Results 271 to 280 of about 5,645,319 (318)

Big Data Quality: A Data Quality Profiling Model

2019
Big Data is becoming a standard data model, and it is gaining wide adoption in the digital universe. Estimating the Quality of Big Data is recognized to be essential for data management and data governance. To ensure a fast and efficient data quality assessment represented by its dimensions, we need to extend the data profiling model to incorporate ...
Ikbal Taleb, Mohamed Adel Serhani, Rachida Dssouli   +2 more
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Evaluating data quality for blended data using a data quality framework

Statistical Journal of the IAOS: Journal of the International Association for Official Statistics
In 2020 the U.S. Federal Committee on Statistical Methodology (FCSM) released “A Framework for Data Quality”, organized by 11 dimensions of data quality grouped among three domains of quality (utility, objectivity, integrity). This paper addresses the use of the FCSM Framework for data quality assessments of blended data.
Jennifer D, Parker, Lisa B, Mirel, Phillip, Lee, Ryan, Mintz, Andrew, Tungate, Ambarish, Vaidyanathan   +5 more
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Data quality and data cleaning

Proceedings of the 2003 ACM SIGMOD international conference on Management of data, 2003
Data quality is a serious concern in any data-driven enterprise, often creating misleading findings during data mining, and causing process disruptions in operational databases. The manifestations of data quality problems can be very expensive- "losing" customers, "misplacing" billions of dollars worth of equipment, misallocated resources due to ...
Theodore Johnson, Tamraparni Dasu
openaire   +1 more source

Data quality for telecommunications

IEEE Journal on Selected Areas in Communications, 1994
The importance of data in large databases to the operation of telecommunications networks has grown considerably. For example, all provisioning, maintenance, and billing operations are critically dependent on data and many new network services are based on real-time access to data. This makes data quality a major issue for the industry.
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Data quality assessment

Communications of the ACM, 2002
How good is a company's data quality? Answering this question requires usable data quality metrics. Currently, most data quality measures are developed on an ad hoc basis to solve specific problems [6, 8], and fundamental principles necessary for developing usable metrics in practice are lacking.
Leo Pipino, Yang W. Lee, Richard Y. Wang
openaire   +1 more source

Truthful Data Quality Elicitation for Quality-Aware Data Crowdsourcing

IEEE Transactions on Control of Network Systems, 2020
Data crowdsourcing has found a broad range of applications (e.g., environmental monitoring and image classification) by leveraging the “wisdom” of a potentially large crowd of “workers” (e.g., mobile users). A key metric of crowdsourcing is data accuracy, which relies on the quality of the participating workers’ data (e.g., the probability that the ...
Xiaowen Gong, Ness B. Shroff
openaire   +1 more source

The data quality paradox

Network Security, 2015
After its people, data is arguably an organisation's most valuable asset. According to recent figures by the Networked Systems and Services department at SINTEF, some 90% of all the data in the world has been created in the past two years. That shouldn't be too much of a surprise to us given the data-driven world we now live in; but what is promising ...
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(Open) Data Quality

2012 IEEE 36th Annual Computer Software and Applications Conference, 2012
Be it environmental data, public utility data, geo data, or some other data the open data approach is a way to open up the most important resource of the 21st century information and further develop our knowledge-based society. Innovation happens when data is open, available online, structured, and readable by man and machine.
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Structured data quality reports to improve EHR data quality

International Journal of Medical Informatics, 2015
To examine whether a structured data quality report (SDQR) and feedback sessions with practice principals and managers improve the quality of routinely collected data in EHRs.The intervention was conducted in four general practices participating in the Fairfield neighborhood electronic Practice Based Research Network (ePBRN).
Jane Taggart, Siaw-Teng Liaw, Hairong Yu
openaire   +2 more sources

Quality and quantity of data

Drug News & Perspectives, 1998
When collecting data, deciding what type and how much to obtain will depend in large measure on how the data will be used and the group(s) that will be given the data, as well as the risk-taking or risk-averse position adopted. Among the most important issues involving the quality of data are knowing what plans and activities will lead to obtaining ...
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