Anti-Pattern Specification and Correction Recommendations for Semantic Cloud Services

Given the economic and technological advantages \ they offer, cloud services are increasing being offered by \ several cloud providers. However, the lack of standardized \ descriptions of cloud services hinders their discovery. \ In an effort to standardize cloud service descriptions, \ several works propose to use ontologies. Nevertheless, \ the adoption of any of the proposed ontologies \ calls for an evaluation to show its efficiency in cloud \ service discovery. Indeed, the existing cloud providers \ describe, their similar offered services in different ways. \ Thus, various existing works aim at standardizing the \ representation of cloud computing services by proposing \ ontologies. However, since the existing proposals \ were not evaluated, they might be less adopted and considered. \ Indeed, the ontology evaluation has a direct impact \ on its understandability and reusability. In this paper, \ we propose an evaluation approach to validate our \ proposed Cloud Service Ontology (CSO), to guarantee \ an adequate cloud service discovery. To this end, this \ paper has a three-fold contribution. First, we specify a \ set of patterns and anti-patterns in order to evaluate our \ CSO. Second, we define an anti-pattern detection algorithm \ based on SPARQL queries which provides a set of \ correction recommendations to help ontologists revise \ their ontology. Finally, tests were conducted in relation \ to: (i) the algorithm efficiency and (ii) anti-pattern detection \ of design anomalies as well as taxonomic and \ domain errors within CSO

Deployment and Operation of Complex Software in Heterogeneous Execution Environments

This open access book provides an overview of the work developed within the SODALITE project, which aims at facilitating the deployment and operation of distributed software on top of heterogeneous infrastructures, including cloud, HPC and edge resources. The experts participating in the project describe how SODALITE works and how it can be exploited by end users. While multiple languages and tools are available in the literature to support DevOps teams in the automation of deployment and operation steps, still these activities require specific know-how and skills that cannot be found in average teams. The SODALITE framework tackles this problem by offering modelling and smart editing features to allow those we call Application Ops Experts to work without knowing low level details about the adopted, potentially heterogeneous, infrastructures. The framework offers also mechanisms to verify the quality of the defined models, generate the corresponding executable infrastructural code, automatically wrap application components within proper execution containers, orchestrate all activities concerned with deployment and operation of all system components, and support on-the-fly self-adaptation and refactoring

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

A Preliminary Study of Integrating Flipped Classroom strategy for Classical Chinese Learning

[[abstract]]This is a multiphase study which aims to investigate how to provide learners with an method to acquire classical Chinese through integrating mobile technology with the flipped classroom approach. Currently, in the first phase of study, the researcher adopts informant design through questionnaire survey to understand students' and instructors' perceptions of using mobile learning devices for classical Chinese learning, and afterwards the researcher constructs the system based on the pilot results. The pilot questionnaire results, structure of the developed mobile learning system and the practical application of the developed system for classical Chinese teaching and learning are described in the paper.[[notice]]補正完

Container description ontology for CaaS

[EN] Besides its classical three service models (IaaS, PaaS, and SaaS), container as a service (CaaS) has gained significant acceptance. It offers without the difficulty of high-performance challenges of traditional hypervisors deployable applications. As the adoption of containers is increasingly wide spreading, the use of tools to manage them across the infrastructure becomes a vital necessity. In this paper, we propose a conceptualisation of a domain ontology for the container description called CDO. CDO presents, in a detailed and equal manner, the functional and non-functional capabilities of containers, Dockers and container orchestration systems. In addition, we provide a framework that aims at simplifying the container management not only for the users but also for the cloud providers. In fact, this framework serves to populate CDO, help the users to deploy their application on a container orchestration system, and enhance interoperability between the cloud providers by providing migration service for deploying applications among different host platforms. Finally, the CDO effectiveness is demonstrated relying on a real case study on the deployment of a micro-service application over a containerised environment under a set of functional and non-functional requirements.K. Boukadi; M.a Rekik; J. Bernal Bernabe; Lloret, J. (2020). Container description ontology for CaaS. International Journal of Web and Grid Services (Online). 16(4):341-363. https://doi.org/10.1504/IJWGS.2020.11094434136316

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

MapReduce based RDF assisted distributed SVM for high throughput spam filtering

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityElectronic mail has become cast and embedded in our everyday lives. Billions of legitimate emails are sent on a daily basis. The widely established underlying infrastructure, its widespread availability as well as its ease of use have all acted as catalysts to such pervasive proliferation. Unfortunately, the same can be alleged about unsolicited bulk email, or rather spam. Various methods, as well as enabling architectures are available to try to mitigate spam permeation. In this respect, this dissertation compliments existing survey work in this area by contributing an extensive literature review of traditional and emerging spam filtering approaches. Techniques, approaches and architectures employed for spam filtering are appraised, critically assessing respective strengths and weaknesses. Velocity, volume and variety are key characteristics of the spam challenge. MapReduce (M/R) has become increasingly popular as an Internet scale, data intensive processing platform. In the context of machine learning based spam filter training, support vector machine (SVM) based techniques have been proven effective. SVM training is however a computationally intensive process. In this dissertation, a M/R based distributed SVM algorithm for scalable spam filter training, designated MRSMO, is presented. By distributing and processing subsets of the training data across multiple participating computing nodes, the distributed SVM reduces spam filter training time significantly. To mitigate the accuracy degradation introduced by the adopted approach, a Resource Description Framework (RDF) based feedback loop is evaluated. Experimental results demonstrate that this improves the accuracy levels of the distributed SVM beyond the original sequential counterpart. Effectively exploiting large scale, ‘Cloud’ based, heterogeneous processing capabilities for M/R in what can be considered a non-deterministic environment requires the consideration of a number of perspectives. In this work, gSched, a Hadoop M/R based, heterogeneous aware task to node matching and allocation scheme is designed. Using MRSMO as a baseline, experimental evaluation indicates that gSched improves on the performance of the out-of-the box Hadoop counterpart in a typical Cloud based infrastructure. The focal contribution to knowledge is a scalable, heterogeneous infrastructure and machine learning based spam filtering scheme, able to capitalize on collaborative accuracy improvements through RDF based, end user feedback. MapReduce based RDF Assisted Distributed SVM for High Throughput Spam Filterin

Products and Services

Today’s global economy offers more opportunities, but is also more complex and competitive than ever before. This fact leads to a wide range of research activity in different fields of interest, especially in the so-called high-tech sectors. This book is a result of widespread research and development activity from many researchers worldwide, covering the aspects of development activities in general, as well as various aspects of the practical application of knowledge

Computer Science's Digest Volume 1

This series is dedicated to the students of the Systems Department, to give them reading material related to computer science in a second language. This book covers the Introduction to Computer Science, Computer Communications, Networking and Web Applications