Maintenance Memories: Beyond Concepts and Techniques for Case Base Maintenance

Iglezakis, Ioannis; Reinartz, Thomas; Roth–Berghofer, Thomas

doi:10.1007/978-3-540-28631-8_18

Cited by 12 publications

(5 citation statements)

References 8 publications

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“…Consequently, architectures and systems have been developed which support this process [3] [14] and also look into the life-time aspect concerned with case-based maintenance. …”

Section: Case-based Reasoning Process Modelmentioning

confidence: 99%

Mining Sparse and Big Data by Case-based Reasoning

Perner¹

2014

Procedia Computer Science

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The increasing use of digital media in daily life has resulted in a need for novel multimedia data analysis techniques. Case-based Reasoning (CBR) solves problems using the already stored knowledge, and captures new knowledge, making it immediately available for solving the next problem. Therefore, case-based reasoning can be seen as a method for problem solving, and also as a method to capture new experience and make it immediately available for problem solving. Therefore, CBR can mine sparse and big data. It can be seen as a learning and knowledge-discovery approach, since it can capture from new experience some general knowledge, such as case classes, prototypes and some higher-level concept. In this talk, we will explain the case-based reasoning process scheme. We will show what kinds of methods are necessary to provide all the functions for such a computer model. We will develop the bridge between CBR and Statistics and show how casebased reasoning can mine big and sparse data. Examples are being given based on multimedia applications. Finally, we will show recent new developments and we will give an outline for further work.

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“…Consequently, architectures and systems have been developed which support this process [3] [14] and also look into the life-time aspect concerned with case-based maintenance. …”

Section: Case-based Reasoning Process Modelmentioning

confidence: 99%

Mining Sparse and Big Data by Case-based Reasoning

Perner¹

2014

Procedia Computer Science

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“…Figure 3 An extension of the classical 4-stage CBR model to emphasize the importance of maintenance in overall system performance, illustrating the setup, initialization, application and maintenance phases of the SIAM methodology for maintaining CBR systems. Adapted from (Iglezakis et al, 2004).…”

Section: Case-base Maintenancementioning

confidence: 99%

“…Reinartz et al (2001) propose to extend the classic four-stage CBR cycle shown in Figure 1 to include two new steps, a review step to monitor the quality of system knowledge and a restore step which selects and applies maintenance operations. Their revised model, shown in Figure 3, emphasizes the important role of maintenance in modern CBR and indeed proposes that the concept of maintenance encompass the retain, review and restore steps (Iglezakis et al, 2004).…”

Section: Case-base Maintenancementioning

confidence: 99%

Retrieval, reuse, revision and retention in case-based reasoning

Mántaras

McSherry

Bridge

et al. 2005

The Knowledge Engineering Review

443

185

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Case-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision, and retention. R. LÓPEZ DE MÁNTARAS ET AL.

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“…Items are a uniform representation for knowledge in the sense that all "data", "information" and "knowledge" things are represented in the same way. The insights gained by analysing the maintenance of items leads naturally to an understanding of the maintenance of if-then rules, conventional programs and other knowledge representation paradigms [3]. The integrity of items represented in this model is maintained by following maintenance linksthe structure of these links is simplified by a decomposition process.…”

Section: Introductionmentioning

confidence: 99%

Maintaining knowledge with a formal model

Debenham

2006

Appl Intell

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Knowledge base maintenance is managed by constructing a formal model. In this model the representation of each chunk of knowledge encapsulates the knowledge in a set of declarative rules, each of which in turn encapsulates the knowledge in a set of imperative programs. In this model an "item" is the unit of knowledge representation. Items are at a higher level of abstraction than rules. Understanding what has to be done to maintain the integrity of an item leads to a specification of the modifications to the set of programs that implement it. An analysis of the maintenance of the formal model is achieved by introducing maintenance links. Analysis of the maintenance links shows that they are of four different types. The density of the maintenance links is reduced by transforming that set into an equivalent set. In this way the knowledge base maintenance problem is analysed and simplified. A side benefit of knowledge items as a formalism is that they contain knowledge constraints that protect the knowledge from unforeseen modification.

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Maintenance Memories: Beyond Concepts and Techniques for Case Base Maintenance

Cited by 12 publications

References 8 publications

Mining Sparse and Big Data by Case-based Reasoning

Mining Sparse and Big Data by Case-based Reasoning

Retrieval, reuse, revision and retention in case-based reasoning

Maintaining knowledge with a formal model

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