IJITCS Vol. 9, No. 9, 8 Sep. 2017
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Organizational data, organizational goals ontology, metrics, dependency relationship
Organizational data is essential to assist domain experts and entrepreneurs for decision making process in relation to the organizational goals but the trustworthiness of organizational data in relation to achieving the organizational goals is often questioned because of the vast amount of organizational data available. This paper proposes a methodology to evaluate organizational data that relates to the organizational goals. This refers to the importance of assisting the organization to utilize relevance of organizational data from the vast amount of available data for decision making to the organizational goals. The aim of this paper is to identify dependency relationship of organizational data that match to the organizational goals and to define a metrics as an analysis approach to measure organizational data to be considered relevant to the organizational goals. The experiment is present to implement the propose methodology in the context of Australian economy. The contribution of this paper will serve as a first step in evaluation approach and analysis of organizational data that relates to the achievement of the organizational goals.
Tengku Adil Tengku Izhar, "Ontology Based Framework to Configure the Organizational Goal Analysis and Decision-Making", International Journal of Information Technology and Computer Science(IJITCS), Vol.9, No.9, pp. 53-59, 2017. DOI:10.5815/ijitcs.2017.09.05
[1]P. Christen, "A survey of indexing techniques for scalable record linkage and deduplication," IEEE Transaction on Knowledge and Data Engineering, vol. 24, pp. 1537-1555, 2012.
[2]T. A. T. Izhar, T. Torabi, I. Bhatti, and F. Liu, "Analytical dependency between organisational goals and actions: Modelling concept," in International Conference on Innovation and Information Management (ICIIM 2012) Chengdu, China, 2012.
[3]T. A. T. Izhar, T. Torabi, M. I. Bhatti, and F. Liu, "Recent developments in the organization goals conformance using ontology," Expert Systems with Applications, vol. 40, pp. 4252-4267, 2013.
[4]O. Romero and A. Abello, "A framework for multidimensional design of data warehouses from ontologies," Data & Knowledge Engineering, vol. 69, pp. 1138-1157, 2010.
[5]A. Mikroyannidis and B. Theodoulidis, "Ontology management and evolution for businee intelligence," International Journal of Information Management, vol. 30, pp. 559-566, 2010.
[6]M. S. Fox, M. Barbuceanu, and M. Gruninger, "An organisation ontology for enterprise modeling: Preliminary concepts for linking structure and behaviour," Computers in Industry, vol. 29, pp. 123-134, 1996.
[7]S. Sharma and K.-M. Osei-Bryson, "Organization-ontology based framework for implementing the business understanding phase of data mining projects," in International Conference on System Sciences, Hawaii, 2008, p. 27.
[8]G. Mansingh, K.-M. Osei-Bryson, and H. Reichgelt, "Building ontology-based knowledge maps to assist knowledge process outsourcing decisions," Knowledge Management Research and Practice, vol. 7, pp. 37-51, 2009.
[9]L. Rao, G. Mansingh, and K.-M. Osei-Bryson, "Building ontology based knowledge maps to assist business process re-engineering," Decision Support Systems, vol. 52, pp. 577-589, 2012.
[10]M. S. Fox, M. Barbuceanu, M. Gruninger, and J. Lin, "An organization ontology for enterprise modelling," in Simulation organizations: Computational models of institutions and groupsAAAI/MIT Press, ed, 1998, pp. 131-152.
[11]S.-H. Liao, W.-J. Chang, and C.-C. Lee, "Mining marketing maps for business alliances," Expert Systems with Applications, vol. 35, pp. 1338-1350, 2008.
[12]H.-C. Kum, D. F. Duncan, and C. J. Stewart, "Supporting self-evaluation in local government via Knowledge Discovery and Data Mining," Government Information Quarterly, vol. 26, pp. 295-304, 2009.
[13]E. Durham, Y. Xue, M. Kantarcioglu, and B. Malin, "Quantifying the correctness, computational complexity, and security of privacy-preserving string comparators for record linkage," Information Fusion, vol. 13, pp. 245-259, 2012.
[14]S. M. Freire, R. T. d. Almeida, M. D. B. Cabral, E. d. A. Bastos, R. C. Souza, and M. G. P. d. Silva, "A record linkage process of a cervical cancer screening database," Computer Method and Program in Biomedecine, vol. 108, pp. 90-101, 2012.
[15]D. P. Jutte, L. L. Roos, and M. D. Brownell, "Administrative record linkage as a tool for public health research," Annual Review Public Health vol. 32, pp. 91-108, 2011.
[16]D. Abril, G. Navarro-Arribas, and V. Torra, "Improving record linkage with supervised learning for disclosure risk assessment," Information Fusion, vol. 13, pp. 274-284, 2012.
[17]A. Karakasidis and V. S. Verykios, "Secure blocking+secure matching= secure record linkage," Journal of Computing Science and Engineering, vol. 5, pp. 223-235, 2011.
[18]C. E. Varghese and G. N. Sundar, "Record matching: Improving performance in classification," International Journal on Computer Science and Engineering, vol. 3, pp. 1207-1212, 2011.
[19]T. N. Herzog, F. J. Sheuren, and W. E. Winkler, Data quality and record linkage technique. Washington, USA: Spinger, 2007.
[20]B. Nugraha, I. Ekasurya, G. Osman and M. Alaydrus, " Analysing of porwer consumtion efficiency on various IoT and Cloud-based wireless health monitoring systems: A survey," International Journal of Information Technology and Computer Science, vol.9, pp. 31-39, 2017.
[21]H.J.Bhatti and B.B. Rad," Databases in cloud computing: A literature review," International Journal of Information Technology and Computer Science, vol.9, pp. 9-17, 2017.