Abstract
In recent years, the radical advancement of technologies has given rise to an abundance of software applications, social media, and smart devices such as smartphone, sensors, and so on. More extensive use of these applications and tools in various industrial domains has led to data deluge, which has fostered enormous challenges and opportunities. However, it is not only the volume of the data but also the speed, variety, and uncertainty, which are promoting a massive challenge for traditional technologies such as data warehouse. These diverse and unprecedented characteristics have engendered the notion of “Big Data.” The data-intensive industries have been experiencing a wide variety of challenges in terms of processing, managing, and analysis of data. For instance, the healthcare sector is confronting difficulties in respect of integration or fusion of diverse medical data stemming from multiple heterogeneous sources. Data integration is critically important within the healthcare sector because it enriches data, enhances its value, and more importantly paves a solid foundation for highly efficient and effective healthcare analytics such as predicting diseases or an outbreak. Several data integration technologies and tools have been developed over the last two decades. This paper aims at studying data integration technologies, tools, and applications within the healthcare domain. Furthermore, this paper discusses future research directions in the integration of Big healthcare data.
Highlights
Healthcare is a highly data-intensive industry [1]
The exponential growth in healthcare data has been forecasted to continue expanding in various forms, such as electronic health records (EHR), patient-reported outcomes, biometric data, medical imaging, biomarker data, wearable devices, and genomic information
BACKGROUND we study different concepts related to Big Healthcare Data integration and provide extensive details about these concepts
Summary
Healthcare is a highly data-intensive industry [1]. The everincreasing trend of healthcare data has already led to a massive growth of the volume. The exponential growth in healthcare data has been forecasted to continue expanding in various forms, such as electronic health records (EHR), patient-reported outcomes, biometric data, medical imaging, biomarker data, wearable devices, and genomic information. We study different concepts related to Big Healthcare Data integration and provide extensive details about these concepts This will help the readers to understand the underlying challenges and techniques of data integration. The concept of Big data has been presented through the 3V model, which refers to high-volume, high velocity, and high-variety information assets [24]. This notion has been extended to a 5V model by including two new ‘‘Vs.’’: Value and Veracity which are incorporated into the Big data definition [25]
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