1.1. Motivation
Problem-solving is an important process that enables corporations to create competitive advantages, especially in the manufacturing industry. Case-based reasoning (CBR) tech-niques (Chang et al., 1996; Kohno et al., 1997; Park et al., 1998; Yang et al. 2004) have been widely used to help workers solve problems. For example, based on these techniques, a decision support system was developed to facilitate problem-solving in a complex produc-tion process (Park et al., 1998). CBR techniques have also been used to implement a self-improvement helpdesk service system (Chang et al., 1996), and integrated with the ART-Kohonen Neural Network (ART-KNN) to enhance fault diagnosis in electric motors (Yang et al., 2004).
Conventional CBR approaches focus on identifying similar problems without explor-ing the information needs of workers and relevant context of situations durexplor-ing prob-lem-solving tasks. Probprob-lem-solving is a complex process that includes a series of uncertain situations and operational actions. Moreover, it is usually knowledge intensive and workers need to access relevant information in order to identify the causes of a situation and take appropriate action to solve it. Situation features are usually occurred according to the context characteristics of problem. Due to the uncertain features of situations, several causes and possible solutions may exist for a specific situation. For example, in a production process, a significant decline in performance may be due to poor materials, inexperienced workers, or faulty machinery. Thus, possible solutions would include replacing the poor materials, re-training the workers, or repairing the faulty machinery. The causes and possible solutions are usually hidden in relevant data resources and difficult to extract. In such uncertain envi-ronments, situation features collected by system are usually partial or incomplete. Workers need to use knowledge gathered and inferred from relevant context information and previous problem-solving experience to clarify the causes and take appropriate action effectively.
Thus, identifying similar cases through CBR is not sufficient to solve problems. An effective knowledge support system is essential so that workers have the information necessary to identify the causes of a problem and take appropriate action to solve it.
1.2. Goals
According to the motivation, this work lists major goals as follows:
z Analyze collected attributes of situation/action of problem-solving;
z Based on CBR and data mining techniques, design a system framework of knowledge support for problem-solving;
z Identify similar situations/actions by CBR;
z Discovery of situation/action profile and knowledge patterns;
z Construct a knowledge support network for knowledge recommendation;
z Implement a prototype system to demonstrate the effectiveness of proposed framework;
z Analyze collected attributes and features of situation for problem-solving;
z Based on CBR, data mining, and rule inference techniques, design a system framework of context-based knowledge support for problem-solving;
z Identify similar context-based situations by CBR;
z Discovery of context-based situation profile and relevant knowledge (e.g., knowledge patterns and relevant knowledge documents);
z Implement a prototype system to demonstrate the effectiveness of proposed framework.
1.3. Contributions
In this work, we propose a mining-based knowledge support system for problem solving. Besides adopting CBR to identify similar situations and the action taken to solve them, we adopt text mining (Automatic Indexing) and rule inference techniques to com-pensate for the shortcomings of CBR technique. For specific situations or actions, their situation/action attributes, features, context characteristics and relevant information (documents) accessed by workers is recorded in a problem-solving log. Historical codified knowledge (textual documents), i.e., experience and know-how extracted from previous problem-solving logs, can provide valuable knowledge for solving the current problem.
The proposed system employs Information Retrieval (Automatic Indexing) techniques to extract the key concepts of relevant information necessary to handle a specific situation or action. The extracted key concepts form a situation/action profile that models the informa-tion needs of workers for a specific problem-solving task. The system can then uses the
situation/action profile to gather existing and new relevant knowledge documents for spe-cific situation/action. We employ association rule mining methods to discover deci-sion-making knowledge rules about frequently adopted actions taken to handle specific situations. These rules are generated as knowledge support to help workers take the appro-priate action to solve a specific situation. Furthermore, the problem-solving process in-cludes a series of uncertain situations and operational actions, and preceding situations or actions may trigger subsequent problem situations. Therefore, workers need to gather such triggering information (chain reactions) to determine appropriate action. For example, if an unstable system causes production to decline, the solution may be to reboot the system.
However, this may result in breakage of materials, which would increase production costs.
The proposed approach applies sequential pattern mining methods to discover dependency knowledge which represents frequent chain-reactions. The knowledge helps workers make appropriate action plans. The discovered profiles and knowledge rules are used to construct a knowledge support network, which provides workers with relevant situation/action in-formation, as well as decision-making and dependency knowledge. A prototype system is developed to demonstrate the effectiveness of the knowledge support network.
Moreover, we adapt system framework to provide context-based knowledge support for problem-solving. The adapted system employs constraint-based association rule mining methods to discover context-based inference rules from the problem-solving log. Con-text-based inference rules identify inferred associations between situation features and relevant context characteristics. Based on the discovered context-based inference rules, the system infers more situation features to assist CBR in situation identification. The proposed system employs Information Retrieval (Automatic Indexing) techniques to extract the key concepts of relevant information necessary to handle a specific situation. The extracted key concepts form a context-based situation profile that models the information needs of work-ers for handling problem situation in certain context. The system can then uses the con-text-based situation profile to gather existing and new relevant knowledge documents for specific situation according to the context information. Furthermore, the adapted system continually infers situation features to form the context-based knowledge patterns which provide workers with relevant inferred knowledge (inferred situation features and relevant context-based inference rules), as well as context-based decision-making and dependency knowledge.
1.4. Organization
The remainder of this work is organized as follows. Chapter 2 reviews related works on knowledge discovery and problem-solving. Chapter 3 introduces the knowledge require-ments of knowledge support for problem-solving. Chapter 4 describes the knowledge sup-port based on CBR and data mining techniques, including knowledge supsup-port framework for problem-solving, discovery of problem-solving knowledge, knowledge support for prob-lem-solving, and a prototype system implementation. Chapter 5 illustrates the knowledge support based on CBR, data mining, and rule inference techniques, including context-based knowledge support framework for problem-solving, discovery of context-based prob-lem-solving knowledge, the prototype system, discussions, and comparisons. Finally, we summarize this work and describe the future works in Chapter 6.