Abstract
Evaluating the quality of data is a problem of a multi-dimensional nature and quite frequently depends on the perspective of an expected use or final purpose of the data. Numerous works have explored the well-known specification of data quality dimensions in various application domains, without addressing the inter-dependencies and aggregation of quality attributes for decision support. In this work we therefore propose a context-dependent formal process to evaluate the quality of data which integrates a preference model from the field of Multi-Criteria Decision Aiding. The parameters of this preference model are determined through interviews with work-domain experts. We show the interest of the proposal on a case study related to the evaluation of the quality of hydrographical survey data.
- [1] . 2016. Metadata-based data quality assessment. VINE Journal of Information and Knowledge Management Systems (2016).Google ScholarDigital Library
- [2] . 2018. Data quality assessment using multi-attribute maintenance perspective. International Journal of Information and Decision Sciences 10, 2 (2018), 147–161.Google ScholarCross Ref
- [3] . 2009. Methodologies for data quality assessment and improvement. ACM Computing Surveys (CSUR) 41, 3 (2009), 1–52.Google ScholarDigital Library
- [4] . 2007. An axiomatic approach to noncompensatory sorting methods in MCDM, I: The case of two categories. European Journal of Operational Research 178, 1 (
April 2007), 217–245.Google ScholarCross Ref - [5] . 2007. An axiomatic approach to noncompensatory sorting methods in MCDM, II: More than two categories. European Journal of Operational Research 178, 1 (
April 2007).Google Scholar - [6] . 2006. Evaluation and Decision Models with Multiple Criteria: Stepping Stones for the Analyst (1st ed.). Springer, Boston.Google Scholar
- [7] . 2018. A multi-criteria decision analysis method for regulatory evaluation of electricity distribution service quality. Utilities Policy 53 (2018), 38–48.Google ScholarCross Ref
- [8] . 2021. Data quality certification using ISO/IEC 25012: Industrial experiences. Journal of Systems and Software 176 (2021), 110938.Google ScholarCross Ref
- [9] . 2000. IHO Transfer Standard for Digital Hydrographic Data.
Technical Report . IHO.Special Publication N°57, 3 edition .Google Scholar - [10] . 2020. IHO Standards for Hydrographic Surveys.
Technical Report . IHO.Special Publication N°44, 6 edition .Google Scholar - [11] . 1976. Decisions with Multiple Objectives: Preferences and Value Tradeoffs. J. Wiley, New York.Google Scholar
- [12] . 2009. Representing data quality in sensor data streaming environments. Journal of Data and Information Quality (JDIQ) 1, 2 (2009), 1–28.Google ScholarDigital Library
- [13] C. Lacagnina,G. Peng, I. Ivanova, R. Downs, H. Ramapriyan, D. Moroni, Y. Wei, L. Wyborn, D. Jones, and A. Ganske. 2021. International community guidelines for sharing and reusing quality information of individual earth science datasets. Earth and Space Science Open Archive (2021). Google ScholarCross Ref
- [14] . 2021. Automatic data quality assessment of hydrographic surveys taking into account experts’ preferences. In OCEANS 2021: San Diego – Porto. 1–10. Google ScholarCross Ref
- [15] . 2011. Learning the parameters of a multiple criteria sorting method. In Algorithmic Decision Theory, , , and (Eds.), Vol. 6992. Springer, 219–233.Google Scholar
- [16] . 2021. Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information (TM). Academic Press.Google Scholar
- [17] . 2001. Using assignment examples to infer weights for ELECTRE TRI method: Some experimental results. European Journal of Operational Research 130, 2 (
April 2001), 263–275.Google ScholarCross Ref - [18] . 1998. Inferring an ELECTRE TRI model from assignment examples. Journal of Global Optimization 12, 2 (1998), 157–174.Google ScholarDigital Library
- [19] . 2002. Using assignment examples to infer category limits for the ELECTRE TRI method. JMCDA 11, 1 (
Nov. 2002), 29–43.Google Scholar - [20] . 2014. Inferring the parameters of a majority rule sorting model with vetoes on large datasets. In DA2PL 2014: From Multiple Criteria Decision Aid to Preference Learning. Ecole Centrale Paris and Université de Mons, 87–94.Google Scholar
- [21] . 2019. Data and information quality in remote sensing. In Information Quality in Information Fusion and Decision Making. Springer, 401–421.Google ScholarCross Ref
- [22] . 2017. Ensuring and improving information quality for earth science data and products. D.-Lib Magazine 23 (2017).Google ScholarCross Ref
- [23] . 2018. Location information quality: A review. Sensors 18, 11 (2018), 3999.Google ScholarCross Ref
- [24] . 1996. Multicriteria Methodology for Decision Aiding. Kluwer Academic, Dordrecht.Google ScholarCross Ref
- [25] . 2015. Data quality challenges in cyber-physical systems. Journal of Data and Information Quality (JDIQ) 6, 2-3 (2015), 1–4.Google ScholarDigital Library
- [26] . 2022. Lot bathymétrique S201500100-1. https://services.data.shom.fr/geonetwork/srv/api/records/LOTS_BATHY_S201500100-1.
Accessed: 2022-09-27 .Google ScholarCross Ref - [27] . 2013. Learning a majority rule model from large sets of assignment examples. In Algorithmic Decision Theory. Springer Berlin, 336–350.Google Scholar
- [28] . 2017. A population-based algorithm for learning a majority rule sorting model with coalitional veto. In Evolutionary Multi-Criterion Optimization, , , , , , , and (Eds.). Springer International Publishing, Cham, 575–589.Google Scholar
- [29] . 2015. A methodology to evaluate important dimensions of information quality in systems. Journal of Data and Information Quality (JDIQ) 6, 2-3 (2015), 1–23.Google ScholarDigital Library
- [30] . 2014. Adapting multi-criteria decision analysis for assessing the quality of software products. Current approaches and future perspectives. Advances in Computers 93 (2014), 153–226.Google ScholarCross Ref
- [31] . 2020. Anatomy of metadata for data curation. Journal of Data and Information Quality (JDIQ) 12, 3 (2020), 1–30.Google ScholarDigital Library
- [32] . 2014. A framework for data quality aware query systems. Information Systems 46 (2014), 24–44.Google ScholarDigital Library
Index Terms
- Data Quality Assessment through a Preference Model
Recommendations
Context-aware Big Data Quality Assessment: A Scoping Review
The term data quality refers to measuring the fitness of data regarding the intended usage. Poor data quality leads to inadequate, inconsistent, and erroneous decisions that could escalate the computational cost, cause a decline in profits, and cause ...
Methodologies for data quality assessment and improvement
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 ...
A Model for Data Quality Assessment
OTM '08: Proceedings of the OTM Confederated International Workshops and Posters on On the Move to Meaningful Internet Systems: 2008 Workshops: ADI, AWeSoMe, COMBEK, EI2N, IWSSA, MONET, OnToContent + QSI, ORM, PerSys, RDDS, SEMELS, and SWWSOne of the major causes for the failure of information systems to deliver can be attributed to data quality. Gartner's figures and other similar studies show the failure rate hovering at a plateau of 50% for data warehouses since 2004. While the true ...
Comments