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



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


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.



[1]  ABET, “Criteria for accrediting engineering programs,” Available: http://www.abet.org/Linked%20Documents-UPDATE/Criteria%20and%20PP/05-06-EAC%20Criteria.pdf. [Accessed June 26, 2010.]
[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.]
Show More References
[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.]
[11]  Barr, J., “Amazon EC2 Beta.” http://aws.typepad.com/aws/2006/08/amazon_ec2_beta.html [Accessed July 16, 2009.]
[12]  Microsoft, “Cloud computing for education,” Available: http://www.microsoft.com/education/solutions/cloudcomputing.aspx. [Accessed July 15, 2011.]
[13]  IBM, “Introducing the IBM Cloud Academy,” Available: http://www.ibm.com/solutions/education/cloudacademy/us/en. [Accessed September 16, 2010.]
[14]  Google, “Gmail, Calendar, Docs and more,” Available: http://www.google.com/a/help/intl/en/edu/index.html [Accessed September 16, 2010.]
Show Less References


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


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.



[1]  Eero, M., Lindegren, M. & Koster, F. (2011). The state and relative importance of drivers Of fish population dynamics: An indicator-based approach. Ecological Indicators 15 (2012) 248-252.
[2]  Kerr, L., Cadrin, S., & Secor, D. (2010). The role of spatial dynamics in the stability, Resilience, and productivity of an estuarine fish population. Ecological Applications, 20(2), 2010, pp. 497-507.
[3]  Griffiths, R.A. (1997). Temporary ponds as amphibian habitats. Aquatic Conservation: Marine and Freshwater Ecosystems, Vol. 7, 119-126.


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


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.



[1]  Y.E. Ioannidis and Y.C. Kang, “Randomized algorithms for optimizing large join queries, ACM 1990.
[2]  M. Jarke and J. Koch, “Query optimization in database systems,” ACM Computing Surveys, volume 16, no. 2, pp. 111-152, June 1984.
[3]  L. P. Mahalingam and K. S. Candan, Multi-Criteria Query Optimization in the Presence of Result Size and Quality Tradeoffs, Multimedia Tools and Applications Journal 23(3) (2004), 167-183.
[4]  Ch. Li, Kevin Ch.-Ch. Chang, I. F. Ilyas, and S. Song, RankSQL: query algebra and optimization for relational top-k queries. In: F. Ozcan, editor, SIGMOD Conference. ACM, 2005, 131-142.
[5]  Stefan Riezler, Statistical Machine Translation for Query Expansion in Answer Retrieval, Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 464-471, Prague, Czech Republic, June 2007.
Show More References
[6]  Raymond T. Ng and V. S. Subrahmanian. Probabilistic logic programming. Information and Computation, 101(2):150-201, 1992.
[7]  Norbert Fuhr and Thomas Rolleke. A probabilistic relational algebra for the integration of information retrieval and database systems. ACM Trans. Inf. Syst., 15(1):32-66, 1997.
[8]  Praveen Seshadri, Hamid Pirahesh, and T. Y. Cliff Leung. Complex query decorrelation. In Intl. Conf. on Data Engineering, 1996.
[9]  Subbu N. Subramanian and Shivakumar Venkataraman. Cost based optimization of decision support queries using transient views. In SIGMOD Intl. Conf. on Management of Data, Seattle, WA, 1998.
[10]  A. Pérez-Uribe and B. Hirsbrunner,―Learning and foraging in robot-bees‖, in Meyer, Berthoz, Floreano, Roitblat andWilson (eds.)’, SAB2000 Proceedings Supplement Book, Intermit. Soc. For Adaptive Behavior, Honolulu, Hawaii, pp. 185-194.
[11]  K. Bennett, M.C. Ferris, and Y.E. Ioannidis, ―A genetic algorithm for database query optimization‖, In Proc. of the 4th International Conference on Genetic Algorithms, 400-407, 1991.
[12]  M.J. Franklin, S.R. Jeffery, S. Krishnamurthy, F. Reiss, S. Rizvi, et al., ―Design considerations for high fan-in systems: the HiFi approach‖, In Proc. Of the CIDR Conf., Jan. 2005.
[13]  A. Sokolov and D. Whitley, “Unbiased Tournament Selection,” in proceedings of the 2005 conference on Genetic and Evolutionary Computation, pp. 1131-1138, 2005
[14]  T.V. VijayKumar, Vikram Singh and Ajay Kumar Verma, “Generating Distributed Query Processing Plans using Genetic Algorithm”, In the proceedings of the International Conference on Data Storage and Data Engineering (DSDE 2010), Bangalore, February 9-10, 2010, pp. 173-177, 2010.
Show Less References