@article {Sabri2021888,
	title = {Cloud Computing in Remote Sensing: Big Data Remote Sensing Knowledge Discovery and Information Analysis},
	journal = {International Journal of Advanced Computer Science and Applications},
	volume = {12},
	number = {5},
	year = {2021},
	note = {cited By 1},
	pages = {888-895},
	abstract = {With the rapid development of remote sensing technology, our ability to obtain remote sensing data has been improved to an unprecedented level. We have entered an era of big data. Remote sensing data clear showing the characteristics of Big Data such as hyper spectral, high spatial resolution, and high time resolution, thus, resulting in a significant increase in the volume, variety, velocity and veracity of data. This paper proposes a feature supporting, salable, and efficient data cube for time-series analysis application, and used the spatial feature data and remote sensing data for comparative study of the water cover and vegetation change.The spatial-feature remote sensing data cube (SRSDC) is described in this paper. It is a data cube whose goal is to provide a spatial-feature-supported, efficient, and scalable multidimensional data analysis system to handle largescale RS data. It provides a high-level architectural overview of the SRSDC.The SRSDC offers spatial feature repositories for storing and managing vector feature data, as well as feature translation for converting spatial feature information to query operations.The paper describes the design and implementation of a feature data cube and distributed execution engine in the SRSDC. It uses the long time-series remote sensing production process and analysis as examples to evaluate the performance of a feature data cube and distributed execution engine. Big data has become a strategic highland in the knowledge economy as a new strategic resource for humans. The core knowledge discovery methods include supervised learning methods data analysis supervised learning, unsupervised learning methods data analysis unsupervised learning, and their combinations and variants. {\textcopyright} 2021. All Rights Reserved.},
	keywords = {Big data, Cloud computing, Cloud-computing, Data cube, Data distributed, Data integration, Data mining, Engines, Execution engine, Feature data, Geometry, Methods. Data analysis, Remote sensing, Remote sensing data, Remote sensing technology, Remote-sensing, Spatial features, Supervised learning, Time series analysis, Unsupervised learning},
	doi = {10.14569/IJACSA.2021.01205104},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107802131\&doi=10.14569\%2fIJACSA.2021.01205104\&partnerID=40\&md5=4251cd5707097684639ead5c44f44bec},
	author = {Sabri, Y. and Bahja, F. and Siham, A. and Maizate, A.}
}
@article {AitTemghart2021335,
	title = {Game Theoretic Approaches to Mitigate Cloud Security Risks: An Initial Insight},
	journal = {Lecture Notes in Business Information Processing},
	volume = {416 LNBIP},
	year = {2021},
	note = {cited By 1},
	pages = {335-347},
	abstract = {Cloud computing is one of the most promising innovations impacting data storage and processing. And with it, clients rely on the IT solutions offered by an external provider instead of on-premise applications. Despite its enormous impacts, customers are still reluctant to outsource their business processes because of security concerns. As data is typically stored and governed by cloud vendors, users need to deal with security issues linked to the loss of control over their sensitive data. Cloud providers need to implement the appropriate security measures that might attract more clients while making the minimum investment. While used in various disciplines, game theory has recently expanded to investigate the effect of the defenders{\textquoteright} and attackers{\textquoteright} behaviors on strategic decision-making. This study aims to develop insights into how game theory can develop better security policies in cloud computing. First, we perform threat modeling to identify the potential threats facing cloud. Second, we identify the limitations of existing game based solutions and then suggest an improved model define an adequate strategy that would figure out the right balance between the required security level and the profit margins. Besides, we present future directions that can be explored to build highly reliable and optimal strategies for cloud services. {\textcopyright} 2021, Springer Nature Switzerland AG.},
	keywords = {Business Process, Cloud computing, Cloud securities, Computation theory, Computer games, Data privacy, Decision making, Decision theory, Digital storage, Game theory, Information analysis, Optimal strategies, Potential threats, Security issues, Security measure, Security policy, Strategic decision making},
	doi = {10.1007/978-3-030-76508-8_24},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111082922\&doi=10.1007\%2f978-3-030-76508-8_24\&partnerID=40\&md5=1619b858093bfb251eb24a3209534772},
	author = {Ait Temghart, A. and Ait Omar, D. and Outanoute, M. and Marwan, M.}
}
@article {AitTemghart2021307,
	title = {Optimal Virtual Machine Provisioning in Cloud Computing Using Game Theory},
	journal = {Lecture Notes in Business Information Processing},
	volume = {416 LNBIP},
	year = {2021},
	note = {cited By 1},
	pages = {307-321},
	abstract = {Virtualization of resources on the cloud computing allow fast and easy scaling of infrastructure to achieve a specified QoS. This will undoubtedly improve the allocation efficiency of physical machines in large-scale data centers. In fact, virtualization technologies not only have a direct effect on the performance and energy consumption but also can reduce the operating costs considerably. As there is fierce competition among cloud providers, there is an urgent need to adopt an elastic resource management system and an optimal pricing strategy as well. Such a strategy is designed principally to satisfy the requirements of a given service and simultaneously increase its associated profit margins. This study highlights why game theory is a very useful tool to analyze the impact of both energy efficiency and response time on virtual machine (VM) provisioning. To this aim, we present a formal policy that ensures fairness in the resource sharing and also takes into account the reasonable expectations market participants. More precisely, we rely on a non-cooperative game model in choosing the right provisioning scheme, and then discuss its equilibrium and stability. In a duopoly market, we conduct some numerical experiments to illustrate the efficiency and performance of the proposed approach and its proof of convergence with a certain number of iterations. {\textcopyright} 2021, Springer Nature Switzerland AG.},
	keywords = {Allocation efficiencies, Cloud computing, Commerce, Computation theory, Computer games, Efficiency and performance, energy efficiency, Energy utilization, Game theory, Information analysis, Market participants, Network security, Number of iterations, Numerical experiments, Operating costs, Provisioning schemes, Resource management systems, Virtual machine, Virtualization, Virtualization technologies},
	doi = {10.1007/978-3-030-76508-8_22},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111167293\&doi=10.1007\%2f978-3-030-76508-8_22\&partnerID=40\&md5=548cfc7c5207af40074c10c8be8d63a7},
	author = {Ait Temghart, A. and Ait Omar, D. and Marwan, M.}
}
@conference {Ouarhim2020,
	title = {Business Process Modeling Notation Extension for Real Time Handling - Application to Novel Coronavirus (2019-nCoV) management process},
	booktitle = {Proceedings of 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications, CloudTech 2020},
	year = {2020},
	note = {cited By 0},
	abstract = {The need of real-time enterprises increases according to our life style and customers{\textquoteright} satisfaction goal. Real-time business process is one of the important components of a real-time enterprise. For that, a formal definition of real-time concept and real-time process was indispensable. This work presents a formal definition of real-time concept, real-time ontology and a proposition of a real-time component with real-time attributes. This new dimension of time contains three components: latency time, acceptance interval and theoretical time. This definition gives a new vision of time, not just an ordinary vision as periods and calendar but as a response to our needs in real-time enterprises: real-time. Before the proposition of a real-time component for BPMN language, which we applicate to Novel Coronavirus (2019-nCoV) management process, as case study. {\textcopyright} 2020 IEEE.},
	keywords = {Artificial intelligence, Business process modeling notations, Cloud computing, Customer satisfaction, Customers{\textquoteright} satisfaction, Formal definition, Management process, Real-time business, Real-time handling, Real-time process, Three component},
	doi = {10.1109/CloudTech49835.2020.9365900},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102630158\&doi=10.1109\%2fCloudTech49835.2020.9365900\&partnerID=40\&md5=efa760e4fe29ae0509729e730f662b56},
	author = {Ouarhim, A. and Lakhrouit, J. and Baina, K.}
}
@conference {Hafdi2020,
	title = {Formal Modeling and Validation of Micro Smart Grids Based on ReDy Architecture},
	booktitle = {Proceedings of 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications, CloudTech 2020},
	year = {2020},
	note = {cited By 0},
	abstract = {Several cities in the world are moving from traditional power grid to Smart Grids. In order to set up Smart Grids, we should be able to face many challenges related to reliability, scalability, dynamism, technological solutions, security, etc. In this paper, we propose a case study where we model a micro Smart Grid according to the ReDy architecture, which is intended for IoT applications. The ReDy architecture provides a base to implement a scalable, reliable, and dynamic IoT network ready to meet Smart Grid needs. In order to prove those requirements, we opted for formal modeling and validation approach using model checking techniques. This formal analysis is carried out using the CADP toolbox. {\textcopyright} 2020 IEEE.},
	keywords = {Artificial intelligence, Binary alloys, Cloud computing, Dysprosium alloys, Electric power transmission networks, Formal analysis, Formal model, Internet of things, IOT applications, IOT networks, Microgrids, Model checking, Model-checking techniques, Network architecture, Power grids, Rhenium alloys, Smart grid, Smart power grids, Technological solution},
	doi = {10.1109/CloudTech49835.2020.9365923},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102629868\&doi=10.1109\%2fCloudTech49835.2020.9365923\&partnerID=40\&md5=7c48017aaa6f8aa00a31f5ae27bd816c},
	author = {Hafdi, K. and Kriouile, A.}
}
@conference {ElMrabtt2020,
	title = {Novel Convex Polyhedron Classifier for Sentiment Analysis},
	booktitle = {Proceedings of 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications, CloudTech 2020},
	year = {2020},
	note = {cited By 1},
	abstract = {In this paper, we propose a Novel Convex Polyhedron classifier (NCPC) based on the geometric concept convex hull. NCPC is basically a linear piecewise classifier (LPC). It partitions linearly non-separable data into various linearly separable subsets. For each of these subset of data, a linear hyperplane is used to classify them. We evaluate the performance of this classifier by combining it with two feature selection methods (Chi-squared and Anova F-value). Using two datasets, the results indicate that our proposed classifier outperforms other LPC-based classifiers. {\textcopyright} 2020 IEEE.},
	keywords = {Artificial intelligence, Chi-Squared, Classification (of information), Cloud computing, Convex hull, Convex polyhedrons, F values, Feature selection methods, Geometric concepts, Geometry, Linearly separable, Piece-wise, Sentiment analysis},
	doi = {10.1109/CloudTech49835.2020.9365906},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102620927\&doi=10.1109\%2fCloudTech49835.2020.9365906\&partnerID=40\&md5=143fd1c523fe8ae24563a9b0482f632c},
	author = {El Mrabtt, S. and Lazaar, M. and Al Achhab, M. and Omara, H.}
}
@article {11714990120160901,
	title = {ANALYSIS AND VERIFICATION OF XACML POLICIES IN A MEDICAL CLOUD ENVIRONMENT.},
	journal = {Scalable Computing: Practice \& Experience},
	volume = {17},
	number = {3},
	year = {2016},
	pages = {189 - 205},
	abstract = {The connectivity of devices, machines and people via Cloud infrastructure can support collaborations among doctors and specialists from different medical organisations. Such collaborations may lead to data sharing and joint tasks and activities. Hence, the collaborating organisations are responsible for managing and protecting data they share. Therefore, they should define a set of access control policies regulating the exchange of data they own. However, existing Cloud services do not offer tools to analyse these policies. In this paper, we propose a Cloud Policy Verification Service (CPVS) for the analysis and the verification of access control policies specified using XACML. The analysis process detects anomalies at two policy levels: a) intra-policy: detects discrepancies between rules within a single security policy (conflicting rules and redundancies), and b) interpolicies: detects anomalies between several security policies such as inconsistency and similarity. The verification},
	keywords = {Automata, Cloud computing, Completeness, Data security, Formal Verification, Information storage \& retrieval systems {\textendash} Medical care, Security Anomaly Detection, XACML Policies},
	issn = {18951767},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=iih\&AN=117149901\&site=ehost-live},
	author = {Ayache, Meryeme and Erradi, Mohammed and Khoumsi, Ahmed and Freisleben, Bernd}
}
