Benchmarking Elastic Cloud Big Data Services Under SLA Constraints

2020 ◽  
pp. 1-18
Author(s):  
Nicolas Poggi ◽  
Víctor Cuevas-Vicenttín ◽  
Josep Lluis Berral ◽  
Thomas Fenech ◽  
Gonzalo Gómez ◽  
...  
Keyword(s):  
Big Data ◽  
Data Services

Architecture for Big Data Storage in Different Cloud Deployment Models

2021 ◽  
pp. 178-208
Author(s):  
Chandu Thota ◽  
Gunasekaran Manogaran ◽  
Daphne Lopez ◽  
Revathi Sundarasekar
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
High Performance ◽  
Data Services ◽  
Big Data Applications ◽  
Nosql Database ◽  
Amazon Web Services ◽  
Product Domains ◽  
Scalable Database

Cloud Computing is a new computing model that distributes the computation on a resource pool. The need for a scalable database capable of expanding to accommodate growth has increased with the growing data in web world. More familiar Cloud Computing vendors such as Amazon Web Services, Microsoft, Google, IBM and Rackspace offer cloud based Hadoop and NoSQL database platforms to process Big Data applications. Variety of services are available that run on top of cloud platforms freeing users from the need to deploy their own systems. Nowadays, integrating Big Data and various cloud deployment models is major concern for Internet companies especially software and data services vendors that are just getting started themselves. This chapter proposes an efficient architecture for integration with comprehensive capabilities including real time and bulk data movement, bi-directional replication, metadata management, high performance transformation, data services and data quality for customer and product domains.

Integrating Big Data Services Into an Undergraduate MIS Curriculum

2017 ◽  
Vol 7 (2) ◽  
pp. 58-73 ◽  
Author(s):  
Scott Jensen
Keyword(s):  
Big Data ◽  
Real World ◽  
Data Science ◽  
Graduate Programs ◽  
Advanced Degree ◽  
Data Services ◽  
Hands On ◽  
Data Problem

There is an insatiable demand in industry for data scientists, and graduate programs and certificates are gearing up to meet this demand. However, there is agreement in the industry that 80% of a data scientist's work consists of the transformation and profiling aspects of wrangling Big Data; work that may not require an advanced degree. In this paper, the authors present hands-on exercises to introduce Big Data to undergraduate MIS students using the CoNVO Framework and Big Data tools to scope a data problem and then wrangle the data to answer questions using a real-world dataset. This can provide undergraduates with a single course introduction to an important aspect of data science.

scholarly journals IEEE Access Special Session Editorial: Big Data Services and Computational Intelligence for Industrial Systems

IEEE Access ◽  
2015 ◽  
Vol 3 ◽  
pp. 3085-3088 ◽  
Author(s):  
Zhangbing Zhou ◽  
Walid Gaaloul ◽  
Patrick C. K. Hung ◽  
Lei Shu ◽  
Wei Tan
Keyword(s):  
Big Data ◽  
Computational Intelligence ◽  
Special Session ◽  
Industrial Systems ◽  
Data Services

scholarly journals Decision Framework for Engaging Cloud-Based Big Data Analytics Vendors

2020 ◽  
Vol 22 (4) ◽  
pp. 60-74
Author(s):  
Emmanuel Wusuhon Yanibo Ayaburi ◽  
Michele Maasberg ◽  
Jaeung Lee
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Competitive Advantage ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Unique Contribution ◽  
Data Services ◽  
Business Environments ◽  
Digital Era ◽  
Service Oriented

Organizations face both opportunities and risks with big data analytics vendors, and the risks are now profound, as data has been likened to the oil of the digital era. The growing body of research at the nexus of big data analytics and cloud computing is examined from the economic perspective, based on agency theory (AT). A conceptual framework is developed for analyzing these opportunities and challenges regarding the use of big data analytics and cloud computing in e-business environments. This framework allows organizations to engage in contracts that target competitive parity with their service-oriented decision support system (SODSS) to achieve a competitive advantage related to their core business model. A unique contribution of this paper is its perspective on how to engage a vendor contractually to achieve this competitive advantage. The framework provides insights for a manager in selecting a vendor for cloud-based big data services.

scholarly journals Open Data Services in the Library

2021 ◽  
Vol 10 (1) ◽  
pp. 1-17
Author(s):  
Qi Zhang ◽  
Chunjing Zhang ◽  
Zheyu Zhang
Keyword(s):  
Big Data ◽  
Future Development ◽  
Open Data ◽  
Legal Protection ◽  
Data Service ◽  
Research And Practice ◽  
Related Research ◽  
Data Services ◽  
The Future ◽  
Data Libraries

The opening and sharing of data are gaining momentum in the era of big data. Libraries have been actively involved in the research and practice of open data. At present, the related research in Chinese libraries is still in progress, which mainly focuses on the introduction of the abroad practices, or on the construction of open data platforms. This paper introduces and analyzes the Shanghai library open data service and provides a useful reference for the open data service of Libraries in China. For the future development of the Shanghai library open data services, this paper puts forward some measures and suggestions that include metadata work, website construction, legal protection, and developer community training.

scholarly journals Deduction of the Policy Issues for Activating the Geo-Spatial Big Data Services

2015 ◽  
Vol 23 (6) ◽  
pp. 19-29 ◽  
Author(s):  
Joon Min Park ◽  
Myeong Ho Lee ◽  
Dong Bin Shin ◽  
Jong Wook Ahn
Keyword(s):  
Big Data ◽  
Policy Issues ◽  
Data Services ◽  
Spatial Big Data
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