Category: Decision Support Systems Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 2200 DOI: 10.4018/978-1-5225-2255-3.ch191 Rough-Set-Based Decision Model for Incomplete Information Systems INTRODUCTION The increasing use of computers and information are getting necessity with increased expertise and digital data storage, that becomes compulsory and importance for usage. Rapidly increasing data traf- fic has revealed a major case for efficient manner with the use of the big data. Developed algorithm and suggested model can help using of the data in database structure and evaluate and process of the data for taking managerial decisions with rough sets approach. Rough sets derived from the fuzzy logic approach to perform the analysis of the incomplete information and uncertain structure. In 1982, this approach proposed by Pawlak, for discover the knowledge from big databases which is used in classification of the attributes recently. Also, Rough set is made as incomplete, inadequate and vague information organizing; it makes it suitable for data analysis (Pawlak, 1994, 1995). Databases use this data and evaluate manage- rial decisions in the process of data mining has become imperative that we give the name of the emergence of the field. The rough set is a concept derived from the fuzzy logic approach to carry out the analysis of structures with uncertain data mining techniques. The decision will be developed in conjunction with computerized decision support model, giving more efficient automation systems with algorithms that are targeted. Our suggested decision support system covers the inputs, user knowledge and expertise, outputs and decision components. In addition, data access, interactive mode, adaptability and flexible mode provides the solve problems and make decision process for certain and uncertain data with suggested rough set based algorithm structure. This paper presents rough set based decision model, a process in which the suggested algorithm and decision support model and evaluate with the knowledge in databases and the knowledge received externally. The following sections include some necessary literature review and the rough based decision model approach. Then follow the future trends and the conclusion. BACKGROUND Rough set theory is an extension of set theory which proposed by Pawlak (1991) for describe and classify the incomplete or insufficient information. Besides it is mathematical tool that overcome the uncertainties and doubts. Also it verifies logic, and allows inconsistent data and no certainty to the discovery of incomplete implications. It is made as incomplete, inadequate and vague information by organizing. Rough set organizes the suitable data for analysis. In real-world applications may includes the some uncertain and incomplete attributes in the knowledge representation systems in dynamic Safiye Turgay Sakarya University, Turkey Orhan Torkul Sakarya University, Turkey Tahsin Turgay Sakarya University, Turkey