontologies can be used for facilitating data collection, improving the quality of the data used, analyzing data, showing the obtained results, and ensuring the reusability of the designed system. In this study, we introduce an overview of data analytics and explain data analytics steps; in addition, we seek to answer the question of how to enrich and improve a data analytics system by using ontologies. We give different examples of how to use ontologies in different steps in the systems. Moreover, we emphasize the pros and cons of using ontologies in data analytics. We then discuss the future outlook for these ontologies for data analytics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. All rights reserved.