Defining Interaction Design Patterns to Extract Knowledge from Big Data

Iñiguez-Jarrín, Carlos; Panach, José Ignacio; López, Oscar Pastor

doi:10.1007/978-3-319-91563-0_30

Cited by 7 publications

(10 citation statements)

References 7 publications

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“…Four articles were excluded at the end of this process with an agreement of the three reviewers. More precisely, we excluded studies aiming to assess severity [8], to assess longitudinal data of cases and controls [9], describing big data management software with one example on genetic disease [10] and aiming to identify "noncommon" diseases without performing a diagnosis [11]. Sixty-eight articles were consequently retained at the end of the whole process ( Fig.…”

Section: Resultsmentioning

confidence: 99%

Diagnosis support systems for rare diseases: a scoping review

Faviez

Chen

Garcelon

et al. 2020

Orphanet J Rare Dis

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Introduction: Rare diseases affect approximately 350 million people worldwide. Delayed diagnosis is frequent due to lack of knowledge of most clinicians and a small number of expert centers. Consequently, computerized diagnosis support systems have been developed to address these issues, with many relying on rare disease expertise and taking advantage of the increasing volume of generated and accessible health-related data. Our objective is to perform a review of all initiatives aiming to support the diagnosis of rare diseases. Methods: A scoping review was conducted based on methods proposed by Arksey and O'Malley. A charting form for relevant study analysis was developed and used to categorize data. Results: Sixty-eight studies were retained at the end of the charting process. Diagnosis targets varied from 1 rare disease to all rare diseases. Material used for diagnosis support consisted mostly of phenotype concepts, images or fluids. Fifty-seven percent of the studies used expert knowledge. Two-thirds of the studies relied on machine learning algorithms, and one-third used simple similarities. Manual algorithms were encountered as well. Most of the studies presented satisfying performance of evaluation by comparison with references or with external validation. Fourteen studies provided online tools, most of which aimed to support the diagnosis of all rare diseases by considering queries based on phenotype concepts. Conclusion: Numerous solutions relying on different materials and use of various methodologies are emerging with satisfying preliminary results. However, the variability of approaches and evaluation processes complicates the comparison of results. Efforts should be made to adequately validate these tools and guarantee reproducibility and explicability.

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Section: Resultsmentioning

confidence: 99%

Diagnosis support systems for rare diseases: a scoping review

Faviez

Chen

Garcelon

et al. 2020

Orphanet J Rare Dis

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“…However, the lack of intuitive and interactive-usable mechanisms of such tools, transforms the analysis activity into a complex and time-consuming task. In order to provide a solution useful for clinical purposes, the data exploitation tools must enhance data discovery, enlarge visualization, allow the performance of data analysis operations and contextualize data by augmenting it [33]. One of the tasks that can be performed is related to the enhancement of Precision Medicine (PM).…”

Section: Exploitationmentioning

confidence: 99%

Smart Data for Genomic Information Systems: the SILE Method

Palacio

López

2018

CSIMQ

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During the last two decades, data generated by Next Generation Sequencing Technologies have revolutionized our understanding of human biology and improved the study on how changes (variations) in the DNA are involved in the risk of suffering a certain disease. A huge amount of genomic data is publicly available and frequently used by the research community in order to extract meaningful and reliable gene-disease relationships. However, the management of this exponential growth of data has become a challenge for biologists. Under such a Big Data problem perspective, they are forced to delve into a lake of complex data, spread in over one thousand heterogeneous repositories, represented in multiple formats and with different levels of quality; but when data are used to solve a concrete problem only, a small part of that "data lake" is really significant; this is what we call the "smart" data perspective. By using conceptual models and the principles of data quality management, adapted to the genomic domain, we propose a systematic approach called the SILE method to move from a Big Data to a Smart Data perspective. The aim of this approach is to populate an Information System with genomic data which are sufficiently accessible, informative and actionable to extract valuable knowledge.

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“…To meet the specified customer performance, cost, integrity goals, increase product development efficiency, Product Life-cycle Management (PLM) system, knowledgeware for managing model size, and Knowledge-based Enterprising (KBE) Strategy were proposed, which improved customer satisfaction, drove down the costs of new product development, reduced the complexity of product modeling, and promoted collaboration and knowledge sharing of designers (Prasad, 2014; Prasad and Rogers, 2005; Shintre and Prasad, 2011). In order to identify and design user data interactions related to knowledge extraction, Iniguez-Jarrin et al (2018) proposed a set of design user interface patterns. To manage information flows more effectively, Ammar-Khodja et al (2008) proposed a knowledge-oriented based engineering application methodology to support the integration of process planning knowledge in a CAD System.…”

Section: Introductionmentioning

confidence: 99%

Individualized and accurate eco-design knowledge push for designers: a CAD-based feedback knowledge push method for the eco-design

Zhang

Shao

Chen

et al. 2021

Concurrent Engineering

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In the conceptual design phase of eco-products, the evaluation of products eco-design is often considered by designers after determining the design scheme. In order to improve the eco-design efficiency, a CAD-based feedback knowledge push method was proposed to meet the designer’s eco-design knowledge requirements. Combine with the designer knowledge background, classified eco-design knowledge, and decomposed design tasks, eco-design knowledge requirements of designers can be obtained. Based on the cosine similarity algorithm of the knowledge vector space model (VSM), the knowledge requirements of the designer and the knowledge in the knowledge database are matched, and the eco-knowledge is actively pushed to the designer. The feedback and evaluation of designers on the initial push of knowledge was recorded by the system. When the designer is assigned relevant eco-design tasks again, the system conducts secondary filtering of eco-design knowledge through previous feedback records and pushes the filtered knowledge to designers, so as to achieve accurate feedback knowledge push. A CAD-based eco-design knowledge push prototype system for automotive products is developed. The eco-design of the front-end module of the automotive is used as an example to verify the effectiveness of the above method.

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Defining Interaction Design Patterns to Extract Knowledge from Big Data

Cited by 7 publications

References 7 publications

Diagnosis support systems for rare diseases: a scoping review

Diagnosis support systems for rare diseases: a scoping review

Smart Data for Genomic Information Systems: the SILE Method

Individualized and accurate eco-design knowledge push for designers: a CAD-based feedback knowledge push method for the eco-design

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