6 research outputs found
Cloud service discovery and analysis: a unified framework
Over the past few years, cloud computing has been more and more attractive as a new
computing paradigm due to high flexibility for provisioning on-demand computing
resources that are used as services through the Internet. The issues around cloud service
discovery have considered by many researchers in the recent years. However,
in cloud computing, with the highly dynamic, distributed, the lack of standardized
description languages, diverse services offered at different levels and non-transparent
nature of cloud services, this research area has gained a significant attention. Robust
cloud service discovery approaches will assist the promotion and growth of cloud
service customers and providers, but will also provide a meaningful contribution to
the acceptance and development of cloud computing. In this dissertation, we have
proposed an automated cloud service discovery approach of cloud services. We have
also conducted extensive experiments to validate our proposed approach. The results
demonstrate the applicability of our approach and its capability of effectively identifying
and categorizing cloud services on the Internet. Firstly, we develop a novel
approach to build cloud service ontology. Cloud service ontology initially is built
based on the National Institute of Standards and Technology (NIST) cloud computing
standard. Then, we add new concepts to ontology by automatically analyzing real
cloud services based on cloud service ontology Algorithm. We also propose cloud
service categorization that use Term Frequency to weigh cloud service ontology concepts
and calculate cosine similarity to measure the similarity between cloud services.
The cloud service categorization algorithm is able to categorize cloud services to clusters for effective categorization of cloud services. In addition, we use Machine
Learning techniques to identify cloud service in real environment. Our cloud service
identifier is built by utilizing cloud service features extracted from the real cloud service
providers. We determine several features such as similarity function, semantic
ontology, cloud service description and cloud services components, to be used effectively
in identifying cloud service on the Web. Also, we build a unified model to
expose the cloud service’s features to a cloud service search user to ease the process of
searching and comparison among a large amount of cloud services by building cloud
service’s profile. Furthermore, we particularly develop a cloud service discovery Engine
that has capability to crawl the Web automatically and collect cloud services.
The collected datasets include meta-data of nearly 7,500 real-world cloud services
providers and nearly 15,000 services (2.45GB). The experimental results show that
our approach i) is able to effectively build automatic cloud service ontology, ii) is
robust in identifying cloud service in real environment and iii) is more scalable in
providing more details about cloud services.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 201
Toward Unified Cloud Service Discovery for Enhanced Service Identification
Nowadays cloud services are being increasingly used by professionals. A wide variety of cloud services are being introduced every day, and each of which is designed to serve a set of specific purposes. Currently, there is no cloud service specific search engine or a comprehensive directory that is available online. Therefore, cloud service customers mainly select cloud services based on the word of mouth, which is of low accuracy and lacks expressiveness. In this paper, we propose a comprehensive cloud service search engine to enable users to perform personalized search based on certain criteria including their own intention of use, cost and the features provided. Specifically, our cloud service search engine focuses on: 1) extracting and identifying cloud services automatically from the Web; 2) building a unified model to represent the cloud service features; and 3) prototyping a search engine for online cloud services. To this end, we propose a novel Service Detection and Tracking (SDT) model for modeling Cloud services. Then based on the SDT model, a cloud service search engine (CSSE) is implemented for helping effectively discover cloud services, relevant service features and service costs that are provided by the cloud service providers
Tourist Mobility Patterns: Faster R-CNN Versus YOLOv7 for Places of Interest Detection
The mobility of tourists plays a significant role in shaping their travel experiences and the overall dynamics of a destination. In recent years, the proliferation of social media platforms has provided a rich source of visual data, allowing us to leverage the abundance of pictures shared by tourists to extract meaningful information. Using computer vision techniques and deep learning algorithms, such as object detection, it becomes possible to extract useful information from tourist pictures. In this study, we look for the best way to detect objects from pictures shared by tourists during their journey in order to determine their locations. To achieve our goal we propose a new methodology composed by; database creation, database annotation, preprocessing, deep learning implementation and evaluation. We implemented two deep learning object detection methods: YOLOv7 and Faster R-CNN. A dataset has been created to provide examples of training and testing for neuronal networks. The training was performed on various basic models, in order to increase the efficiency of the training time and to compare the results. We evaluated the results using three parameters: precision, recall and mAP. The results indicate that YOLOv7 has the precision and performance, with over 90 % mAP, 92.1 % precision and 92.7 % recall
Toward Unified Cloud Service Discovery for Enhanced Service Identification
Nowadays cloud services are being increasingly used by professionals. A wide variety of cloud services are being introduced every day, and each of which is designed to serve a set of specific purposes. Currently, there is no cloud service specific search engine or a comprehensive directory that is available online. Therefore, cloud service customers mainly select cloud services based on the word of mouth, which is of low accuracy and lacks expressiveness. In this paper, we propose a comprehensive cloud service search engine to enable users to perform personalized search based on certain criteria including their own intention of use, cost and the features provided. Specifically, our cloud service search engine focuses on: (1) extracting and identifying cloud services automatically from the Web; (2) building a unified model to represent the cloud service features; and (3) prototyping a search engine for online cloud services. To this end, we propose a novel Service Detection and Tracking (SDT) model for modeling Cloud services. Then based on the SDT model, a cloud service search engine (CSSE) is implemented for helping effectively discover cloud services, relevant service features and service costs that are provided by the cloud service providers