Towards a Methodology for Data Mining Project Development: The Importance of Abstraction

González-Aranda, P.; Menasalvas, Ernestina; Millán, Socorro; Ruiz, Carlos; Segovia, Javier

doi:10.1007/978-3-540-78488-3_10

Cited by 7 publications

(7 citation statements)

References 11 publications

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“…The authors define a life cycle for the data mining process, and the involvement of human resources in this cycle, according to their role. However, Gonzalez-Aranda et al (2008) deplore the lack of a methodology for conducting large data mining projects, because of the lack of abstraction in the definition of the data mining process. The authors propose then a methodology for planning a data mining project, considering the data mining process steps and its life cycle, but without proposing a formal specification.…”

Section: Related Workmentioning

confidence: 99%

“…The problem of the lack of abstraction in the data mining process was mentioned by Gonzalez-Aranda et al (2008) and Pardillo et al (2008). Pardillo et al (2008) show the different models and existing standards for data mining process, and their limitations to provide intuitive artefacts to specify the data mining process, the authors then propose to apply the model driven architecture (MDA) (OMG, 2014a;Blanc, 2005) for data mining, to enable analysts to model their analyses easily and independently of platforms.…”

Section: Introductionmentioning

confidence: 99%

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A support architecture to MDA contribution for data mining

Meskine¹,

Bahloul²

2020

IJDMMM

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The data mining process is the sequence of tasks applied to data, in order to discover relations between them to have knowledge. However, the data mining process lacks a formal specification that allows it to be modelled independently of platforms. Model driven architecture (MDA) is an approach for the development of software systems, based on the use of models to improve their productivity. Several research works have been elaborated to align the MDA approach with data mining on data warehouses, to specify the data mining process in a very high level of abstraction. In our work, we propose a support architecture that allows positioning these researches in different abstraction levels, on the basis of several criteria; with the aim to identify strengths for each level, in term of modelling; and to have a clear visibility on the MDA contribution for data mining.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A support architecture to MDA contribution for data mining

Meskine¹,

Bahloul²

2020

IJDMMM

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“…We also omit any constraint that specifies the syntax of case filters. 5 AsCase stereotype defines the convenient query AsCase::isTime(e: Element) :…”

Section: A4 Stereotypes Of the Instancespecification Metaclassmentioning

confidence: 99%

“…Thus, analysts can concentrate on data-mining rather than on cleansing and integrating data [4]. Nevertheless, time-series analysis is carried out more as an art than a science [5]. It is traditionally performed on top of flat files which do not explicitly represent the 0950 underlying complex data relationships.…”

Section: Introductionmentioning

confidence: 99%

A UML profile for the conceptual modelling of data-mining with time-series in data warehouses

Zubcoff

Pardillo

Trujillo

2009

Information and Software Technology

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“…Nevertheless, most projects are being developed more as art than as science, making it difficult to understand, evaluate, and compare results as there is no standard methodology. A methodology based on RUP and CRISP-DM has been proposed [6] in order to address these weaknesses.…”

Section: State Of the Artmentioning

confidence: 99%

Advanced predictive quality control strategy involving different facilities

Ordieres-Meré

Marcos

Alba‐Elías

et al. 2012

Int J Adv Manuf Technol

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There are many industries that use highly technological solutions to improve quality in all of their products. The steel industry is one example. Several automatic surface-inspection systems are used in the steel industry to identify various types of defects and to help operators decide whether to accept, reroute, or downgrade the material, subject to the assessment process. This paper focuses on promoting a strategy that considers all defects in an integrated fashion. It does this by managing the uncertainty about the exact position of a defect due to different process conditions by means of Gaussian additive influence functions. The relevance of the approach is in making possible consistency and reliability between surface inspection systems. The results obtained are an increase in confidence in the automatic inspection system and an ability to introduce improved prediction and advanced routing models. The prediction is provided to technical operators to help them in their decision-making process. It shows the increase in improvement gained by reducing the 40 % of coils that are downgraded at the hot strip mill because of specific defects. In addition, this technology facilitates an increase of 50 % in the accuracy of the estimate of defect survival after the cleaning facility in comparison to the former approach. The proposed technology is implemented by means of software-based, multi-agent solutions. It makes possible the independent treatment of information, presentation, quality analysis, and other relevant functions.

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Towards a Methodology for Data Mining Project Development: The Importance of Abstraction

Cited by 7 publications

References 11 publications

A support architecture to MDA contribution for data mining

A support architecture to MDA contribution for data mining

A UML profile for the conceptual modelling of data-mining with time-series in data warehouses

Advanced predictive quality control strategy involving different facilities

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