We present a method for stable buildup of snow on surfaces of arbitrary topology and geometric complexity. This is achieved by tracing Continue reading
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Image denoising algorithms often assume an additive white Gaussian noise (AWGN) process that is independent of the actual RGB values. Such approaches cannot effectively Continue reading
This paper investigates automated diagnosis of red blood cell’s and describes a method to classify the different shapes of red blood cells using the image processing techniques. The shape of red blood cells can be Continue reading
The set of web pages not reachable using conventional web search engines is usually called the hidden or deep web. One client-side hurdle for crawling the hidden web is Flash files. This project presents a tool for Continue reading
Data analytics plays a significant role in gaining insight of big data that can benefit in decision making and problem solving for various application domains such as science, engineering, and commerce. Cloud computing is a suitable platform for Big Data Analytic Applications (BDAAs) that can greatly reduce application cost by elastically provisioning resources based on user requirements and in a pay as you go model.
BDAAs are typically catered for specific domains and are usually expensive. Moreover, it is difficult to provision resources for BDAAs with fluctuating resource requirements and reduce the resource cost. As a result, BDAAs are mostly used by large enterprises. Therefore, it is necessary to have a general Analytics as a Service (AaaS) platform that can provision BDAAs to users in various domains as consumable services in an easy to use way and at lower price.
To support the AaaS platform, our research focuses on efficiently scheduling Cloud resources for BDAAs to satisfy Quality of Service (QoS) requirements of budget and deadline for data analytic requests and maximize profit for the AaaS platform. We propose an admission control and resource scheduling algorithm, which not only satisfies QoS requirements of requests as guaranteed in Service Level Agreements (SLAs), but also increases the profit for AaaS providers by offering a cost-effective resource scheduling solution.
We propose the architecture and models for the AaaS platform and conduct experiments to evaluate the proposed algorithm. Results show the efficiency of the algorithm in SLA guarantee, profit enhancement, and cost saving.
Source: University of Melbourne
Authors: Mehdi Sookhak | Abdullah Gani | Muhammad Khurram Khan | Rajkumar Buyya
Automation of model building enables new predictive models to be generated in a faster, easier and more straightforward way once new data is available to predict on. Automation can also reduce the demand for tedious bookkeeping that Continue reading
The need to visualise sets of data and networks within a company is a well-known task. In this thesis, research has been done of techniques used to Continue reading
Data mining can be viewed as a result of the natural evolution of information technology. Data mining is the process of discovering interesting patterns and knowledge from large amounts of data. The data sources can Continue reading
Human pluripotent stem cells (hPSC) are important in medicine due to several of their distinctive features. However, genomic abnormalities are Continue reading
This thesis presents high performance forward error correcting codes suitable for random and burst errors. It initially verifies the performance of burst error Continue reading
Over the past few years, there has been an increased interest in automatic facial behavior analysis and understanding. We present OpenFace an open source tool intended for computer vision and machine learning Continue reading