Evaluation method of enterprise information quality based on QFD

Lin, Gu; Gu, Jing; Dong, Fangfang

doi:10.1109/cecnet.2011.5768548

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…The development of this matrix is one of the steps in Quality Function Deployment (QFD). It aims to link client needs to technical prerequisites developed by specialist, improving information visibility and ensuring quality at the conception of a product project (OAKLAND, 1994;CHENG et al, 1995;AL-HAKIM, 2004;LIN et al 2011) Aiming for quality, QFD and the House of Quality are applied in industry, agribusiness, services, as well as in organizational processes. Many of these applications already use other tools in conjunction with QFD, targeting improvements.…”

Section: Information Quality and The House Of Qualitymentioning

confidence: 99%

“…Diffuse logic has been present in a variety papers that discuss the application of QFD, and it has become an important key term for researching QFD application. In the search for the correlation between the terms QFD and Information Quality, authors (AL- HAKIM, 2004;PINTO, 2006;WANG et al, 2008;LIN et al 2011;HAIDER and LEE, 2012) have been adapting QFD and House of Quality in different manners to evaluate information quality. Research they performed has demonstrated that advancements have developed in this field of study.…”

Section: Information Quality and The House Of Qualitymentioning

confidence: 99%

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Strategic information quality utilizing the House of Quality

Koehler

Bastos

et al. 2020

Int J Innov Educ Res

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Living in the Age of Knowledge means living in search of innovation. That is, quality information and high valued added knowledge that can lead companies and individuals to the spotlight in a highly competitive and globalized world. Information is considered to be raw material for creating knowledge, which in turn, adds value to organizations, promotes innovation and puts the spotlight on organizations. For this reason, the objective of this study is to use a tool to analyze the quality of organizational strategic information in two phases. In the first phase, data and sources will be assessed; and in the second phase of the tool, strategic information and information guidance practices will be analyzed. Thereby, the study provides analyses of an organization a step-in advance, so as to improve its processes and tools to truly transform its strategic information into competitive advantage.

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Section: Information Quality and The House Of Qualitymentioning

confidence: 99%

Section: Information Quality and The House Of Qualitymentioning

confidence: 99%

Strategic information quality utilizing the House of Quality

Koehler

Bastos

et al. 2020

Int J Innov Educ Res

View full text Add to dashboard Cite

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“…There has been less previous evidence in some studies from the context, focused on analyzing the evolution of data quality [ 1 ], basic probability [ 2 ], information quality evaluation method [ 3 ], text mining techniques [ 4 ], research on data and information quality [ 5 ], evaluation methods for information quality criteria [ 6 ], and mainly higher accuracy for data quality [ 7 ]. Innovative and important aspects are highlighted for the main models applied in object learning, and are still being adopted in conjunction with numerous solutions and methodologies.…”

Section: Introductionmentioning

confidence: 99%

Active Actions in the Extraction of Urban Objects for Information Quality and Knowledge Recommendation with Machine Learning

Silva

Mendes

Blas

et al. 2022

Sensors

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Due to the increasing urban development, it has become important for municipalities to permanently understand land use and ecological processes, and make cities smart and sustainable by implementing technological tools for land monitoring. An important problem is the absence of technologies that certify the quality of information for the creation of strategies. In this context, expressive volumes of data are used, requiring great effort to understand their structures, and then access information with the desired quality. This study are designed to provide an initial response to the need for mapping zones in the city of Itajaí (SC), Brazil. The solution proposes to aid object recognition employing object-based classifiers OneR, NaiveBayes, J48, IBk, and Hoeffding Tree algorithms used together with GeoDMA, and a first approach in the use of Region-based Convolutional Neural Network (R-CNN) and the YOLO algorithm. All this is to characterize vegetation zones, exposed soil zones, asphalt, and buildings within an urban and rural area. Through the implemented model for active identification of geospatial objects with similarity levels, it was possible to apply the data crossover after detecting the best classifier with accuracy (85%) and the kappa agreement coefficient (76%). The case study presents the dynamics of urban and rural expansion, where expressive volumes of data are obtained and submitted to different methods of cataloging and preparation to subsidize rapid control actions. Finally, the research describes a practical and systematic approach, evaluating the extraction of information to the recommendation of knowledge with greater scientific relevance. Allowing the methods presented to apply the calibration of values for each object, to achieve results with greater accuracy, which is proposed to help improve conservation and management decisions related to the zones within the city, leaving as a legacy the construction of a minimum technological infrastructure to support the decision.

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