Abstract-Advances in information technology and its widespread growth in several areas
Summary Hadoop distributed file system (HDFS) and MapReduce model have become popular technologies for large‐scale data organization and analysis. Existing model of data organization and processing in Hadoop using HDFS and MapReduce are ideally tailored for search and data parallel applications, for which there is no need of data dependency with its neighboring/adjacent data. However, many scientific applications such as image mining, data mining, knowledge data mining, and satellite image processing are dependent on adjacent data for processing and analysis. In this paper, we identify the requirements of the overlapped data organization and propose a two‐phase extension to HDFS and MapReduce programming model, called XHAMI, to address them. The extended interfaces are presented as APIs and implemented in the context of image processing application domain. We demonstrated effectiveness of XHAMI through case studies of image processing functions along with the results. Although XHAMI has little overhead in data storage and input/output operations, it greatly enhances the system performance and simplifies the application development process. Our proposed system, XHAMI, works without any changes for the existing MapReduce models and can be utilized by many applications where there is a requirement of overlapped data. Copyright © 2016 John Wiley & Sons, Ltd.
Abstract-Cloud computing is a promising cost efficient service oriented computing platform in the fields of science, engineering, business and social networking for delivering the resources on demand. Big Data Clouds is a new generation data analytics platform using Cloud computing as a back end technologies, for information mining, knowledge discovery and decision making based on statistical and empirical tools. MapReduce scheduling models for Big Data computing operate in the cluster mode, where the data nodes are pre-configured with the computing facility for processing. These MapReduce models are based on compute push model-pushing the logic to the data node for analysis, which is primarily for minimizing or eliminating data migration overheads between computing resources and data nodes. Such models, however, substantially perform well in the cluster setups, but are infelicitous for the platforms having the decoupled data storage and computing resources. In this paper, we propose a Genetic Algorithm based scheduler for such Big Data Cloud where decoupled computational and data services are offered as services. The approach is based on evolutionary methods focussed on data dependencies, computational resources and effective utilization of bandwidth thus achieving higher throughputs.
<div>Deep learning techniques are very prominent in processing remotely sensed synthetic aperture radar (SAR) images for real-time, high-impact applications, such as image classification, object detection, and semantic segmentation. The accuracy of deep learning models, such as convolutional neural networks (CNNs), depends on the quality of the input data. Compared to the model-centric approach, where the model parameters are optimized during training, the data-centric approach can enhance the performance accuracy as data quality is improved before training the models. Improving the data quality of SAR images is challenging as SAR image properties are different from optical (OPT) images. Image fusion techniques proved to enhance the quality of SAR images when combined with OPT images. Many fusion techniques exist for combining SAR and OPT images in the classical domain. This paper proposes a novel approach to using quantum computing for the image fusion of SAR and OPT images. Eight different quantum processing techniques are used for the fusion of the images. We designed and created a dataset for land-use classification by collecting data using the Google Earth Engine. The quality metric measurements show that the quality of SAR images has improved by using the proposed quantum processing techniques. In addition, performance evaluation of the deep learning CNNs on the dataset was carried out for all quantum processing techniques. Our approach improved the classification accuracy from 82.64%, with only SAR images for training, to 95.36% using the proposed image fusion techniques.</div>
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