Using a Metadata Framework to Improve Data Resources Quality

2002 ◽  
pp. 20-34
Author(s):  
Tor Guimaraes ◽  
Youngohc Yoon ◽  
Peter Aiken
Keyword(s):  
Life Cycle ◽  
Data Quality ◽  
Data Representation ◽  
Quality Data ◽  
Extended Model ◽  
Quality Engineering ◽  
Model Quality ◽  
Specific Data ◽  
Utilization Data ◽  
Life Cycle Phase

The importance of properly managing the quality of organizational data resources is widely recognized. A metadata framework is presented as the critical tool in addressing the necessary requirements to ensure data quality. This is particularly useful in increasingly encountered complex situations where data usage crosses system boundaries. The basic concept of metadata quality as a foundation for data quality engineering is discussed, as well as an extended data life cycle model consisting of eight phases: metadata creation, metadata structuring, metadata refinement, data creation, data utilization, data assessment, data refinement, and data manipulation. This extended model will enable further development of life cycle phase-specific data quality engineering methods. The paper also expands the concept of applicable data quality dimensions, presenting data quality as a function of four distinct components: data value quality, data representation quality, data model quality, and data architecture quality. Each of these, in turn, is described in terms of specific data quality attributes.

Applying a Metadata Framework to Improve Data Quality

2005 ◽  
pp. 146-151
Author(s):  
Victoria Youngohc Yoon ◽  
Peter Aiken ◽  
Tor Guimaraes
Keyword(s):  
Life Cycle ◽  
Data Quality ◽  
Quality Engineering ◽  
Life Cycle Model ◽  
Cycle Model ◽  
Imperfect Data ◽  
Information Resources Management ◽  
Business Operations ◽  
Data Life Cycle ◽  
A Company

The importance of a company-wide framework for managing data resources has been recognized (Gunter, 2001; Lee, 2003, 2004; Madnick, Wang & Xian, 2003, 2004; Sawhney, 2001; Shankaranarayan, Ziad & Wang, 2003). It is considered a major component of information resources management (Guimaraes, 1988). Many organizations are discovering that imperfect data in information systems negatively affect their business operations and can be extremely costly (Brown, 2001; Keizer, 2004). The expanded data life cycle model proposed here enables us to identify links between cycle phases and data quality engineering dimensions. Expanding the data life cycle model and the dimensions of data quality will enable organizations to more effectively implement the inter- as well as intra-system use of their data resources, as well as better coordinate the development and application of their data quality engineering methods.

Designing Information Product (IP) Maps On the Process of Data Processing and Academic Information

2017 ◽  
Vol 4 (1) ◽  
pp. 25-31 ◽  
Author(s):  
Diana Effendi
Keyword(s):  
Data Quality ◽  
Data Management ◽  
Information Management ◽  
Information Quality ◽  
Quality Data ◽  
Management Approach ◽  
Quality Of Data ◽  
Information Product ◽  
Academic Activities

Information Product Approach (IP Approach) is an information management approach. It can be used to manage product information and data quality analysis. IP-Map can be used by organizations to facilitate the management of knowledge in collecting, storing, maintaining, and using the data in an organized. The  process of data management of academic activities in X University has not yet used the IP approach. X University has not given attention to the management of information quality of its. During this time X University just concern to system applications used to support the automation of data management in the process of academic activities. IP-Map that made in this paper can be used as a basis for analyzing the quality of data and information. By the IP-MAP, X University is expected to know which parts of the process that need improvement in the quality of data and information management.   Index term: IP Approach, IP-Map, information quality, data quality. REFERENCES[1] H. Zhu, S. Madnick, Y. Lee, and R. Wang, “Data and Information Quality Research: Its Evolution and Future,” Working Paper, MIT, USA, 2012.[2] Lee, Yang W; at al, Journey To Data Quality, MIT Press: Cambridge, 2006.[3] L. Al-Hakim, Information Quality Management: Theory and Applications. Idea Group Inc (IGI), 2007.[4] “Access : A semiotic information quality framework: development and comparative analysis : Journal ofInformation Technology.” [Online]. Available: http://www.palgravejournals.com/jit/journal/v20/n2/full/2000038a.html. [Accessed: 18-Sep-2015].[5] Effendi, Diana, Pengukuran Dan Perbaikan Kualitas Data Dan Informasi Di Perguruan Tinggi MenggunakanCALDEA Dan EVAMECAL (Studi Kasus X University), Proceeding Seminar Nasional RESASTEK, 2012, pp.TIG.1-TI-G.6.

Data Quality Engineering Handbook.

1994 ◽  
Author(s):  
DEFENSE LOGISTICS AGENCY ALEXANDRIA VA
Keyword(s):  
Data Quality ◽  
Quality Engineering

Design of Marine Data Warehouse ETL System

2014 ◽  
Vol 668-669 ◽  
pp. 1374-1377 ◽  
Author(s):  
Wei Jun Wen
Keyword(s):  
Decision Making ◽  
Data Quality ◽  
Data Warehouse ◽  
Environmental Data ◽  
Data Sources ◽  
Quality Data ◽  
Critical Step ◽  
Marine Data ◽  
Data Extracting ◽  
Marine Environmental

ETL refers to the process of data extracting, transformation and loading and is deemed as a critical step in ensuring the quality, data specification and standardization of marine environmental data. Marine data, due to their complication, field diversity and huge volume, still remain decentralized, polyphyletic and isomerous with different semantics and hence far from being able to provide effective data sources for decision making. ETL enables the construction of marine environmental data warehouse in the form of cleaning, transformation, integration, loading and periodic updating of basic marine data warehouse. The paper presents a research on rules for cleaning, transformation and integration of marine data, based on which original ETL system of marine environmental data warehouse is so designed and developed. The system further guarantees data quality and correctness in analysis and decision-making based on marine environmental data in the future.

Comparative Life Cycle Assessment of Autoclaved Concrete Blocks and Fired Blocks in China

2018 ◽  
Vol 913 ◽  
pp. 1018-1026
Author(s):  
Yan Qiong Sun ◽  
Yu Liu ◽  
Su Ping Cui
Keyword(s):  
Sensitivity Analysis ◽  
Life Cycle Assessment ◽  
Life Cycle ◽  
Energy Consumption ◽  
Data Quality ◽  
Human Health ◽  
Functional Unit ◽  
Cement Production ◽  
Terrestrial Ecotoxicity ◽  
Concrete Blocks

In this paper, a variety of blocks were grouped into the autoclaved blocks and fired blocks as far as the productive technology is concerned. In order to compare the life cycle impacts of the two kinds of the blocks, a life cycle assessment of two products on the functional unit 1m3 was carried out through the exploitation of mineral stage, transportation stage and the production of the blocks stage on the considering of the resource and energy consumption and the pollutant discharges. The results demonstrated that the fired blocks appeared to have less impact than autoclaved concrete blocks on human health, marine ecotoxicity toxicity and terrestrial ecotoxicity toxicity nearly 30%. The raw coal led to the serious impacts on the fossil depletion through the cement production stage of the autoclaved concrete blocks accounting for 45.86% and the gangue exploitation stage of the fired blocks accounting for 42.5%. Assessment of the data quality that the data was of pretty high or within the permission. The sensitivity analysis and contribution analysis assessment showed that the conclusion were robust.

The Quality Data Model of Product Lifecycle and Base Application in Electronic Components Industry

2014 ◽  
Vol 635-637 ◽  
pp. 1948-1951
Author(s):  
Yao Guang Hu ◽  
Dong Feng Wu ◽  
Jing Qian Wen
Keyword(s):  
Life Cycle ◽  
Data Analysis ◽  
Data Model ◽  
Product Life Cycle ◽  
Business Processes ◽  
Quality Data ◽  
Electronic Components ◽  
Product Lifecycle ◽  
Related Data ◽  
Base Application

On the basis of the electronic components business processes and the analysis of the quality data related, a model based on the object entity of the product life cycle is proposed. Object entity as the carrier of the related data this model mergers and reorganizes the related business, meanwhile links the entity through the revolved information of the quality data model thus achieving the integrity of the business in both time and space. This data model as the basis, can effectively realize the integration and sharing of quality data, facilitates the quality data analysis and quality traceability, and improve the capabilities of quality data management for the enterprise.

scholarly journals A New Remote Guided Method for Supervised Web-Based Cognitive Testing to Ensure High Quality Data

2021 ◽  
Author(s):  
Victoria Leong ◽  
Kausar Raheel ◽  
Sim Jia Yi ◽  
Kriti Kacker ◽  
Vasilis M. Karlaftis ◽  
...  
Keyword(s):  
Data Quality ◽  
Response Times ◽  
Psychological Research ◽  
Cognitive Testing ◽  
Quality Data ◽  
High Quality ◽  
Online Data ◽  
Test Environment ◽  
Web Based ◽  
Cognitive Data

Background. The global COVID-19 pandemic has triggered a fundamental reexamination of how human psychological research can be conducted both safely and robustly in a new era of digital working and physical distancing. Online web-based testing has risen to the fore as a promising solution for rapid mass collection of cognitive data without requiring human contact. However, a long-standing debate exists over the data quality and validity of web-based studies. Here, we examine the opportunities and challenges afforded by the societal shift toward web-based testing, highlight an urgent need to establish a standard data quality assurance framework for online studies, and develop and validate a new supervised online testing methodology, remote guided testing (RGT). Methods. A total of 85 healthy young adults were tested on 10 cognitive tasks assessing executive functioning (flexibility, memory and inhibition) and learning. Tasks were administered either face-to-face in the laboratory (N=41) or online using remote guided testing (N=44), delivered using identical web-based platforms (CANTAB, Inquisit and i-ABC). Data quality was assessed using detailed trial-level measures (missed trials, outlying and excluded responses, response times), as well as overall task performance measures. Results. The results indicated that, across all measures of data quality and performance, RGT data was statistically-equivalent to data collected in person in the lab. Moreover, RGT participants out-performed the lab group on measured verbal intelligence, which could reflect test environment differences, including possible effects of mask-wearing on communication. Conclusions. These data suggest that the RGT methodology could help to ameliorate concerns regarding online data quality and - particularly for studies involving high-risk or rare cohorts - offer an alternative for collecting high-quality human cognitive data without requiring in-person physical attendance.

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