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

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

[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.
 

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

[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

Article

Application of IS-Balanced Scorecard in Performance Measurement of e-Government Services in Kenya

1School of Computing and Informatics, University of Nairobi, Nairobi, Kenya


American Journal of Information Systems. 2015, 3(1), 1-14
doi: 10.12691/ajis-3-1-1
Copyright © 2015 Science and Education Publishing

Cite this paper:
Grace Leah AKINYI, Christopher A. MOTURI. Application of IS-Balanced Scorecard in Performance Measurement of e-Government Services in Kenya. American Journal of Information Systems. 2015; 3(1):1-14. doi: 10.12691/ajis-3-1-1.

Correspondence to: Grace  Leah AKINYI, School of Computing and Informatics, University of Nairobi, Nairobi, Kenya. Email: ograceleah@gmail.com

Abstract

This research applied the Balanced Scorecard concept to audit performance of e-Government services at Kenya Revenue Authority. An analysis was made on how KRA developed performance measurement data. A systematic study of the existing performance tools was carried out in establishing the basis for conceptualizing the Information Systems Balanced Scorecard. Various dimensions of e-Government services were measured and a tool was proposed that would assess the quality dimensions of the e-Government services from a management perspective. The proposed tool was validated using i-Tax service of KRA. We list the indicators and metrics to be used to measure the performance of e-Government services. This research suggests an adoption of an IS-BSC which measures and evaluates e-Government services from four perspectives: business value, user orientation, internal process and future readiness. The research concludes with recommendations to help governments develop a performance measurement mechanism to assess the impact of investing in e-Government. Considering that performance measurement is a prerequisite to e-Government efforts to audit services and assure citizen of government’s accountability, the findings will be beneficial to ministries adopting e-Government initiatives as they will gain an understanding about the mixed method of using metrics in IT governance balanced scorecard.

Keywords

References

[1]  Kaplan, R., &Norton, D. P., “The balanced scorecard: measures that drive performance,” Harvard Business Review 70(1). 71-79. 1992.
 
[2]  Republic of Kenya, E-government strategy: The strategic framework, administrative structure, training Requirements and standardization framework, Nairobi, Government Printer, 2014.
 
[3]  Vision 2030Kenya - http://www.vision2030.go.ke/. 2014.
 
[4]  Waema T. M.,“A Conceptual Framework for Assessing the Effects of E-Government on Governance,” Proceedings of The 1st International Conference in Computer Science and IT, Nairobi, 98-103. 2007.
 
[5]  http://www.kra.go.ke. 2014.
 
Show More References
[6]  Bouckaert, G., Halligan, J.,Managing Performance: International Comparisons, Routledge, Abingdon. 2008.
 
[7]  Halligan, J., & Bouckaert, G. “Performance governance: from ideal type to practice”, paper presented at the Conference of the International Research Society for Public Management, Dublin, 11-13, April, 2011.
 
[8]  Van Dooren, W., Bouckaert, G., Halligan, J., Performance Management in the Public Sector, Routledge, Abingdon, 2010.
 
[9]  Rhodes, M., L., Biondi, L., Gomes, R., Melo, A. I., Ohemeng, F., Perez-Lopez, G., Rossi, A., & Sutiyono, W., “Current state of public sector performance management in seven selected countries,” International Journal of Productivity and Performance Management, 61( 3), 235-271. 2012.
 
[10]  UNDESA, “From E-government to E-inclusion. UN global E-government readiness report,”New York: United Nations publication, 2005.
 
[11]  Schuppan, T., “E-Government in developing countries: Experiences from sub-Saharan Africa,” Government Information Quarterly, 26, 118-127. 2009.
 
[12]  Lawson-Body, A., Mukankusi, L., & Miller, G., “An adaptation of the Balanced Scorecard for e-Government service delivery: a content analysis,” Journal of Service Science, 1(1), 75-82. 2011.
 
[13]  Al-Hujran, O., Al-dalahmeh, M., &Aloudat, A., “The Role of National Culture on Citizen Adoption of e-Government Services: An Empirical Study,” Electronic Journal of e-Government, 9(2): 93-106. 2012.
 
[14]  UNESCO, E-Government Toolkit for Developing Countries, 4(2), 2005.
 
[15]  Scheer, A.W., Abolhassan, F., Jost, W. & Kirchmer, M., Business Process Excellence , ARIS in Practice, Berlin : Springer, 2002.
 
[16]  Seel, C. & Thomas, O., “Process Performance Measurement for E-Government: A Case Scenario from the German Ministerial Administration,” Systemics, Cybernetics and Informatics, 5 (3). 2012.
 
[17]  Afriliana, N., &Gaol, F. L., “Performance Measurement of Higher Education Information System Using IT Balanced Scorecard. In Intelligent Information and Database Systems,” Springer International Publishing, 412-421, 2014.
 
[18]  Alhyari, S., Alazab, M., Venkatraman, S., Alazab, M., &Alazab, A., “Performance evaluation of e-government services using balanced scorecard: An empirical study in Jordan. Benchmarking:,” An International Journal, 20(4). 512-536.2013.
 
[19]  Ying, J., “The application of BSC in China’s e-government performance evaluation,” In Symposium on Reform and Transition in Public Administration Theory and Practice in Greater China, Brown University, 1-4. June. 2010.
 
[20]  Jairak & Praneetpolgrang, 2013.
 
[21]  Berghout, E., & Tan, C. W., “Understanding the impact of business cases on IT investment decisions: An analysis of municipal e-government projects,” Information & Management, 50(7), 489-506. 2013.
 
[22]  Barbosa, A. F., Pozzebon, M., &Diniz, E. H., “Rethinking E-Government Performance Assessment From A Citizen Perspective,” Public Administration, 91(3), 744-762. 2013.
 
[23]  Delone, W. H., “The DeLone and McLean model of information systems success: a ten-year update,” Journal of management information systems, 19(4), 9-30. 2003.
 
[24]  Juiz, C., Guerrero, C., &Lera, I., “Implementing Good Governance Principles for the Public Sector in Information Technology Governance Frameworks,” Open Journal of Accounting, 2014.
 
[25]  Huang, C. D., & Hu, Q., “Integrating web services with competitive strategies: the balanced scorecard approach,” Communications of the AIS, 13(6), 57-80. 2004.
 
[26]  Nfuka, N., &Rusu, L., “Critical Success Framework for Implementing Effective IT Governance in Tanzanian Public Sector Organizations,” Journal of Global Information Technology Management, 16(3), 53-77. 2013.
 
[27]  Venkatraman, S., &Alazab, M., Quality Approaches for Performance Measurement in Jordanian E-Government Services. IT in the Public Sphere: Applications in Administration, Government, Politics, and Planning, 99-119. 2014.
 
[28]  Martinsons, M., Davison, R., &Tse, D., The balanced scorecard: a foundation for the strategic management of information systems. Decision support systems, 25(1), 71-88. 1999.
 
[29]  Palmius, J., “Criteria for measuring and comparing information systems. In 30th Information Systems Research Seminar in Scandinavia .IRIS (30). 2007.
 
[30]  Mugenda, O. M., & Mugenda, A. G. Research methods. Quantitative and qualitative approaches. Nairobi. Acts Press. 2003.
 
[31]  Kothari, C. R. Research methodology. Methods and techniques. New Delhi. New AgeInternational (P) Limited Publishers. 2004.
 
[32]  Matavire, R., Chigona, W., Roode, D, Sewchurran, E, Davids, Z, Mukudu, A., &Boamah-Abu, C., “Challenges of eGovernment Project Implementation in a South African Context,” The Electronic Journal Information Systems Evaluation, 13(2), 153-164. (2010).
 
[33]  Thompson, K. R., &Mathys, N. J., “It's time to add the employee dimension to the balanced scorecard,” Organizational Dynamics, 42(2), 135-144. 2013.
 
[34]  Banker, R. D., Chang, H., Janakiraman, S. N., &Konstans, C., “A balanced scorecard analysis of performance metrics,” European Journal of Operational Research, 154(2), 423-436. 2004b.
 
[35]  Banker, R.D., Chang, H., Pizzini, M.J., “The balanced scorecard: judgmental effects of performance measures linked to strategy,” The Accounting Review, 79(1), 1-23. 2004a.
 
[36]  Kaplan, R.S., & Norton, D.P., “Using the balanced scorecard as a strategic management system,” Harvard Business Review, 74(1). 75-85. 1996.
 
[37]  Sinha, A., “Balanced Scorecard: A Strategic Management Tool,” Vidyasagar University Journal of Commerce, 11. 2006.
 
[38]  Gueorguiev, I., “Balanced Scorecard Based Management Information System – A Potential for Public Monitoring and Good Governance Advancement,” The Electronic Journal of e-Government, 3(1), 29-38. 2005.
 
[39]  Grembergen, W., Saull, R. &Haes, S. D., “Linking the IT Balanced Scorecard to the Business Obectives at a Major Canadian Financial Group,” Journal of Information Technology Cases and Applications, 2003.
 
Show Less References