Welcome to American Journal of Information Systems

American Journal of Information Systems is a peer-reviewed, open access journal that provides rapid publication of articles in all areas of information systems. The goal of this journal is to provide a platform for scientists and academicians all over the world to promote, share, and discuss various new issues and developments in different areas of information systems.

ISSN (Print): 2374-1953

ISSN (Online): 2374-1988

Editor-in-Chief: Sergii Kavun

Website: http://www.sciepub.com/journal/AJIS

   

Article

Application of Infrastructure as a Service in IT Education

1Math and Computer Science, University of Houston-Victoria, Victoria, United States


American Journal of Information Systems. 2014, 2(2), 42-48
doi: 10.12691/ajis-2-2-3
Copyright © 2014 Science and Education Publishing

Cite this paper:
Li Chao. Application of Infrastructure as a Service in IT Education. American Journal of Information Systems. 2014; 2(2):42-48. doi: 10.12691/ajis-2-2-3.

Correspondence to: Li  Chao, Math and Computer Science, University of Houston-Victoria, Victoria, United States. Email: chaol@uhv.edu

Abstract

This paper considers cloud service development to support hands-on practice in IT education. For IT education, cloud services can be used to reduce cost, enhance security, and provide flexibility. This paper presents a case study to illustrate how cloud services can be used to support hands-on practice for IT courses. It also provides a five-step development strategy to develop cloud based computer labs for various types of IT courses.

Keywords

References

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[2]  Chang, V., and Guetl, C., “Generation Y learning in the 21st century: Integration of virtual worlds and cloud computing services.” In Z. Abas et al. (Eds.), Proceedings of Global Learn Asia Pacific 2010 (pp. 1888-1897). Chesapeake, VA: AACE. 2010.
 
[3]  Chao, L, Strategies and technologies for developing online computer labs for technology-based courses. Hershey, PA: IGI Global, 2008.
 
[4]  Fox, A, “Cloud computing in education,” Available: http://inews.berkeley.edu/articles/Spring2009/cloud-computing. [Accessed July 26, 2010.]
 
[5]  Nicholson, J. L, “Cloud computing: Top issues for higher education,” Available: http://www.universitybusiness.com/viewarticle.aspx?articleid=1342 [Accessed July 26, 2010.]
 
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[6]  Stein, S., Ware, J., Laboy, J., & Schaffer, H. E, “Improving K-12 pedagogy via a Cloud designed for education,” International Journal of Information Management, 33 (1), 235-241. 2013.
 
[7]  Chao, L, Cloud technology and its application in IT education. In M. Koehler & P. Mishra (Eds.), Proceedings of Society for Information Technology & Teacher Education International Conference 2011 (pp. 3053-3056). Chesapeake, VA: AACE. 2011.
 
[8]  Cloud Weeks, “Cloud computing – demystifying SaaS, PaaS and IaaS,” Available: http://www.cloudtweaks.com/2010/05/cloud-computing-demystifying-saas-paas-and-iaas. [Accessed September 16, 2010.]
 
[9]  Velte, T., Velte, A., & Elsenpeter, R., Cloud computing, a practical approach. New York: McGraw-Hill Osborne Media. 2009.
 
[10]  Amazon, “AWS in Education,” Available: http://aws.amazon.com/education [Accessed July 15, 2011.]
 
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[12]  Microsoft, “Cloud computing for education,” Available: http://www.microsoft.com/education/solutions/cloudcomputing.aspx. [Accessed July 15, 2011.]
 
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Article

Computer System Users are like Fish

1Drexel University, Philadelphia, PA


American Journal of Information Systems. 2014, 2(3), 49-51
doi: 10.12691/ajis-2-3-1
Copyright © 2014 Science and Education Publishing

Cite this paper:
Ralph M. DeFrangesco. Computer System Users are like Fish. American Journal of Information Systems. 2014; 2(3):49-51. doi: 10.12691/ajis-2-3-1.

Correspondence to: Ralph  M. DeFrangesco, Drexel University, Philadelphia, PA. Email: rd337@drexel.edu

Abstract

This paper has looked at the habits of computer users when faced with a slow system and has drawn a direct correlation between how they react and fish population dynamics. A survey has been presented that supports the proposed theory.

Keywords

References

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[3]  Griffiths, R.A. (1997). Temporary ponds as amphibian habitats. Aquatic Conservation: Marine and Freshwater Ecosystems, Vol. 7, 119-126.
 

Article

Estimating Plans along with Cost in Multiple Query Processing Environments by Applying Particle Swarm Optimization Technique

1Deaprtment of Computer Sc.&Engg., Ajay Binay Institute of Technology, Cuttack

2S.O.A. University, Bhubaneswar

3Government College of Engineering, Bhawanipatna


American Journal of Information Systems. 2014, 2(3), 52-55
doi: 10.12691/ajis-2-3-2
Copyright © 2014 Science and Education Publishing

Cite this paper:
Sambit Kumar Mishra, Srikanta Pattnaik, Dulu patnaik. Estimating Plans along with Cost in Multiple Query Processing Environments by Applying Particle Swarm Optimization Technique. American Journal of Information Systems. 2014; 2(3):52-55. doi: 10.12691/ajis-2-3-2.

Correspondence to: Sambit  Kumar Mishra, Deaprtment of Computer Sc.&Engg., Ajay Binay Institute of Technology, Cuttack. Email: sambit_pr@rediffmail.com

Abstract

The Main idea of multiple query processing is to optimize a set of queries together and execute the common operations once. Major tasks in multiple query processing are common operation or expression identification and global execution plan construction. Query plans are generally derived from registered continuous queries. They are composed of operators, which perform the actual data processing, queries which buffer data as it moves between operators to hold state of operators. The complex part is to decompose queries and query plans and rearrange the sub queries and query plans on the network. The main functions to achieve an optimal query distribution are usually minimizing network usage and minimizing response time of queries. While dealing with query distribution problem, the challenges like modeling topology of the network, decomposing queries into some sub queries and sub query placement may be occurred. Operators are the basic data processing units in a query plan. An operator takes one or more streams as input and produces a stream as output. As in the traditional database management system, a plan for query connects a set of operators in a tree. The output of a child operator forms an input of its parent operator. In this paper it is aimed to retrieve the cost of query plans as well as cost of particles of swarm in multiple query processing environments by applying particle swarm optimization techniques.

Keywords

References

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