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Case Representation Methodology for a Scalable Case-Based Reasoning
Mälardalen University, School of Innovation, Design and Engineering.
2018 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

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.

Place, publisher, year, edition, pages
2018. , p. 23
Keywords [en]
Case-Based Reasoning, CBR, Case Representation, MapReduce
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-41116OAI: oai:DiVA.org:mdh-41116DiVA, id: diva2:1253127
Subject / course
Computer Science
Supervisors
Examiners
Available from: 2018-10-19 Created: 2018-10-03 Last updated: 2018-10-19Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • Other style
More styles
Language
  • de-DE
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  • en-US
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  • nn-NB
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  • Other locale
More languages
Output format
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