Reducing the gap between experts' knowledge and data: The TOM4D methodology

Pomponio, Laura; Goc, Marc Le

doi:10.1016/j.datak.2014.07.006

Cited by 14 publications

(5 citation statements)

References 44 publications

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“…Basically, such approaches are confronted with the mismatch between a priori models and raw data provided by sensors (cf. [6] for a detailed analysis of this problem). This problem is currently approached through the adaptation of models with available data sets [7].…”

Section: Related Workmentioning

confidence: 99%

“…The TOT constitutes the common mathematical framework of (i) a Knowledge Engineering methodology called TOM4D (Timed Observation Modeling for Diagnosis, cf. [6] for a complete introduction), specifically designed to model a monitored dynamic process, (ii) the KDD process TOM4L detailed with more detail sin section IV, and (iii) an abstraction method called TOM4A (Timed Observation Method for Abstraction, [6], [21]), inspired from the Knowledge Level of Newell [22] and the Method of Gradients of Abstraction of Floridi [20], allowing to reduce the gap between experts and machines. A Java tool called TOM4K (Timed Observations Management of Knowledge, Figure 1) implements the main concepts of the TOT.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Emergence of Regularities in the Stochastic Behavior of Human

Goc

Barthelot

Pascual³

2015

2015 IEEE International Conference on Data Mining Workshop (ICDMW)

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This paper aims to show that regularities emerge from the strongly stochastic properties of human behavior. To this aim, the paper presents the application of a new Knowledge Discovery in Database (KDD) process, called Timed Observations Mining for Learning (TOM4L), on the timed data provided by a smart building of offices of the southeast of France during 12 months from April 2011 to March 2012. The TOM4L process produces then 12 behavioral models of the white-collar workers of the office, one for each month of the studied period. This sequence of models put on the light the strongly stochastic properties of human behavior since they differ significantly from one month to another. This illustrates the intrinsic difficulty of discovering behavior rules from the timed data provided by a smart environment. Nevertheless, regularities clearly emerges from this sequence of behavioral models that are closely linked with seasons. Two seasons, a cold season of five month and a warm seasons of seven months, are clearly identified with this sequence but to this aim, more abstract models are required. Finally, this paper shows that the TOM4L approach is clearly operational and powerful for human behavior modeling in smart environments but higher abstraction levels of representation must be defined to discover more general behavior rules.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Emergence of Regularities in the Stochastic Behavior of Human

Goc

Barthelot

Pascual³

2015

2015 IEEE International Conference on Data Mining Workshop (ICDMW)

Self Cite

View full text Add to dashboard Cite

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“…They also reported a computer program entitled "TDSS" to assist both senior and junior site layout planning engineer. In [146], the authors provided a framework named "TOM4D" to integrate tacit knowledge and explicit knowledge. This framework has been used in security of the dam of Cubblize (France), and the resultant TOM4D models have been validated by the hydraulic dam experts of the French governmental organization (Cemagref), which controls the security of hydraulic civil engineering structures.…”

Section: Tacit Knowledge Applicationmentioning

confidence: 99%

A Review of Tacit Knowledge: Current Situation and the Direction to Go

Hao

Zhao

Yan

et al. 2017

Int. J. Soft. Eng. Knowl. Eng.

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Currently, tacit knowledge has attracted increasing research attention. However, the theoretical foundation of tacit knowledge is still not well formulated, because the researches are very disperse. This work provides a review of the current researches. First, the definition of tacit knowledge is discussed by answering several questions. Next, tacit knowledge sharing, tacit knowledge quantization are identified as two research topics in the current research community. Following that, the technical progress of each topic is summarized and analyzed. Finally, we provide a thumbnail of the researches and identify three research consensuses to answer where we are. While, seven research directions are identified to answer where we shall go.

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“…On the other hand, there is generally a lack of quality of information mainly due to collection processes and low operating complex data . It is necessary procedures for the collection and management of simpler data and adjusted to real needs, facilitating the interpretation and analysis . Here are some questions generally required by the area of production and maintenance, presented in an integrated way.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, 26 DM techniques have been reviewed . Nevertheless, the main revised methods taken into account to develop Graphical Analysis for Operation Management Method (GAOM) have been: Balanced Scorecard, Total Productive Maintenance, Overall Equipment Effectiveness, and the Graphical Analysis for Maintenance Management (GAMM) method…”

Section: Introductionmentioning

confidence: 99%

Graphical Analysis for Operation Management: A Graphical Method to Support Operation Decision Making

Viveros

Márquez

Martínez

et al. 2015

Quality & Reliability Eng

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This paper proposes a graphical method to easy decision‐making in industrial plants operations. The proposed tool ‘Graphical Analysis for Operation Management (GAOM) method’ allows to visualizing and analyzing production‐related parameters, integrating assets/systems maintenance aspects. This integration is based on the Total Productive Maintenance model, using its quantitative management techniques for optimal decision‐making in day‐to‐day operations. On the one hand, GAOM monitors possible production target deviations, and on the other, the tool illustrates different aspects to gain control on the production process, such as availability, repair time, cumulative production, or overall equipment effectiveness. Through appropriate information filtering, individual analysis by class of intervention (corrective maintenance, preventive maintenance, or operational intervention) and production level can be developed. Graphical Analysis for Operation Management (GAOM) integrates maintenance information (number of intervention, type of intervention, required/not required stoppage) with production information (cumulative production, cumulative defective products, and cumulative production target) during a certain timeframe (cumulative calendar time, duration of intervention). Then the tool computes basic performance indicators supporting operational decision‐making. GAOM provides interesting graphical outputs using scatter diagrams integrating indicators on the same graph. GAOM is inspired in the Graphical Analysis for Maintenance Management method, published by the authors (LB, AC, and PV) in 2012. Copyright © 2015 John Wiley & Sons, Ltd.

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Reducing the gap between experts' knowledge and data: The TOM4D methodology

Cited by 14 publications

References 44 publications

Emergence of Regularities in the Stochastic Behavior of Human

Emergence of Regularities in the Stochastic Behavior of Human

A Review of Tacit Knowledge: Current Situation and the Direction to Go

Graphical Analysis for Operation Management: A Graphical Method to Support Operation Decision Making

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