Rough Sets And Decision Analysis

Pawlak, Zdzisław

doi:10.1080/03155986.2000.11732405

Cited by 35 publications

(26 citation statements)

References 33 publications

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“…The search strategy used to retrieve the cases determines the effectiveness and efficiency of case retrieval. For reducing the data set and assigning weights to the case feature attributes we use the rough set theory [9], [10], [18]. Rough set theory is a mathematical tool to deal with problems on vagueness and uncertainty.…”

Section: Case-based Reasoning Methods Based On Rough Setsmentioning

confidence: 99%

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-based Reasoning Methods Based On Rough Setsmentioning

confidence: 99%

Analyzing Massive Machine Maintenance Data in a Computing Cloud

Bahga

Madisetti

2012

IEEE Trans. Parallel Distrib. Syst.

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“…We built an if-then rule model using a supervised learning method based on Rough Sets (Pawlak 1991;Komorowski 1999;. It associated Gene Ontology (GO) classes of biological processes (The Gene Ontology Consortium 2000) with minimal features of temporal gene transcript profiles from the fibroblast serum response in a data set provided by Iyer et al (1999).…”

Section: Department Of Cancer Research and Molecular Medicine Norwegmentioning

confidence: 99%

“…The present version used ROSETTA kernel version 1.0.1 and was further developed to meet the requirements of knowledge discovery in molecular biology. Rough Set theory (Pawlak 1991;Komorowski 1999;Skowron et al 2002) constitutes a mathematical framework for inducing minimal decision rules (if-then rules) from examples. The general idea is to use Boolean reasoning to obtain minimal sets of features with the same discriminatory properties as the full set of features.…”

Section: Training the Rule Modelmentioning

confidence: 99%

“…A Rough Set-based supervised learning method (Pawlak 1991;Komorowski 1999;Skowron et al 2002) was used to generate the model from the 549 training examples represented by their GO biological process annotations and by their gene expression levels. To accommodate the high complexity of the temporal gene expression profiles observed for genes in one biological process class (see Figs.…”

Section: Generating the Rule Modelmentioning

confidence: 99%

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Predicting Gene Ontology Biological Process From Temporal Gene Expression Patterns

Lægreid¹,

Hvidsten²,

Midelfart³

et al. 2003

Genome Res.

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The aim of the present study was to generate hypotheses on the involvement of uncharacterized genes in biological processes. To this end, supervised learning was used to analyze microarray-derived time-series gene expression data. Our method was objectively evaluated on known genes using cross-validation and provided high-precision Gene Ontology biological process classifications for 211 of the 213 uncharacterized genes in the data set used. In addition, new roles in biological process were hypothesized for known genes. Our method uses biological knowledge expressed by Gene Ontology and generates a rule model associating this knowledge with minimal characteristic features of temporal gene expression profiles. This model allows learning and classification of multiple biological process roles for each gene and can predict participation of genes in a biological process even though the genes of this class exhibit a wide variety of gene expression profiles including inverse coregulation. A considerable number of the hypothesized new roles for known genes were confirmed by literature search. In addition, many biological process roles hypothesized for uncharacterized genes were found to agree with assumptions based on homology information. To our knowledge, a gene classifier of similar scope and functionality has not been reported earlier.

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“…In this paper, a cloud computing-based computing framework with text case-based reasoning (TCBR) is proposed, which borrows the ideas from [15,[17][18][19]. The main idea is to extract fault features by text mining, reduce attributes by rough set theory [20][21][22][23][24][25], and solve the fault diagnosis and predication problem by deploying a CBR module based on the Hadoop platform with MapReduce framework [26,27], which is a computing paradigm for Big Data management created at Google. This computing paradigm is able to scale up the computing to thousands of processors and terabytes (or petabytes) of data.…”

Section: Introductionmentioning

confidence: 99%

Text Case-Based Reasoning Framework for Fault Diagnosis and Predication by Cloud Computing

Zhong

Wang

et al. 2018

Mathematical Problems in Engineering

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In Discrete Event System, such as railway onboard system, overwhelming volume of textual data is recorded in the form of repair verbatim collected during the fault diagnosis process. Efficient text mining of such maintenance data plays an important role in discovering the best-practice repair knowledge from millions of repair verbatims, which help to conduct accurate fault diagnosis and predication. This paper presents a text case-based reasoning framework by cloud computing, which uses the diagnosis ontology for annotating fault features recorded in the repair verbatim. The extracted fault features are further reduced by rough set theory. Finally, the case retrieval is employed to search the best-practice repair actions for fixing faulty parts. By cloud computing, rough set-based attribute reduction and case retrieval are able to scale up the Big Data records and improve the efficiency of fault diagnosis and predication. The effectiveness of the proposed method is validated through a fault diagnosis of train onboard equipment.

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Rough Sets And Decision Analysis

Cited by 35 publications

References 33 publications

Analyzing Massive Machine Maintenance Data in a Computing Cloud

Analyzing Massive Machine Maintenance Data in a Computing Cloud

Predicting Gene Ontology Biological Process From Temporal Gene Expression Patterns

Text Case-Based Reasoning Framework for Fault Diagnosis and Predication by Cloud Computing

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