Concrete design rules can facilitate the designer to depict capable design and reliable products. However, it is difficult to deduce systematic design rules from previous experience and to modify the rules in a rule-based system. In the last decade, Case-Based Reasoning (CBR) has become an important methodology to solve the problem. The objective of this paper is to determine the appropriate case representation used in assembly part design for developing a CBR system. The designer can obtain real-time Early experimental results indicate that the case representation can appropriately represent the expertise and experience of assembly part design based on CBR methodology.

Planning for teaching imposes a significant burden on teachers, as teachers need to prepare different lesson plans for different classes according to various constraints. Statistical evidence shows that lesson planning in the Malaysian context is done in isolation and lesson plan sharing is limited. The purpose of this thesis is to investigate whether a case-based system can reduce the time teachers spend on constructing lesson plans. A case-based system was designed SmartLP. In this system, a case consists of a problem description and solution pair and an attributevalue representation for the case is used. SmartLP is a synthesis type of CBR system which attempts to create a new solution by combining parts of previous solutions in the adaptation. Five activities in the CBR cycle retrieve, reuse, revise, review and retain are created via three types of design: application, architectural and user interface. The inputs are the requirements and constraints of the curriculum and the student facilities available, and the output is the solution, i.e. appropriate elements of a lesson plan. The retrieval module consists of five types of search advanced search, hierarchical, Boolean, basic and browsing. Solving a problem in this system involves obtaining a problem description, measuring the similarity of the current problem to previous problems stored in a database, retrieving one or more similar cases and attempting to reuse the solution of the retrieved cases, possibly after adaptation. Case adaptation for multiple lesson plans helps teachers to customise the retrieved plan to suit their constraints. This is followed by case revision, which allows users to access and revise their constructed lesson plans in the system. Validation mechanisms, through case verification, ensure that the retained cases are of quality. A formative study was conducted to investigate the effects of SmartLP on performance. The study revealed that all the lesson plans constructed with SmartLP assistance took significantly less time than the control lesson plans constructed without SmartLP assistance, although they might have access to computers and other tools. No significant difference in writing quality, measured by a scoring system, was noticed for the control group, who constructed lesson plans on the same tasks without receiving any assistance. The limitations of SmartLP are indicated and the focus of further research is proposed. Keywords: Case-based system, CBR approach, knowledge acquisition, knowledge representation, case representation, evaluation, lesson planning.

Case-Based Reasoning (CBR) is an Artificial Intelligence (AI) methodology and a growing field of research. CBR uses past experiences to help solve new problems the system faces. To do so CBR is comprised of a few core parts, such as case representation, case library, case retrieval, and case adaptation. This thesis will focus on the case representation aspect of CBR systems and presents a scalable case representation for big data environments. One aspect of focus on big data environments is also the focus of a MapReduce environment. MapReduce is a software framework enabling the use of a Map and Reduce function to be executed over a network cluster. This thesis conducts a systematic literature review to gain an understanding of the current case representations used in various CBR systems. The systematic literature review presents two major types of case representations, hierarchical and vector-based representations. However, the review also finds that the field of case representation research to be lacking. Most papers were focused on other aspects of CBR systems, mainly case retrieval. This thesis also proposes the design of a scalable and distributed case representation. The proposed case representation is of a hierarchical nature and is designed in such a way that it can utilize a MapReduce environment for working with the case library in components such as case retrieval. In the proof of concept, part of the case representation was implemented and tested using two data-sets. One data-set contains EEG sensor data measuring sleepiness while the other contains information about employees health and time taken off work. These tests show the case representation adequately representing the respective data-sets. The strength of the proposed case representation method is further discussed using a cross of papers. These papers cover the use of XML structured data in both CBR and MapReduce showing how this case representation is suitable for both uses. This shows strong capabilities of the case representation being further implemented and the addition of a case retrieval method to utilize it.