توضیحات
ABSTRACT
With the rapid growth of social media, the number of images being uploaded to the internet is exploding. Massive quantities of images are shared through multi-platform services such as Snap chat, Instagram, Facebook and Whats App, recent studies estimate that over 1.8 billion photos are uploaded every day. However, for the most part, applications that make use of this vast data have yet to emerge. Most current image processing applications, designed for small-scale, local computation, do not scale well to web-sized problems with their large requirements for computational resources and storage. The emergence of processing frameworks such as the Hadoop MapReduce platform addresses the problem of providing a system for computationally intensive data processing and distributed storage. However, to learn the technical complexities of developing useful applications using Hadoop requires a large investment of time and experience on the part of the developer. As such, the pool of researchers and programmers with the varied skills to develop applications that can use large sets of images has been limited. To address this we have developed the Hadoop Image Processing Framework, which provides a Hadoop-based library to support large-scale image processing. The main aim of the framework is to allow developers of image processing applications to leverage the Hadoop MapReduce framework without having to master its technical details and introduce an additional source of complexity and error into their programs
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INTRODUCTION
With the spread of social media in recent years, a large amount of image data has been accumulating. When processing this massive data resource has been limited to single computers, computational power and storage ability quickly become bottlenecks. Alternately, processing tasks can typically be performed on a distributed system by dividing the task into several subtasks. The ability to parallelize tasks allows for scalable, efficient execution of resource-intensive applications. The Hadoop MapReduce framework provides a platform for such tasks
Year : 2015
Publisher: IEEE
By : Sridhar Vemula , Christopher Crick
File Information : English Language / 8 Page / Size : 274 K
سال : 2015
ناشر : IEEE
کاری از : Sridhar Vemula , Christopher Crick
اطلاعات فایل : زبان انگلیسی / 8 صفحه /حجم : 274 K
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