مقاله انگلیسی رایگان در مورد استفاده از داده های توزیع شده بر روی HBase – هینداوی 2018

Large datasets have been in existence, continuously, for hundreds of years, beginning in the Renaissance Era when researchers began to archive measurements, pictures, and documents to discover fundamental truths in nature [1–4]. The term “Big Data” was introduced in 2000 by Francis Diebold, an economist at the University of Pennsylvania, and became popular when IBM and Oracle adopted it in 2010 and thereafter in healthcare [5]. Gantz and Reinsel [6] predicted in their “The Digital Universe” study that the digital data created and consumed per year will reach 40,000 Exabyte by 2020, from which a third will be processed using Big Data technologies. Big Data has been characterized in several ways: as NoSQL key-indexed [7, 8], unstructured [9] computer interpretations, text, information-based [10], and so on. With this in mind, Big Data Analytics (BDA) in healthcare requires a more comprehensive approach than traditional data mining; it calls for a unified methodology to validate new technologies that can accommodate the velocity, veracity, and volume capacities needed to facilitate the discovery of information across all healthcare data types of healthcare domains [11]. There are many recent studies of BDAs in healthcare defined according to many technologies used, like Hadoop/MapReduce [12, 13]. BDA itself is the process used to extract knowledge from sets of Big Data [14]. The life sciences and biomedical informatics have been among the fields most active in conducting BDA research [15]. Kayyali et al. [16] estimated that the application of BDA to the US healthcare system could save more than $300 billion annually. Clinical operations and research and development are the two largest areas for potential savings: $165 billion and $108 billion, respectively [17]. Research has focused mainly on the size and complexity of healthcare-related datasets, which includes personal medical records, radiology images, clinical trial data submissions, population data, and human genomic sequences (Table 1). Information-intensive technologies, such as 3D imaging, genomic sequencing, and biometric sensor readings, are helping fuel the exponential growth of healthcare databases [12, 18]. Furthermore, the use of Big Data in healthcare presents several challenges. The first challenge is to select appropriate statistical and computational method(s). The second is to extract meaningful information for meaningful use.The third is to find ways of facilitating information access and sharing. A fourth challenge is data reuse, insofar as “massive amounts of data are commonly collected without an immediate business case, but simply because it is affordable” [19]. Finally, another challenge is false knowledge discovery: “exploratory results emerging from Big Data are no less likely to be false” [5] than reporting from known datasets. In cancer registries, for example, biomedical data are now being generated at a speed much faster than researchers can keep up with using traditional methods [20].