Automated Case Base Creation and Management

Yang, Chunsheng; Orchard, Robert; Farley, Benoit; Zaluski, Marvin

doi:10.1007/3-540-45034-3_13

Cited by 6 publications

(6 citation statements)

References 7 publications

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“…The ability to automatically create case bases from the legacy data in databases at the initial stage and to automatically collect new cases at the online runtime stage, is important for the success of CBR systems [14]. Yang et al [14] have proposed a scheme for automated case creation and case base management to reduce the effort required for case creation and to overcome the difficulty of effective creation of high-quality cases.…”

Section: Case-base Creation Modulementioning

confidence: 99%

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Analyzing Massive Machine Maintenance Data in a Computing Cloud

Bahga

Madisetti

2012

IEEE Trans. Parallel Distrib. Syst.

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Abstract-We present a novel framework, CloudView, for storage, processing and analysis of massive machine maintenance data, collected from a large number of sensors embedded in industrial machines, in a cloud computing environment. This paper describes the architecture, design, and implementation of CloudView, and how the proposed framework leverages the parallel computing capability of a computing cloud based on a large-scale distributed batch processing infrastructure that is built of commodity hardware. A case-based reasoning (CBR) approach is adopted for machine fault prediction, where the past cases of failure from a large number of machines are collected in a cloud. A case-base of past cases of failure is created using the global information obtained from a large number of machines. CloudView facilitates organization of sensor data and creation of case-base with global information. Case-base creation jobs are formulated using the MapReduce parallel data processing model. CloudView captures the failure cases across a large number of machines and shares the failure information with a number of local nodes in the form of case-base updates that occur in a time scale of every few hours. At local nodes, the real-time sensor data from a group of machines in the same facility/plant is continuously matched to the cases from the case-base for predicting the incipient faults-this local processing takes a much shorter time of a few seconds. The case-base is updated regularly (in the time scale of a few hours) on the cloud to include new cases of failure, and these case-base updates are pushed from CloudView to the local nodes. Experimental measurements show that fault predictions can be done in real-time (on a timescale of seconds) at the local nodes and massive machine data analysis for case-base creation and updating can be done on a timescale of minutes in the cloud. Our approach, in addition to being the first reported use of the cloud architecture for maintenance data storage, processing and analysis, also evaluates several possible cloud-based architectures that leverage the advantages of the parallel computing capabilities of the cloud to make local decisions with global information efficiently, while avoiding potential data bottlenecks that can occur in getting the maintenance data in and out of the cloud.

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Section: Case-base Creation Modulementioning

confidence: 99%

“…Yang et al [14] have proposed a scheme for automated case creation and case base management to reduce the effort required for case creation and to overcome the difficulty of effective creation of high-quality cases.…”

Section: Case-base Creation Modulementioning

confidence: 99%

Analyzing Massive Machine Maintenance Data in a Computing Cloud

Bahga

Madisetti

2012

IEEE Trans. Parallel Distrib. Syst.

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“…Also, this approach can be used as a complimentary approach to the other case authoring approaches implemented in IDS. A case-base can be created using this case authoring approach and can later be extended with the Automated Case Creation System in IDS (Yang et al 2002). This approach eliminates problems solved by the technical documentation and allows for more computationally intensive approaches to focus on the undocumented problems that occur on the aircraft.…”

Section: Case-base Updatementioning

confidence: 99%

Case Authoring from Text and Historical Experiences

Zaluski

Japkowicz

Matwin

2003

Advances in Artificial Intelligence

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Abstract. The problem of repair and maintenance of complex systems, such as aircraft, cars and trucks is certainly a nontrivial task. Maintenance technicians must use a great amount of knowledge and information resources to solve problems that may occur. This paper describes a semi-automated tool that sorts through the mass of information a maintenance technician must consult in order to make a repair, thus helping him decide how to tackle the problem and thereby increasing his efficiency and, possibly, his reliability. Our tool was developed using state-of-the-art Case-Based Reasoning and Information Extraction technologies. More specifically, we developed a semi-automated Case Authoring method that creates a Case-Base in two steps. It begins by extracting knowledge from readily available resources such as technical documents and follows by complementing those cases using individual experiences in the maintenance organization. The case-base developed is a reflection of the knowledge encoded in the technical documentation and an authentication of the cases with real historical instances. Our case authoring approach is applied to the real world in the aerospace domain.

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“…CBR is one of the reasoning paradigms and is a feasible and effective solution to problems which are difficult to be solved with traditional methods such as model-based reasoning. CBR-based approaches have been widely applied to various real-world applications such as diagnostics [26] [32], design [27] [34], planning [30], finance [31], health care [33], and decisionmaking support [7][8][9][10][11][12]. Also, we are looking into applying agent technologies to ship collision avoidance.…”

Section: Introductionmentioning

confidence: 99%

Case learning for CBR-based collision avoidance systems

Liu

Yang

et al. 2010

Appl Intell

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Abstract.With the rapid development of case-based reasoning (CBR) techniques, CBR has been widely applied to real-world applications such as collision avoidance systems. A successful CBR-based system relies on a high-quality case base, and a case creation technique for generating such a case base is highly required. In this paper, we propose an automated case learning method for CBR-based collision avoidance systems. Building on techniques from CBR and natural language processing, we developed a methodology for learning cases from maritime affair records. After giving an overview on the developed systems, we present the methodology and the experiments conducted in case creation and case evaluation. The experimental results demonstrated the usefulness and applicability of the case learning approach for generating cases from the historic maritime affair records.

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Automated Case Base Creation and Management

Cited by 6 publications

References 7 publications

Analyzing Massive Machine Maintenance Data in a Computing Cloud

Analyzing Massive Machine Maintenance Data in a Computing Cloud

Case Authoring from Text and Historical Experiences

Case learning for CBR-based collision avoidance systems

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