A Data Quality in Use model for Big Data

Merino, Jorge; Caballero, Ismael; Rivas, Bibiano; Serrano, Manuel A.; Piattini, Mario

doi:10.1016/j.future.2015.11.024

Cited by 146 publications

(69 citation statements)

References 6 publications

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“…New models must be proposed to make sure that data are valid in different data lake zones to ensure the data quality. Automatic validation [13,20] in the raw data zone, users' comments of analytics in the access zone can be one of the solutions. • Data life-cycle management (DLM): it's necessary to define specific workflows to model the life-cycle of all the data that stored in different zones of a data lake and the relationships between different data.…”

Section: Data Lake Governancementioning

confidence: 99%

Data Lakes: Trends and Perspectives

Ravat

Yan

2019

Lecture Notes in Computer Science

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As a relatively new concept, data lake has neither a standard definition nor an acknowledged architecture. Thus, we study the existing work and propose a complete definition and a generic and extensible architecture of data lake. What's more, we introduce three future research axes in connection with our health-care Information Technology (IT) activities. They are related to (i) metadata management that consists of intra-and inter-metadata, (ii) a unified ecosystem for companies' data warehouses and data lakes and (iii) data lake governance.

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Section: Data Lake Governancementioning

confidence: 99%

Data Lakes: Trends and Perspectives

Ravat

Yan

2019

Lecture Notes in Computer Science

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“…Data quality is a shared concern in research, and in public health [45]. Therefore, data quality profiling as metadata should be integrated in big data infrastructure as proposed by Merino et al [46] in their "Data-Quality-in-Use model." Metadata definitions and ontologies for data sharing will enable the fingerprinting of data repositories and make the researchers aware of the quality of the available…”

Section: Recommendationsmentioning

confidence: 99%

Exploration Of The Timeliness Of ICD Codes In Administrative Databases: A Nationwide Study

Simon

Rance

Katsahian

et al. 2018

Preprint

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INTRODUCTION:The ICD codes are ubiquitously available in hospital information systems and have been used in a number of areas such as epidemiology, phenotype-genotype association mining, surveillance of the use of drugs and medical devices and health care evaluation. We aimed to analyze the timeliness of the 3-character ICD-10 codes collected in the French national hospital discharge summary database between 2008 and 2017 and we classified the codes according to their evolution. MATERIAL AND METHODS:We extracted all 3-character ICD-10 codes from all French hospital discharge summaries between 2008 and 2017. For each code and by the month, we computed a relative frequency; we also computed the overall amplitude of the study period.Temporal clustering, according to the SAX representation, was performed to classify the main evolution patterns. RESULTS:We extracted 238,334,751 encounters corresponding to 56,621,773 distinct patients. 1,006 ICD codes presented a variation of the relative amplitude of frequencies lower than 50%, 510 codes between 50% and 100% and 521 greater than 100%. Out of the 2,037 codes included in the study, we kept the 1,758 for the temporal clustering. Four clusters were identified, including a global increase and a global decrease patterns. DISCUSSION:The overall results showed a strong instability (i.e. large variation of frequency over time) of the use of ICD codes over time, with an important variation of the relative amplitude of the frequencies. We distinguished between external factors due to changes in billing, organization, policy or regulation and intrinsic factors due to epidemiological phenomenon. The detailed analysis of profiles show that the same cluster can contain profiles influenced by intrinsic epidemiological or external factors or both. Additional knowledge and sources are probably required to determine automatically the origin of the profile.

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“…Tables 1 and 2 provide a brief breakdown of the challenges from a general and data life cycle perspective. Although significant strides have been made to explain and explore big data management, existing data management frameworks do not encompass or incorporate all objective and subjective data needs from data retrieval to retirement, including all governance requirements (Merino, Caballero, Rivas, Serrano, & Piattini, 2016;Priebe & Markus, 2015;Rajagopalan & Vellaipandiyan, 2013).…”

Section: Literature Analysismentioning

confidence: 99%

Data Visualization in Support of Executive Decision Making

Moore

2017

IJIKM

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Aim/PurposeThis journal paper seeks to understand historical aspects of data management, leading to the current data issues faced by organizational executives in relation to big data and how best to present the information to circumvent big data challenges for executive strategic decision making. BackgroundThis journal paper seeks to understand what executives value in data visualization, based on the literature published from prior data studies. MethodologyThe qualitative methodology was used to understand the sentiments of executives and data analysts using semi-structured interview techniques. ContributionThe preliminary findings can provide practical knowledge for data visualization designers, but can also provide academics with knowledge to reflect on and use, specifically in relation to information systems (IS) that integrate human experience with technology in more valuable and productive ways. FindingsPreliminary results from interviews with executives and data analysts point to the relevance of understanding and effectively presenting the data source and the data journey, using the right data visualization technology to fit the nature of the data, creating an intuitive platform which enables collaboration and newness, the data presenter's ability to convey the data message and the alignment of the visualization to core the objectives as key criteria to be applied for successful data visualizations Recommendations for Practitioners Practitioners, specifically data analysts, should consider the results highlighted in the findings and adopt such recommendations when presenting data visualizations. These include data and premise understanding, ensuring alignment to the executive's objective, possessing the ability to convey messages succinctly and clearly to the audience, having knowledge of the domain to answer questions effectively, and using the right technology to convey the message. Recommendation for ResearchersThe importance of human cognitive and sensory processes and its impact in IS development is paramount. More focus can be placed on the psychological factors of technology acceptance. The current TAM model, used to describe use, identifies perceived usefulness and perceived ease-of-use as the primary consid-

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A Data Quality in Use model for Big Data

Cited by 146 publications

References 6 publications

Data Lakes: Trends and Perspectives

Data Lakes: Trends and Perspectives

Exploration Of The Timeliness Of ICD Codes In Administrative Databases: A Nationwide Study

Data Visualization in Support of Executive Decision Making

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