On Quality Measures for Case Base Maintenance

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

doi:10.1007/3-540-44527-7_22

Cited by 25 publications

(17 citation statements)

References 7 publications

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“…A very early approach using an equivalent notion in instance-based learning research was represented by Protos [16]. The definition of prototype is also very similar to the one of abstract case [17], obtained by merging two or more cases with the same solution in a single entity.…”

Section: Case-based Reasoningmentioning

confidence: 99%

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Prototype-based management of business process exception cases

Montani

2009

Appl Intell

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Business process optimization may require to deviate from a default process model, in response to unexpected situations, thus raising exceptions. In this paper, we present a system for supporting end users in handling exceptions in business process management, which exploits the case-based reasoning (CBR) methodology. CBR offers the advantage of relying on operative knowledge, thus reducing the cost of knowledge elicitation, with respect to other methodologies.To maintain and organize the case base, we resort to a type of generalized cases, known as prototypes. The use of prototypes allows us to structure the case base itself, thus speeding up retrieval, and avoiding redundancy. In our system prototypes are also intended as a means to help process engineers in defining revised versions of the process schema, in response to frequent exceptions.The system is currently in use at one of the largest logistics centres in Italy.

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Section: Case-based Reasoningmentioning

confidence: 99%

“…In particular, similarly to an abstract case [17], in our approach a prototype summarizes a set of ground cases sharing the same solution. Operatively, it has the same structure of a ground case, and its features are automatically calculated as follows.…”

Section: Case Base Maintenancementioning

confidence: 99%

Prototype-based management of business process exception cases

Montani

2009

Appl Intell

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“…Although every case in the diagnoses process is important, it might be redundant for the system, because maybe the system already has the knowledge to propose that diagnoses, or maybe some similar or more general cases already exists in the system. Maintenance of the system could be implemented using case properties [4,14].…”

Section: ) Matching 4) Adaptation/validation 5) Forgettingmentioning

confidence: 99%

Multiple Sclerosis Diagnoses--Case-Base Reasoning Approach

Kurbalija

Ivanović

Budimac

et al. 2007

Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS'07)

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Case-Based Reasoning (CBR) is the area of the artificial intelligence in which the new problems are solved by adapting the solutions of the previously successfully solved similar problems. Medicine is a suitable domain for application of CBR because the knowledge of medical experts consists of mixture of textbook knowledge and experience, which consists of cases. Architecture of the system for the diagnoses of multiple sclerosis disease, based on CBR is proposed in this paper.

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“…According to Preece and Shinghal anomalies are not errors but are symptoms for probable errors in a knowledge base. Analogously, Iglezakis et al [11,18] introduced quality measures for the detection of design anomalies for case-based knowledge. In the context of this paper we focus on areas in the knowledge base that are mainly responsible for worsening the knowledge design.…”

Section: Design Anomalies In Knowledge Basesmentioning

confidence: 99%

Refactoring Methods for Knowledge Bases

Baumeister

Puppe

Seipel

2004

Engineering Knowledge in the Age of the Semantic Web

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Abstract. The manual development of large knowledge systems is a difficult and error-prone task. In order to facilitate extensions to an existing knowledge base the structural design of the implemented knowledge needs to be improved from time to time. However, experts are often deterred even from important design improvements since some restructurings are too complex to handle. In this paper, we introduce a framework that allows for automated refactorings. Refactoring methods are well-defined and are executed in a semi-automated way. In this manner, the developer is supported during the process of restructuring of even large knowledge bases. Refactoring methods are usually applied to improve the design of the knowledge base; in this paper, we sketch some design anomalies that identify poor design of the knowledge base.

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On Quality Measures for Case Base Maintenance

Cited by 25 publications

References 7 publications

Prototype-based management of business process exception cases

Prototype-based management of business process exception cases

Multiple Sclerosis Diagnoses--Case-Base Reasoning Approach

Refactoring Methods for Knowledge Bases

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