Abstract
Simulation and data mining can provide managers with decision support tools. However, the heart of data mining is knowledge discovery; as it enables skilled practitioners with the power to discover relevant objects and the relationships that exist between these objects, while simulation provides a vehicle to represent those objects and their relationships. In this chapter, the authors will propose an intelligent DSS framework based on data mining and simulation integration. The main output of this framework is the increase of knowledge. Two case studies will be presented, the first one on car market demand simulation. The simulation model was built using neural networks to get the first set of prediction results. Data mining methodology used named ANFIS (Adaptive Neuro-Fuzzy Inference System). The second case study will demonstrate how applying data mining and simulation in assuring quality in higher education
TopIntroduction
Data mining techniques provide people with new power to research and manipulate the existing large volume of data. A data mining process discovers interesting information from the hidden data that can either be used for future prediction and/or intelligently summarizing the details of the data (Mei, and Thole, 2008).
On the other hand, Simulation is a powerful technique for systems representations; because it provides a concise way for knowledge encapsulation. Simulation can be used effectively supporting managers in decision making, especially in situations characterized by uncertainty. Simulation can provide realistic models for testing real-world decision making scenarios (what-if scenarios), and comparing alternative decisions in order to choose the best solution affecting company’s success, by enhancing profitability, market share, and customer satisfaction.
Simulation methodologies, such as what-if analysis, can provide the engine to analyze company’s policy changes. For example, adding new tellers to a bank, or adding new airline route, or changing the number of machines in a job shop (Better, Glover, and Laguna, 2007).
Using data mining can help recalibrating system simulation models in many real world applications, as it provides the insights gleaned from the hidden and interesting data patterns.
This chapter will be divided as the following: the next section will present the data mining and business intelligence techniques used in conjunction with simulation, different experiences on the integration of simulation and data mining will be presented, then we will propose an intelligent DSS framework based on data mining and simulation integration, finally the proposed framework will be validated using a two case studies on car market demand simulation, and applying data mining in assuring quality in higher education.
Key Terms in this Chapter
Data Mining (DM): Is the process of discovering interesting information from the hidden data that can either be used for future prediction and/or intelligently summarizing the details of the data (Mei, and Thole, 2008).
Business Intelligence (BI): Is an umbrella term that combines architectures, tools, data bases, applications, practices, and methodologies (Turban, Aronson, Liang, and Sharda, 2007). It is the process of transforming various types of business data into meaningful information that can help, decision makers at all levels, getting deeper insight of business.
Quality Assurance (QA): Is all those planned and systematic actions necessary to provide adequate confidence that a product or service will satisfy given requirements for quality.
Data Warehouse (DW): Is a physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format (Turban, Aronson, Liang, and Sharda, 2007).
Knowledge Management (KM): Is the acquisition, storage, retrieval, application, generation, and review of the knowledge assets of an organization in a controlled way.
Decision Support System (DSS): Is an approach (or methodology) for supporting making. It uses an interactive, flexible, adaptable computer-based information system especially developed for supporting the solution to a specific nonstructured management problem(Turban, Aronson, Liang, and Sharda, 2007).
Adaptive Neuro-Fuzzy Inference System (ANFIS): Is a data mining methodology based on a combination of fuzzy logic & neural networks by clustering values in fuzzy sets, membership functions are estimated during training, and using neural networks to estimate weights (Alnoukari, Alzoabi, and Hanna, 2008).