A Data-Science-As-A-Service Model
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Evaluation of Cloud Computing Services Based on NIST 800-145 ______
NIST Special Publication 500-322 Evaluation of Cloud Computing Services Based on NIST SP 800-145 Eric Simmon NIST Cloud Computing Cloud Services Working Group NIST Cloud Computing Program Information Technology Laboratory This publication is available free of charge from: https://doi.org/10.6028/NIST.SP.500.322 NIST Special Publication 500-322 Evaluation of Cloud Computing Services Based on NIST SP 800-145 Eric Simmon NIST Cloud Computing Cloud Services Working Group NIST Cloud Computing Program Information Technology Laboratory This publication is available free of charge from: https://doi.org/10.6028/NIST.SP.500-322 February 2018 U.S. Department of Commerce Wilbur L. Ross, Jr., Secretary National Institute of Standards and Technology Walter Copan, NIST Director and Undersecretary of Commerce for Standards and Technology Certain commercial entities, equipment, or materials may be identified in this document in order to describe an experimental procedure or concept adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the entities, materials, or equipment are necessarily the best available for the purpose. National Institute of Standards and Technology Special Publication 500-322 Natl. Inst. Stand. Technol. Spec. Publ. 500-322, 27 pages (February 2018) CODEN: NSPUE2 This publication is available free of charge from: https://doi.org/10.6028/NIST.SP.500-322 NIST SP 500-322 Evaluation of Cloud Computing Services Based on NIST 800-145 ______________________________________________________________________________________________________ Reports on Computer Systems Technology The Information Technology Laboratory (ITL) at NIST promotes the U.S. -
Cloud Computing and Enterprise Data Reliability Luan Gashi University for Business and Technology, [email protected]
University of Business and Technology in Kosovo UBT Knowledge Center UBT International Conference 2016 UBT International Conference Oct 28th, 9:00 AM - Oct 30th, 5:00 PM Cloud Computing and Enterprise Data Reliability Luan Gashi University for Business and Technology, [email protected] Follow this and additional works at: https://knowledgecenter.ubt-uni.net/conference Part of the Communication Commons, and the Computer Sciences Commons Recommended Citation Gashi, Luan, "Cloud Computing and Enterprise Data Reliability" (2016). UBT International Conference. 56. https://knowledgecenter.ubt-uni.net/conference/2016/all-events/56 This Event is brought to you for free and open access by the Publication and Journals at UBT Knowledge Center. It has been accepted for inclusion in UBT International Conference by an authorized administrator of UBT Knowledge Center. For more information, please contact [email protected]. Cloud Computing and Enterprise Data Reliability Cloud Computing and Enterprise Data Reliability Luan Gashi UBT – Higher Education Institution, Lagjja Kalabria, 10000 p.n., Prishtine, Kosovo [email protected] Abstract. Cloud services offer many benefits from information and communication technology that to be credible must first be secured. To use the potential of cloud computing, data is transferred, processed and stored in the infrastructures of these service providers. This indicates that the owners of data, particularly enterprises, have puzzled when storing their data is done outside the scope of their control. Research conducted on this topic show how this should be addressed unequivocally. The provided information on the organization of cloud computing models, services and standards, with a focus on security aspects in protecting enterprise data where emphasis shows how data access is treated with reliability from providers of these services. -
H-Kaas: a Knowledge-As-A-Service Architecture for E-Health
Brazilian Journal of Biological Sciences, 2018, v. 5, No. 9, p. 3-12. ISSN 2358-2731 https://doi.org/10.21472/bjbs.050901 H-KaaS: A Knowledge-as-a-Service architecture for E-health Renan G. Barreto¹, Lucas Aversari¹, Cecília Neta A. P. Gomes² and Natasha C. Q. Lino¹ ¹Universidade Federal da Paraíba. Centro de Ciências Exatas e da Natureza. Programa de Pós-Graduação em Informática. Campus I. -PB. Brazil. (CEP 58051-900). ²Programa de Pós-Graduação em Modelos de Decisão e Saúde.João CentroPessoa de Ciências Exatas e da Natureza. Universidade Federal da Paraíba. Campus I. -PB. Brazil. (CEP 58051-900). João Pessoa Abstract. Due to the need to improve access to knowledge and the establishment of means for sharing and organizing data in Received the health area, this research proposes an architecture based on January 8, 2018 the paradigm of Knowledge-as-a-Service (KaaS). This can be used in the medical field and can offer centralized access to Accepted April 30, 2018 ontologies and other means of knowledge representation. In this paper, a detailed description of each part of the architecture and Released its implementation was made, highlighting its main features and April 30, 2018 interfaces. In addition, a communication protocol was specified and used between the knowledge consumer and the knowledge Full Text Article service provider. Thus, the development of this research contributed to the creation of a new architecture, called H-KaaS, which established itself as a platform capable of managing multiple data sources and knowledge models, centralizing access through an easily adaptable API. Keywords: Knowledge-as-a-service architecture; Health informatics; Knowledge representation. -
Geospatial Data AS a Service: Use Cases
GEOSPATIAL DATA AS A SERVICE USE CASES A Report of the National Geospatial Advisory Committee December 2018 Geospatial Data as a Service – Use Cases December 2018 Introduction This document was developed to complement the material in the NGAC report on Geospatial Data as a Service: A vital investment in American Enterprise and to provide real-world use cases of Data as a Service (DaaS) in action. DaaS opens new possibilities and innovation by improving access to and use of data. Users and producers across government, industry, open source, education, and the sciences are turning to DaaS to meet their huge appetite for information, exponential storage needs, data archiving and to minimize data duplication and reduce costs. To illustrate these points, we examined four case studies that provide more in-depth use cases from across Federal and local agencies and the private sector. These case studies showcase real world scenarios with large, distributed data sources to support a broad range of end user needs. The case studies presented are not intended to be an exhaustive list of examples of DaaS in use; there are many other relevant examples and programs of DaaS across the stakeholder community. To demonstrate use of DaaS for local governments and emergency response, we highlight the work of the Missouri Task Force One (MO-TF1) and Boone County Fire Protection District (BCFPD). MO-TF1 and BCFPD are using public cloud for basemap creation, data access, and delivery to enable a common operating picture of critical data during deployments. (Appendix 1 - Data as a Service & Cloud Computing: Disaster Response Just-in-Time Basemap Creation for Deployments). -
Cloud Computing and Big Data Is There a Relation Between the Two: a Study
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 17 (2017) pp. 6970-6982 © Research India Publications. http://www.ripublication.com Cloud Computing and Big Data is there a Relation between the Two: A Study Nabeel Zanoon1, Abdullah Al-Haj2, Sufian M Khwaldeh3 1 Department of Applied science, Al- Balqa Applied University/Aqaba, Jordan. 2 Faculty of Information Technology, University of Jordan/Aqaba, Jordan. 3Business Information Technology (BIT) Department, The University of Jordan/Aqaba, Jordan. 1Orcid ID: 0000-0003-0581-206X Abstract and combinations of each. Such data can be directly or indirectly related to geospatial information [3]. Communicating by using information technology in various ways produces big amounts of data. Such data requires Cloud computing refers to on-demand computer resources and processing and storage. The cloud is an online storage model systems available across the network that can provide a where data is stored on multiple virtual servers. Big data number of integrated computing services without local processing represents a new challenge in computing, resources to facilitate user access. These resources include especially in cloud computing. Data processing involves data data storage capacity, backup and self-synchronization [4]. acquisition, storage and analysis. In this respect, there are Most IT Infrastructure computing consist of services that are many questions including, what is the relationship between provided and delivered through public centers and servers big data and cloud computing? And how is big data processed based on them. Here, clouds appear as individual access in cloud computing? The answer to these questions will be points for the computing needs of the consumer. -
Evolution of As-A-Service Era in Cloud
Evolution of as-a-Service Era in Cloud Sugam Sharma Center for Survey Statistics and Methodology, Iowa State University, Ames, Iowa, USA Email: [email protected] Abstract. Today, a paradigm shift is being observed in science, where the focus is gradually shifting toward the cloud environments to obtain appropriate, robust and affordable services to deal with Big Data challenges (Sharma et al. 2014, 2015a, 2015b). Cloud computing avoids any need to locally maintain the overly scaled computing infrastructure that include not only dedicated space, but the expensive hardware and software also. In this paper, we study the evolution of as-a-Service modalities, stimulated by cloud computing, and explore the most complete inventory of new members beyond traditional cloud computing stack. Keywords. Cloud, as-a-Service 1. Introduction Today, the cloud computing also has emerged as one of the major shifts in recent information and communication age that promises an affordable and robust computational architecture for large-scale and even for overly complex enterprise applications (Fenn et al. 2008). It is a powerful and revolutionary paradigm that offers service-oriented computing and abstracts the software-equipped hardware infrastructure from the clients or users. Although, the concept of cloud computing is mainly popular in three praxises- 1) IaaS (Infrastructure-as-a-Service ), 2) PaaS (Platform-as-a-Service ), and 3) SaaS (Software-as-a-Service ), but in this data science age, should be equally expandable to DBaaS (Database- as-a-Service ) (Curino, et al., 2011; Seibold et al., 2012). Over the last few year, the cloud computing has evolved as scalable, secure and cost-effective solutions to an overwhelming number of applications from diversified areas. -
Accelerate Delivery of Your Modern Data Analytics Platform with the Adatis Framework
Accelerate delivery of your Modern Data Analytics Platform with the Adatis Framework Data analytics is more relevant in today’s business than ever but traditional data warehouse platforms are evolving. The demands placed on analytics platforms are to provide both the traditional, structured, standardised BI and to support new ways of analysing data based on agility, supporting rapid change and semi/unstructured data. To support this new form of advanced analytics, including machine learning, predictive and artificial intelligence, a new flexible, cloud-based platform is required. Adatis ’ Microsoft Azure based Data Platform Framework is used extensively on client “With Adatis’ projects to under-pin and fast track the development of enterprise data platforms that satisfy their clients data analytics requirements. The Adatis Framework has knowledge of Microsoft been developed over may years based on industry best practice and is constantly Azure Data Analytics, updated and enhanced to make the most of technology enhancements released by Microsoft. for the first time we are able to instantly The Adatis Framework uses the advanced data transformation capabilities of the Microsoft Azure stack, including Azure Data Factory, Azure ML Services, Azure monitor, predict, Databricks, and Azure Data Lake Store, to: troubleshoot Measurement and ▪ Ingest data from multiple sources and file types analyse inand ways review we ▪ Cleanse data and handle errors ▪ Track lineage and log activity would not believe ▪ Automate processes and generate code possible before.” ▪ Manage costs and resource use How it works The Adatis framework takes feeds from multiple source systems and, provides a technical framework and reference architecture for ingesting, preparing, shaping, integrating, and storing the data in a data lake Adatis Modern Data Platform | adatis.co.uk The Adatis framework brings many benefits to your team and your projects: Proven: We’ve built and refined the framework over years of working with companies on their transformation initiatives. -
Lenovo Big Data Validated Design for Cazena Saas Using Cloudera on Thinkagile HX
Lenovo Big Data Validated Design for Cazena SaaS using Cloudera on ThinkAgile HX Last update: 27 June 2019 Version 1.0 Configuration Reference Number: BGDCL03XX91 Reference architecture for Solution based on the Cloudera Enterprise with Cazena ThinkSystem HX platform running SaaS Data Lakes VMware vSphere virtualization Deployment considerations for Solution based on Cazena’s Fully scalable racks including detailed Managed SaaS Data Lake running validated bills of material on the ThinkAgile HX platform Dan Kangas (Lenovo) Venkat Chandra (Cazena) John Piekos (Cazena) Keith Adams (Lenovo) Ajay Dholokia (Lenovo) Gary Cudak (Lenovo) 1 Lenovo Big Data Validated Design for Cazena SaaS using Cloudera on ThinkAgile HX Table of Contents 1 Introduction .............................................................................................. 4 2 Business problem and business value .................................................. 5 Business Problem .................................................................................................... 5 Business Value ........................................................................................................ 5 2.2.1 Time to Production....................................................................................................................... 5 2.2.2 Deploy ML & Analytics Quickly with the AppCloud ..................................................................... 6 2.2.3 Plug & Play Enterprise Deployment: .......................................................................................... -
Universidade Estadual Paulista “Júlio De Mesquita Filho”, Campus De São José Do Rio Preto
Naylor Garcia Bachiega Algoritmo de Escalonamento de Instância de Máquina Virtual na Computação em Nuvem São José do Rio Preto 2014 Naylor Garcia Bachiega Algoritmo de Escalonamento de Instância de Máquina Virtual na Computação em Nuvem Dissertação apresentada como parte dos requisitos para obtenção do título de Mestre em Ciência da Computação, junto ao Programa de Pós-Graduação em Ciência da Computação, Área de Concentração - Sistemas de Computação, do Instituto de Biociências, Letras e Ciências Exatas da Universidade Estadual Paulista “Júlio de Mesquita Filho”, Campus de São José do Rio Preto. Orientador: Profª. Drª. Roberta Spolon São José do Rio Preto 2014 Bachiega, Naylor Garcia. Algoritmo de escalonamento de instância de máquina virtual na computação em nuvem / Naylor Garcia Bachiega. -- São José do Rio Preto, 2014 92 f. : il., gráfs., tabs. Orientador: Roberta Spolon Dissertação (mestrado) – Universidade Estadual Paulista “Júlio de Mesquita Filho”, Instituto de Biociências, Letras e Ciências Exatas 1. Computação. 2. Computação em nuvem. 3. Recursos de redes de computadores. 4. Algorítmos de computador. 5. Armazenamento de dados. I. Spolon, Roberta. II. Universidade Estadual Paulista "Júlio de Mesquita Filho". Instituto de Biociências, Letras e Ciências Exatas. III. Título. CDU – 681.3.025 Ficha catalográfica elaborada pela Biblioteca do IBILCE UNESP - Câmpus de São José do Rio Preto Naylor Garcia Bachiega Algoritmo de Escalonamento de Instância de Máquina Virtual na Computação em Nuvem Dissertação apresentada como parte dos requisitos para obtenção do título de Mestre em Ciência da Computação, junto ao Programa de Pós-Graduação em Ciência da Computação, Área de Concentração - Sistemas de Computação, do Instituto de Biociências, Letras e Ciências Exatas da Universidade Estadual Paulista “Júlio de Mesquita Filho”, Campus de São José do Rio Preto. -
Customer Knowledge Management in the Cloud Ecosytem
Communications of the IIMA Volume 16 Issue 2 Article 3 2018 Customer Knowledge Management In the Cloud Ecosytem Joseph O. Chan Roosevelt University, [email protected] Follow this and additional works at: https://scholarworks.lib.csusb.edu/ciima Part of the Management Information Systems Commons Recommended Citation Chan, Joseph O. (2018) "Customer Knowledge Management In the Cloud Ecosytem," Communications of the IIMA: Vol. 16 : Iss. 2 , Article 3. Available at: https://scholarworks.lib.csusb.edu/ciima/vol16/iss2/3 This Article is brought to you for free and open access by CSUSB ScholarWorks. It has been accepted for inclusion in Communications of the IIMA by an authorized editor of CSUSB ScholarWorks. For more information, please contact [email protected]. 1 Customer Knowledge Management in the Cloud Ecosystem INTRODUCTION The economic order of the twenty-first century is driven by knowledge based on the value of relationships (Galbreath, 2002) throughout the extended enterprise. Knowledge management (KM) and customer relationship management (CRM) have become cornerstones of value creation strategies in a market saturated with product offerings. CKM arises from synthesizing the knowledge management and customer relationship management processes, where customer knowledge is acquired through CRM operations and transformed into actionable knowledge insights through the KM process to enhance operations. Alongside the advances in KM and CRM is the emergence of such business technologies as Big Data and Cloud Computing. Big Data refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyse (McKinsey Global Institute, 2011), and is characterized by the datasets’ high volume, velocity, variety, veracity and value. -
Towards an Efficient Distributed Cloud
TOWARDS AN EFFICIENT DISTRIBUTED CLOUD ARCHITECTURE BY PRAVEEN KHETHAVATH Bachelor of Engineering in Electronics and Communication Engineering Osmania University Hyderabad, AP, INDIA 2006 Master of Science in Computer Science University of Northern Virginia Annandale, VA 2008 Submitted to the Faculty of the Graduate College of the Oklahoma State University in partial fulfillment of the requirements for the Degree of DOCTOR OF PHILOSOPHY July, 2014 TOWARDS AN EFFICIENT DISTRIBUTED CLOUD ARCHITECTURE Dissertation Approved: Johnson P Thomas Dissertation Adviser Eric Chan-tin Dissertation Co-Adviser Subhash Kak Mary Gade ii LIST THE PUBLICATIONS YOU HAVE FROM THIS WORK Praveen Khethavath, Johnson Thomas. “Game Theoretic approach to Resource provisioning in a Distributed Cloud”, submitted at 28th IEEE International Conference on. Advanced Information Networking and Applications Workshops WAINA 2014(Accepted) Praveen Khethavath, Johnson Thomas, Eric Chan-Tin, and Hong Liu. "Introducing a Distributed Cloud Architecture with Efficient Resource Discovery and Optimal Resource Allocation". In Proceedings of 3rd IEEE SERVICES CloudPerf Workshop 2013 Praveen Khethavath, Nhat, Prof. Johnson P Thomas. “A Virtual Robot Sensor Network (VRSN)”. In Proceedings of 2nd International Workshop on Networks of Cooperating Objects CONET 2011 Praveen Khethavath, Johnson Thomas. “Distributed Cloud Architecture: Resource Modelling and Security Concerns”. In Proceedings of 3rd Annual conference on Theoretical and Applied Computer Science (TACS 2012) iii ACKNOWLEDGEMENTS I would like to express my deepest gratitude to my advisor, Dr. Johnson Thomas for his excellent guidance, patience, and providing me with an excellent atmosphere for doing research and throughout my thesis. His guidance helped me to successfully complete my research. For me, he was not only a respectable professor who led me on the way to do research, but also an attentive tutor who trained me to be a good teacher in my future career. -
IT Infrastructure and Support Systems
Chapter IT Infrastructure and 2 Support Systems Chapter 2 Link Library Learning Objectives Quick Look at Chapter 2 ³ Understand the types of information systems and how they Sprint Nextel’s Subscriber Data Management process data. System ᕢ Understand the types of information systems used to sup- port business operations and decision makers. 2.1 Data and Software Application Concepts ᕣ Describe how IT supports supply chains and business processes. 2.2 Types of Information Systems and Support ᕤ Understand the attributes, benefits, and risks of service- based and cloud computing infrastructures. 2.3 Supply Chain and Logistics Support 2.4 IT Infrastructures, Cloud Computing, and Services Business Case: Airbus Improves Productivity with RFID Nonprofit Case: Royal Shakespeare Company Leverages Patron Data to Improve Performance Analysis Using Spreadsheets: Managing Gasoline Costs Cases on Student Web Site 2.1 Mary Kay’s IT Systems 2.2 Predictive Analysis Can Help You Avoid Traffic Jams References Integrating IT ACC FIN MKT OM HRM IS 30 Chapter 2 LINK LIBRARY Blog on cloud computing infoworld.com/blogs/david-linthicum Planners Lab, for building a DSS plannerslab.com Supply Chain and Logistics Institute SCL.gatech.edu/ Salesforce.com cloud demos salesforce.com U.S. Defense Information Systems Agency disa.mil Supply Chain, Europe’s strategic supply chain management resource supplychainstandard.com QUICK LOOK at Chapter 2, IT Infrastructure and Support Systems This section introduces you to the business issues, challenges, Decision making and problem solving require data and IT solutions in Chapter 2. Topics and issues mentioned in and models for analysis; they are supported by decision the Quick Look are explained in the chapter.