research-article

Methodologies for data quality assessment and improvement

Authors:

Carlo Batini,

Cinzia Cappiello,

Chiara Francalanci,

Andrea MaurinoAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 41, Issue 3

Article No.: 16, Pages 1 - 52

https://doi.org/10.1145/1541880.1541883

Published: 30 July 2009 Publication History

Get Access

Abstract

The literature provides a wide range of techniques to assess and improve the quality of data. Due to the diversity and complexity of these techniques, research has recently focused on defining methodologies that help the selection, customization, and application of data quality assessment and improvement techniques. The goal of this article is to provide a systematic and comparative description of such methodologies. Methodologies are compared along several dimensions, including the methodological phases and steps, the strategies and techniques, the data quality dimensions, the types of data, and, finally, the types of information systems addressed by each methodology. The article concludes with a summary description of each methodology.

References

[1]

Abiteboul, S., Buneman, P., and Suciu, D. 2000. Data on the Web: From Relations to Semistructured Data and XML. Morgan Kaufmann Publishers.

Digital Library

Google Scholar

[2]

Aiken, P. 1996. Data Reverse Engineering. McGraw Hill.

Google Scholar

[3]

Arenas, M., Bertossi, L., and Chomicki, J. 1999. Consistent query answers in inconsistent databases. In Proceedings of the 18th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS). ACM, New York, 68--79.

Digital Library

Google Scholar

[4]

Atzeni, P. and Antonellis, V. D. 1993. Relational Database Theory. Benjamin/Cummings.

Digital Library

Google Scholar

[5]

Atzeni, P., Merialdo, P., and Sindoni, G. 2001. Web site evaluation: Methodology and case study. In Proceedings of International Workshop on data Semantics in Web Information Systems (DASWIS).

Digital Library

Google Scholar

[6]

Ballou, D. and Pazer, H. 1985. Modeling data and process quality in multi-input, multi-output information systems. Manag. Sci. 31, 2.

Digital Library

Google Scholar

[7]

Ballou, D., Wang, R., Pazer, H., and Tayi, G. 1998. Modeling information manufacturing systems to determine information product quality. Manage. Sci. 44, 4.

Digital Library

Google Scholar

[8]

Basile, A., Batini, C., Grega, S., Mastrella, M., and Maurino, A. 2007. Orme: A new methodology for information quality and basel II operational risk. In Proceeedings of the 12th International Conference of Information Quality, Industrial Track.

Google Scholar

[9]

Basili, V., Caldiera, C., Rombach, H. 1994. Goal question metric paradigm.

Google Scholar

[10]

Baskarada, S., Koronios, A., and Gao, J. 2006. Towards a capability maturity model for information quality management: a tdqm approach. In Proceedings of the 11th International Conference on Information Quality.

Google Scholar

[11]

Batini, C., Cabitza, F., Cappiello, C., and Francalanci, C. 2008. A comprehensive data quality methodology for Web and structured data. Int. J. Innov. Comput. Appl. 1, 3, 205--218.

Digital Library

Google Scholar

[12]

Batini, C. and Scannapieco, M. 2006. Data Quality: Concepts, Methodologies and Techniques. Springer Verlag.

Digital Library

Google Scholar

[13]

Bertolazzi, P., Santis, L. D., and Scannapieco, M. 2003. Automatic record matching in cooperative information systems. In Proceedings of the ICDT International Workshop on Data Quality in Cooperative Information Systems (DQCIS).

Google Scholar

[14]

Bettschen, P. 2005. Master data management (MDM) enables IQ at Tetra Pak. In Proceedings of the 10th International Conference on Information Quality.

Google Scholar

[15]

Bilke, A., Bleiholder, J., B&#246;hm, C., Draba, K., Naumann, F., and Weis, M. September 2005. Automatic data fusion with HumMer. In Proceedings of the VLDB Demonstration Program.

Digital Library

Google Scholar

[16]

Bovee, M., Srivastava, R., and Mak, B. September 2001. A conceptual framework and belief-function approach to assessing overall information quality. In Proceedings of the 6th International Conference on Information Quality.

Google Scholar

[17]

Buneman, P. 1997. Semi-structured data. In Proceedings of the 16th ACM Symposium on Principles of Database Systems (PODS).

Digital Library

Google Scholar

[18]

Cal&#236;, A., Calvanese, D., De Giacomo, G., and Lenzerini, M. 2004. Data integration under integrity constraints. Inform. Syst. 29, 2, 147--163.

Digital Library

Google Scholar

[19]

Calvanese, D., De Giacomo, D., and Lenzerini, M. 1999. Modeling and querying semi-structured data. Network. Inform. Syst. J. 2, 2, 253--273.

Google Scholar

[20]

Cappiello, C., Francalanci, C., and Pernici, B. 2003. Preserving Web sites: A data quality approach. In Proceedings of the 7th International Conference on Information Quality (ICIQ).

Google Scholar

[21]

Cappiello, C., Francalanci, C., Pernici, B., Plebani, P., and Scannapieco, M. 2003b. Data quality assurance in cooperative information systems: a multi-dimension certificate. In Proceedings of the ICDT International Workshop on Data Quality in Cooperative Information Systems (DQCIS).

Google Scholar

[22]

Catarci, T., and Scannapieco, M. 2002. Data quality under the computer science perspective. Archivi Computer 2.

Google Scholar

[23]

Chapman, A., Richards, H., and Hawken, S. 2006. Data and information quality at the Canadian institute for health information. In Proceedings of the 11th International Conference on Information Quality.

Google Scholar

[24]

Chengalur-Smith, I. N., Ballou, D. P., and Pazer, H. L. 1999. The impact of data quality information on decision making: An exploratory analysis. IEEE Trans. Knowl. Data Eng. 11, 6, 853--864.

Digital Library

Google Scholar

[25]

Corey, D., Cobler, L., Haynes, K., and Walker, R. 1996. Data quality assurance activities in the military health services system. In Proceedings of the 1st International Conference on Information Quality. 127--153.

Google Scholar

[26]

Dasu, T. and Johnson, T. 2003. Exploratory Data Mining and Data cleaning. Probability and Statistics series, John Wiley.

Digital Library

Google Scholar

[27]

Data Warehousing Institute. 2006. Data quality and the bottom line: Achieving business success through a commitment to high quality data. http://www.dw-institute.com/.

Google Scholar

[28]

De Amicis, F., Barone, D., and Batini, C. 2006. An analytical framework to analyze dependencies among data quality dimensions. In Proceedings of the 11th International Conference on Information Quality (ICIQ). 369--383.

Google Scholar

[29]

De Amicis, F. and Batini, C. 2004. A methodology for data quality assessment on financial data. Studies Commun. Sci. SCKM.

Google Scholar

[30]

De Michelis, G., Dubois, E., Jarke, M., Matthes, F., Mylopoulos, J., Papazoglou, M., Pohl, K., Schmidt, J., Woo, C., and Yu, E. 1997. Cooperative Information Systems: A Manifesto. In Cooperative Information Systems: Trends & Directions, M. Papazoglou and G. Schlageter, Eds. Academic-Press.

Google Scholar

[31]

De Santis, L., Scannapieco, M., and Catarci, T. 2003. Trusting data quality in cooperative information systems. In Proceedings of the 11th International Conference on Cooperative Information Systems (CoopIS). Catania, Italy.

Google Scholar

[32]

Dedeke, A. 2005. Building quality into the information supply chain. Advances in Management Information Systems-Information Quality Monograph (AMIS-IQ) Monograph. R. Wang, E. Pierce, S. Madnick, and Fisher C.W., Eds.

Google Scholar

[33]

DQI. 2004. Data quality initiative framework. Project report. www.wales.nhs.uk/sites/documents/319/DQI_Framwork_Update_Letter_160604.pdf

Google Scholar

[34]

English, L. 1999. Improving Data Warehouse and Business Information Quality. Wiley & Sons.

Digital Library

Google Scholar

[35]

English, L. 2002. Process management and information quality: how improving information production processes improved information (product) quality. In Proceedings of the 7th International Conference on Information Quality (ICIQ). 206--211.

Google Scholar

[36]

Eppler, M. and Helfert, M. 2004. A classification and analysis of data quality costs. In Proceedings of the 9th International Conference on Information Systems (ICIQ).

Google Scholar

[37]

Eppler, M. and M&#252;nzenmaier, P. 2002. Measuring information quality in the Web context: A survey of state-of-the-art instruments and an application methodology. In Proceedings of the 7th International Conference on Information Systems (ICIQ).

Google Scholar

[38]

Falorsi, P., Pallara, S., Pavone, A., Alessandroni, A., Massella, E., and Scannapieco, M. 2003. Improving the quality of toponymic data in the italian public administration. In Proceedings of the ICDT Workshop on Data Quality in Cooperative Information Systems (DQCIS).

Google Scholar

[39]

Fellegi, I. P., and Holt, D. 1976. A systematic approach to automatic edit and imputation. J. Amer. Stat. Assoc. 71, 353, 17--35.

Crossref

Google Scholar

[40]

Fisher, C. and Kingma, B. 2001. Criticality of data quality as exemplified in two disasters. Inform. Manage. 39, 109--116.

Digital Library

Google Scholar

[41]

Fraternali, P., Lanzi, P., Matera, M., and Maurino, A. 2004. Model-driven Web usage analysis for the evaluation of Web application quality. J. Web Eng. 3, 2, 124--152.

Digital Library

Google Scholar

[42]

Gackowski, Z. 2006. Redefining information quality: the operations management approach. In Proceedings of the 11th International Conference on Information Quality (ICIQ). 399--419.

Google Scholar

[43]

Hammer, M. 1990. Reengineering work: Don't automate, obliterate. Harvard Bus. Rev. 104--112.

Google Scholar

[44]

Hammer, M. and Champy, J. 2001. Reengineering the Corporation: A Manifesto for Business Revolution, Harper Collins.

Google Scholar

[45]

Hernandez, M. and Stolfo, S. 1998. Real-world data is dirty: Data cleansing and the merge/purge problem. J. Data Min. Knowl. Dis. 1, 2.

Digital Library

Google Scholar

[46]

Isakowitz, T., Bieber, M., and Vitali, F. 1998. Web information systems - introduction. Commun. ACM 41, 7, 78--80.

Digital Library

Google Scholar

[47]

Isakowitz, T., Stohr, E., and Balasubramanian, P. 1995. RMM: A methodology for structured hypermedia design. Comm. ACM 58, 8.

Digital Library

Google Scholar

[48]

Istat. 2004. Guidelines for the data quality improvement of localization data in public administration (in Italian). www.istat.it

Google Scholar

[49]

Jarke, M., Lenzerini, M., Vassiliou, Y., and Vassiliadis, P., Eds. 1995. Fundamentals of Data Warehouses. Springer Verlag.

Digital Library

Google Scholar

[50]

Jeusfeld, M., Quix, C., and Jarke, M. 1998. Design and analysis of quality information for data warehouses. In Proceedings of the 17th International Conference on Conceptual Modeling.

Digital Library

Google Scholar

[51]

Kerr, K. and Norris, T. 2004. The development of a healthcare data quality framework and strategy. In Proceedings of the 9th International Conference on Information Quality.

Google Scholar

[52]

Kettinger, W. and Grover, V. 1995. Special section: Toward a theory of business process change management. J. Manag. Inform. Syst. 12, 1, 9--30.

Digital Library

Google Scholar

[53]

Kovac, R. and Weickert, C. 2002. Starting with quality: Using TDQM in a start-up organization. In Proceedings of the 7th International Conference on Information Quality (ICIQ). Boston, 69--78.

Google Scholar

[54]

Lee, Y. W., Strong, D. M., Kahn, B. K., and Wang, R. Y. 2002. AIMQ: A methodology for information quality assessment. Inform. Manage. 40, 2, 133--460.

Digital Library

Google Scholar

[55]

Lenzerini, M. 2002. Data integration: A theoretical perspective. In Proceedings of the 21st ACM Symposium on Principles of Database Systems (PODS).

Digital Library

Google Scholar

[56]

Liu, L. and Chi, L. 2002. Evolutionary data quality. In Proceedings of the 7th International Conference on Information Quality.

Google Scholar

[57]

Long, J. and Seko, C. April 2005. A cyclic-hierarchical method for database data-quality evaluation and improvement. In Advances in Management Information Systems-Information Quality Monograph (AMIS-IQ) Monograph, R. Wang, E. Pierce, S. Madnick, and Fisher C.W.

Google Scholar

[58]

Loshin, D. 2004. Enterprise Knowledge Management - The Data Quality Approach. Series in Data Management Systems, Morgan Kaufmann, chapter 4.

Digital Library

Google Scholar

[59]

Lyman, P. and Varian, H. R. 2003. How much information. http://www.sims.berkeley.edu/how-much-info-2003.

Google Scholar

[60]

Mecca, G., Atzeni, P., Masci, M., Merialdo, P., and Sindoni, G. 1998. The Araneus Web-based management system. In Proceedings of the ACM SIGMOD International Conference on Management of Data, L. M. Haas and A. Tiwary, Eds. ACM Press, 544--546.

Digital Library

Google Scholar

[61]

Mecca, G., Merialdo, P., Atzeni, P., and Crescenzi, V. 1999. The (short) araeneus guide to Web site development. In Proceedings of the 2nd International Workshop on the Web and Databases (WebDB) Conjunction with Sigmod.

Google Scholar

[62]

Motro, A. and Anokhin, P. 2005. Fusionplex: Resolution of data inconsistencies in the data integration of heterogeneous information sources. Inform. Fusion, 7, 2, 176--196.

Digital Library

Google Scholar

[63]

Muthu, S., Withman, L., and Cheraghi, S. H. 1999. Business process re-engineering : a consolidated methodology. In Proceedings of the 4th annual International Conference on Industrial Engineering Theory, Applications and Practice.

Google Scholar

[64]

Nadkarni, P. 2006. Delivering data on time: The assurant health case. In Proceedings of the 11th International Conference on Information Quality.

Google Scholar

[65]

Naumann, F. 2002. Quality-driven query answering for integrated information systems. Lecture Notes in Computer Science, vol. 2261.

Digital Library

Google Scholar

[66]

Nelson, J., Poels, G., Genero, M., and Piattini, Eds. 2003. Proceedings of the 2nd International Workshop on Conceptual Modeling Quality (IWCMQ). Lecture Notes in Computer Science, vol. 2814, Springer.

Google Scholar

[67]

Oakland, J. 1989. Total Quality Management. Springer.

Google Scholar

[68]

Office of Management and Budget. 2006. Information quality guidelines for ensuring and maximizing the quality, objectivity, utility, and integrity of information disseminated by agencies. http://www.whitehouse.gov/omb/fedreg/reproducible.html.

Google Scholar

[69]

Pernici, B. and Scannapieco, M. 2003. Data quality in Web information systems. J. Data Semant. 1, 48--68.

Crossref

Google Scholar

[70]

Pipino, L., Lee, Y., and Wang, R. 2002. Data quality assessment. Commun. ACM 45, 4.

Digital Library

Google Scholar

[71]

Raghunathan, S. 1999. Impact of information quality and decision-maker quality on decision quality: a theoretical model and simulation analysis. Decis. Supp. Syst. 26, 275--286.

Digital Library

Google Scholar

[72]

Rahm, E., Thor, A., Aum&#252;ller, D., Hong-Hai, D., Golovin, N., and Kirsten, T. June 2005. iFuice information fusion utilizing instance correspondences and peer mappings. In Proceedings of the 8th International Workshop on the Web and Databases (WebDB). located with SIGMOD.

Google Scholar

[73]

Rao, R. 2003. From unstructured data to actionable intelligence. IT Professional 535, 6, 29--35.

Digital Library

Google Scholar

[74]

Redman, T. 1996. Data Quality for the Information Age. Artech House.

Digital Library

Google Scholar

[75]

Redman, T. 1998. The impact of poor data quality on the typical enteprise. Comm. ACM 41, 2, 79--82.

Digital Library

Google Scholar

[76]

Scannapieco, M., A.Virgillito, Marchetti, M., Mecella, M., and Baldoni, R. 2004. The DaQuinCIS architecture: a platform for exchanging and improving data quality in Cooperative Information Systems. Inform. Syst. 29, 7, 551--582.

Digital Library

Google Scholar

[77]

Scannapieco, M., Pernici, B., and Pierce, E. 2002. IP-UML: Towards a Methodology for Quality Improvement based on the IP-MAP Framework. In Proceedings of the 7th International Conference on Information Quality (ICIQ). Boston.

Google Scholar

[78]

Scannapieco, M., Pernici, B., and Pierce, E. 2005. IP-UML: A methodology for quality improvement-based on IP-MAP and UML. In Information Quality, Advances in Management Information Systems, Information Quality Monograph (AMIS-IQ), R. Wang, E. Pierce, S. Madnik, and C. Fisher, Eds.

Google Scholar

[79]

Sessions, V. 2007. Employing the TDQM methodology: An assessment of the SC SOR. In Proceedings of the 12th International Conference on Information Quality. 519--537.

Google Scholar

[80]

Shankaranarayan, G., Wang, R. Y., and Ziad, M. 2000. Modeling the manufacture of an information product with IP-MAP. In Proceedings of the 6th International Conference on Information Quality (ICIQ 2000). Boston.

Google Scholar

[81]

Shankaranarayanan, G. and Wang, R. 2007. IPMAP: Current state and perspectives. In Proceedings of the 12th International Conference on Information Quality.

Google Scholar

[82]

Sheng, Y. 2003. Exploring the mediating and moderating effects of information quality on firm's endeavour on information systems. In Proceedings of the 8th International Conference on Information Quality 2003 (ICIQ). 344--352.

Google Scholar

[83]

Sheng, Y. and Mykytyn, P. 2002. Information technology investment and firm performance: A perspective of data quality. In Proceedings of the 7th International Conference on Information Quality (ICIQ). DC, 132--141.

Google Scholar

[84]

Stoica, M., Chawat, N., and Shin, N. 2003. An investigation of the methodologies of business process reengineering. In Proceedings of Information Systems Education Conference.

Google Scholar

[85]

Su, Y. and Jin, Z. 2004. A methodology for information quality assessment in the designing and manufacturing processes of mechanical products. In Proceedings of the 9th International Conference on Information Quality (ICIQ). 447--465.

Google Scholar

[86]

US Department of Defense. 1994. Data administration procedures. DoD rep. 8320.1-M.

Google Scholar

[87]

Vassiliadis, P., Vagena, Z., Skiadopoulos, S., Karayannidis, N., and Sellis, T. 2001. ARTKOS: toward the modeling, design, control and execution of ETL processes. Inform. Syst. 26, 537--561.

Digital Library

Google Scholar

[88]

Vermeer, B. 2000. How important is data quality for evaluating the impact of edi on global supply chains. In Proceedings of the 33rd Haway Conference on Systems Sciences.

Digital Library

Google Scholar

[89]

Wand, Y. and Wang, R. 1996. Anchoring data quality dimensions in ontological foundations. Comm. ACM 39, 11.

Digital Library

Google Scholar

[90]

Wang, R. 1998. A product perspective on total data quality management. Comm. ACM 41, 2.

Digital Library

Google Scholar

[91]

Wang, R. and Strong, D. 1996. Beyond accuracy: What data quality means to data consumers. J. Manage. Inform. Syst. 12, 4.

Digital Library

Google Scholar

[92]

World Wide Web Consortium. www.w3.org/WAI/. Web accessibility initiative.

Google Scholar

[93]

Zachman, J. 2006. Zachman institute for framework advancement (ZIFA). www.zifa.com.

Google Scholar

Cited By

View all

Hussain MZhang T(2025)Machine learning-based outlier detection for pipeline in-line inspection dataReliability Engineering & System Safety10.1016/j.ress.2024.110553254(110553)Online publication date: Feb-2025
https://doi.org/10.1016/j.ress.2024.110553
Sun RAbouarghoub WDemir E(2025)Enhancing data quality in maritime transportation: A practical method for imputing missing ship static dataOcean Engineering10.1016/j.oceaneng.2024.119722315(119722)Online publication date: Jan-2025
https://doi.org/10.1016/j.oceaneng.2024.119722
Alanazi MMahmood AChowdhury M(2025)ICS-LTU2022: A dataset for ICS vulnerabilitiesComputers & Security10.1016/j.cose.2024.104143148(104143)Online publication date: Jan-2025
https://doi.org/10.1016/j.cose.2024.104143
Show More Cited By

Index Terms

Methodologies for data quality assessment and improvement
1. General and reference
  1. Document types
    1. Surveys and overviews
2. Information systems
  1. Data management systems

Recommendations

Data Quality Assessment and Improvement

As the Vrije Universiteit Brussel switched from an in-house built CRIS to Pure, a large number of data quality issues were discovered. In order to solve these, a large-scale data quality assessment and improvement program was started. The assessment ...
Data quality assessment: The Hybrid Approach

Various techniques have been proposed to enable organisations to assess the current quality level of their data. Unfortunately, organisations have many different requirements related to data quality (DQ) assessment. For example, some organisations may ...
Requirements for Data Quality Metrics
Challenge Paper, Experience Paper and Research Paper

Data quality and especially the assessment of data quality have been intensively discussed in research and practice alike. To support an economically oriented management of data quality and decision making under uncertainty, it is essential to assess ...

Reviews

Reviewer: Andreas E. Schwald

Data quality comprises a wide subject area, with a variety of dissimilar issues. It is far from trivial to compile a comprehensive survey of the field. This treatise on data quality assessment and improvement presents 13 methodologies, over 50 pages, and lists 92 references up to the year 2007. It aims to provide a "systematic and comparative description along several dimensions, including phases and steps, strategies and techniques, data quality dimensions, types of data, and types of information systems." The paper may be unsatisfactory and too shallow for an advocate of a particular methodology. However, it can be quite helpful for a quick overview, especially for those who are looking for improvement, implementation advice, or new ideas. It covers a wide field and stimulates the interest of the reader?although, in most cases, a reference is needed to obtain an answer to a specific question or for an in-depth treatment of a topic. This is a noteworthy effort that sums up a great deal of information from a rather heterogeneous field. It covers several publications that might not be available in a library of modest size, thereby bringing this information to the attention of a wider reader community. However, whether quality is in the eyes of an observer or in measurable attributes of an object remains an open question. Online Computing Reviews Service

Access critical reviews of Computing literature here

Become a reviewer for Computing Reviews.

Comments

Information & Contributors

Information

Published In

ACM Computing Surveys Volume 41, Issue 3

July 2009

284 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/1541880

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 July 2009

Accepted: 01 May 2008

Revised: 01 December 2007

Received: 01 December 2006

Published in CSUR Volume 41, Issue 3

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

European IST

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

762
Total Citations
View Citations
20,742
Total Downloads

Downloads (Last 12 months)2,313
Downloads (Last 6 weeks)252

Reflects downloads up to 25 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Hussain MZhang T(2025)Machine learning-based outlier detection for pipeline in-line inspection dataReliability Engineering & System Safety10.1016/j.ress.2024.110553254(110553)Online publication date: Feb-2025
https://doi.org/10.1016/j.ress.2024.110553
Sun RAbouarghoub WDemir E(2025)Enhancing data quality in maritime transportation: A practical method for imputing missing ship static dataOcean Engineering10.1016/j.oceaneng.2024.119722315(119722)Online publication date: Jan-2025
https://doi.org/10.1016/j.oceaneng.2024.119722
Alanazi MMahmood AChowdhury M(2025)ICS-LTU2022: A dataset for ICS vulnerabilitiesComputers & Security10.1016/j.cose.2024.104143148(104143)Online publication date: Jan-2025
https://doi.org/10.1016/j.cose.2024.104143
Kevin N. Shah Sandip J. Gami Abhishek Trehan (2024)An Intelligent Approach to Data Quality Management AI-Powered Quality Monitoring in AnalyticsInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJARSCT-22820(109-119)Online publication date: 21-Dec-2024
https://doi.org/10.48175/IJARSCT-22820
Huang-fu HZhang NWang LLiang HXian BGan XLai Y(2024)Geographical accessibility to healthcare by point-of–interest data from online maps: a comparative studyGeospatial Health10.4081/gh.2024.132219:2Online publication date: 20-Dec-2024
https://doi.org/10.4081/gh.2024.1322
Pal K(2024)Software Engineering Approach for Designing Apparel Business Data AnalyticsData-Driven Business Intelligence Systems for Socio-Technical Organizations10.4018/979-8-3693-1210-0.ch006(128-151)Online publication date: 23-Feb-2024
https://doi.org/10.4018/979-8-3693-1210-0.ch006
Frick NTerwolbeck JSeibel BMetternich J(2024)Design Model for the Digital Shadow of a Value StreamSystems10.3390/systems1201002012:1(20)Online publication date: 9-Jan-2024
https://doi.org/10.3390/systems12010020
Bruni RDaraio CDi Leo S(2024)Flexible Techniques to Detect Typical Hidden Errors in Large Longitudinal DatasetsSymmetry10.3390/sym1605052916:5(529)Online publication date: 28-Apr-2024
https://doi.org/10.3390/sym16050529
Britton TBaslik PSchmid LHeinz B(2024)Enhancing Understanding through Data Visualization: What Can Available Data Reveal about Access to Energy in Displacement Contexts on the African Continent?Sustainability10.3390/su1611465316:11(4653)Online publication date: 30-May-2024
https://doi.org/10.3390/su16114653
Šikić FKalafatić ZSubašić MLončarić S(2024)Enhanced Out-of-Stock Detection in Retail Shelf Images Based on Deep LearningSensors10.3390/s2402069324:2(693)Online publication date: 22-Jan-2024
https://doi.org/10.3390/s24020693
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Data Quality Assessment and Improvement

Data quality assessment: The Hybrid Approach

Requirements for Data Quality Metrics

Reviews

Access critical reviews of Computing literature here

Comments

Published In

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Funding Sources

Other Metrics

Article Metrics

Other Metrics

Cited By

Login options

Full Access

PDF

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Data Quality Assessment and Improvement

Data quality assessment: The Hybrid Approach

Requirements for Data Quality Metrics

Reviews

Access critical reviews of Computing literature here

Comments

Information

Published In

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Funding Sources

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations