Corporate memory (CM) is a major asset of any modern organization, and provides the company the special edge that makes it more competitive. Up to date, CM has not been applied to tourism, whose environmental change is rapid in both the supply environment and ecotourist behavior. In addition, the ecotourism often possesses numerous qualitative data. The qualitative nature of the data makes them difficult to analyze by standard statistical techniques. The rough set approach is one of the approaches that are suitable for processing qualitative information. The rough set theory offers a large collection of tools for knowledge discovery from data by computing the most relevant sets of attributes called reducts. In this paper, corporate memory in an organization uses the rough set as an approach to mine data due to the qualitative nature in ecotourism. A rough set based corporate memory is proposed to resolve the problems: (1) the local ecotourism association do not understand travelling purposes of tourists and how the tourists feedback to the place. (2) The local ecotourism association do not know how to improve the travel package such that the valued eco-tourists are losing. The proposed approach allows the local ecotourism association to understand tourist' behavior as a basis, which aims to achieve the goal of the sustainable ecotourism development.

Quality assurance (QA) is a process employed to ensure a certain level of quality in a product or service. One of the techniques in QA is to predict the product quality based on the product features. However, traditional QA techniques have faced some drawbacks such as heavily depending on the collection and analysis of data and frequently dealing with uncertainty processing. In order to improve the effectiveness during a QA process, a hybrid approach incorporated with data mining techniques such as rough set theory (RST), fuzzy logic (FL) and genetic algorithm (GA) is proposed in this paper. Based on an empirical case study, the proposed solution approach provides great promise in QA.

The incremental technique is a way to solve the issue of added-in data without re-implementing the original algorithm in a dynamic database. There are numerous studies of incremental rough set based approaches. However, these approaches are applied to traditional rough set based rule induction, which may generate redundant rules without focus, and they do not verify the classification of a decision table. In addition, these previous incremental approaches are not efficient in a large database. In this paper, an incremental rule-extraction algorithm based on the previous rule extraction algorithm is proposed to resolve the aforementioned issues. Applying this algorithm, while a new object is added to an information system, it is unnecessary to re-compute rule sets from the very beginning. The proposed approach updates rule sets by partially modifying the original rule sets, which increases the efficiency. This is especially useful while extracting rules in a large database.

At the beginning of the 21st century, the emergence of knowledge management is viewed as a natural evolution. Knowledge management is defined as the formal management of knowledge for facilitating creation, access, and reuse of knowledge, typically by using advanced technology. To be easily communicated and shared, tacit knowledge has to be explicated as explicit knowledge (e.g., in product specification or a scientific formula or production rules); and this explicit knowledge has to be shared as applicable data through the use of information technology. Product design is such a business process that a great part of the design knowledge is often a tacit type, being difficult to share, or available only in forms of natural language documents. However, the expertise recorded in these documents is an essential resource of successful competition in the market. This paper presents a knowledge explication and sharing approach; specifically focusing on the knowledge management of modular product design. The solution approach involves modeling modular products, formulating the explicit knowledge, discovering new design knowledge with data mining, and sharing the knowledge with web services technology. The proposed approach is to be applied to an actual case of motherboard design/assembly in one of the largest PC manufacturing enterprises.

Analytical knowledge is distributed among domain experts, analysts, and data-storage systems. Extracting such knowledge from databases is of interest to corporations. The traditional top-down development of corporate memory is not appropriate for modern organizations because of the distributed nature of information. This paper proposes models of analytical knowledge and new ways of developing corporate memory by using an extensible markup language (XML). It aims at efficient exploration of useful knowledge by mining the Web. The proposed approach of modeling analytical knowledge is explicit and sharable. The concepts introduced in the paper have been demonstrated with a manufacturing case study.

Corporation managers make informed decisions based upon a combination of judgment and knowledge from various departments such as marketing, sales, research, development, manufacturing, and finance. Ideally, all relevant knowledge should be brought together before judgment is exercised. However, determining the knowledge requirements and obtaining pertinent, consistent, and up-to-date knowledge across a large company is a complex process. Crucial-knowledge determination is a complex process: specifically the identification of crucial knowledge and knowledge requirements. In this paper, a methodology is developed for modeling the knowledge requirements and the associated tasks for collecting the knowledge simultaneously. This methodology provides a valuable contribution in knowledge-management systems by defining a major plan for discovering issues-oriented knowledge. One procedure and one heuristic algorithm are illustrated with numerical examples.

Businesses are undergoing a major paradigm shift, moving from traditional management into a world of agile organizations and processes. An agile corporation should be able to rapidly respond to market changes. For this reason, corporations have been seeking to develop numerous information technology (IT) systems to assist with the management of their business processes. Many of the coming new business processes may contain embedded intelligent agent-based systems. Agent technology looks set to radically alter not only the way in which computers are interacted, but also the way complex processes, e.g., product development, are conceptualized and built. The paper presents a fuzzy approach based on an intelligent agent framework to develop modular products. This approach aims to address the research issue: "How can modular design be carried out through intelligent agents to meet a customer's fuzzy requirements using modules that come from suppliers that are geographically separated and operate on differing computer platforms?" The proposed methodology is applied to a real-world case that involves module-based synthesis at one of largest distribution centers in the world.

Decision support system (DSS) is an interactive computer-based system, which helps decision makers utilize data and models to solve unstructured problems. Current business is undergoing a major paradigm shift that is taking it from traditional management into a world of agility. An agile corporation should be able to rapidly respond to the market changes. Solution approaches to quickly support decision making and model analyzing are crucial in business management. The approaches may support solutions for the decision makers who are geographically separated and operate on differing computer platforms. In this paper, a generalized label-correcting (GLC) approach is developed to analyze the models represented with logical networks. This GLC approach is agile because by combining various operators and comparators, different types of paths in the networks can be solved with one algorithm for different values of the initial node. The main contribution of this paper is to provide an approach in analyzing the logical networks, and the approach is implemented through the World Wide Web (WWW) regardless of the geographical constraints and platforms used.

Modularity refers to the use of common units to create product variants. This paper aims at the development of models and solution approaches to the modularity problem for mechanical, electrical, and mixed process products (e.g., electromechanical products). To interpret various types of modularity, e.g., component-swapping, component-sharing and bus modularity, a matrix representation of the modularity problem is presented. The decomposition approach is used to determine modules for different products. The representation and solution approaches presented are illustrated with numerous examples. The paper presents a formal approach to modularity allowing for optimal forming of modules even in the situation of insufficient availability of information. The modules determined may be shared across different products.