Results 261 to 270 of about 32,662,171 (307)

Data quality inference

Proceedings of the 2nd international workshop on Information quality in information systems, 2005
In the field of sensor networks, data integration and collaboration, and intelligence gathering efforts, information on the quality of data sources are important but are often not available. We describe a technique to rank data sources by observing and comparing their behavior (i.e., the data produced by data sources) to rank.
Raymond K. Pon, Alfonso F. Cardenas
openaire   +1 more source

Models of Data Quality

2018
The research proposes a new approach to data quality management presenting three groups of DSL (Domain Specific Language). The first language group uses concept of data object in order to describe data to be analysed, the second group describes the requirements on data quality, and the third group describes data quality management process. The proposed
Zane Bicevska, Janis Bicevskis, Ivo Oditis   +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
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Data standard ≠ data quality.

Studies in health technology and informatics, 2015
The relationship between data quality and data standards has not been clearly articulated. While some directly state that data standards increase data quality, others claim the opposite. Depending on the type of data standard and the aspects of data quality considered, both arguments may in fact be correct.
Meredith Nahm, W. Ed Hammond
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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
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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

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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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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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