Advanced Principles for Improving Database Design, Systems by Keng Siau, John Erickson

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By Keng Siau, John Erickson

Contemporary years have witnessed sizeable leaps within the power of database applied sciences, making a new point of strength to advance complicated purposes that upload worth at unheard of degrees in all components of data administration and usage. Parallel to this evolution is a necessity within the academia and for authoritative references to the study during this region, to set up a entire wisdom base that may allow the knowledge know-how and managerial groups to gain greatest merits from those options.

Advanced ideas for bettering Database layout, structures Modeling, and software program improvement offers state of the art study and research of the latest developments within the fields of database structures and software program improvement. This booklet presents academicians, researchers, and database practitioners with an exhaustive choice of reports that, jointly, characterize the country of data within the box.

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Additional resources for Advanced Principles for Improving Database Design, Systems Modeling, and Software Development

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Journal of Organizational Computing and Electronic Commerce, 9(1), 7-32. Milton, K. , & Kazmierczak, E. (2004). An ontology of data modelling languages: A study using a common-sense realistic ontology. Journal of Database Management, 15(2), 19-38. Mylopoulos, J. (1992). Conceptual modeling and telos. In P. Locuopoulos & R. ), Conceptual modeling, databases and cases. New York: John Wiley & Sons. Odell, J. (2002). Objects and agents compared. Journal of Object Technology, 1(1), 41-53. Opdahl, A.

Paper presented at the 37th Hawaii International Conference on System Sciences (HICSS-37), Big Island, Hawaii. , & Ramesh, R. (2005). MibML: A conceptual modeling grammar for integrative business information systems using the agent metaphor (Working Paper). New York: State University of New York at Buffalo, School of Management. This work was previously published in Journal of Database Management, Vol. 18, Issue 1, edited by K. Siau, pp. 1-19, copyright 2007 by IGI Publishing, formerly known as Idea Group Publishing (an imprint of IGI Global).

The clear semantics also make it easy for systems developers to identify correct MibML constructs to represent problem domain knowledge. Take goal and task for example. It is always difficult to differentiate a goal and a task. A real-world instance “order inventory” can either be modeled as a goal or as a task depending on the level of abstraction. With a goal clearly defined as a stable state of an agent and a task as an internal transformation, we understand that a task changes states of an agent.

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