Ontological Multidimensional Data Models and Contextual Data Quality

Bertossi, Leopoldo; Milani, Mostafa

doi:10.1145/3148239

Cited by 18 publications

(32 citation statements)

References 81 publications

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“…Data quality is a complex construct composed of multiple dimensions [35]- [39]. Although previous scholars agree that there is no definite definition for data quality, however, it was acknowledged that data quality must meet user requirements for specific usage context or fitness for use [40]- [42].…”

Section: A Data Qualitymentioning

confidence: 99%

Factors Influencing Master Data Quality: A Systematic Review

Ibrahim¹,

Mohamed²,

Safie³

2021

IJACSA

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Master data refers to the data that represents the core business of the organization, shared among different applications, departments, and organizations and most valued as the important asset to the organization. Despite the outward benefit of master data mainly in decision making and organization performance, the quality of master data is at risk. This is due to the critical challenges in managing master data quality the organization may expose. Hence the primary aim of this study is to identify factors influencing master data quality from the lens of total quality management while adopting the systematic literature review method. The study proposed 19 factors that inhibit the quality of master data namely data governance, information system, data quality policy and standard, data quality assessment, integration, continuous improvement, teamwork, data quality vision and strategy, understanding of the systems and data quality, data architecture management, personnel competency, top management support, business driver, legislation, information security management, training, change management, customer focus, and data supplier management that can be categorized to five components which are organizational, managerial, stakeholder, technological, and external. Another important finding is the identification of the differences for factors influencing master data compared to other data domain which are business driver, organizational structure, organizational culture, performance evaluation and rewards, evaluate cost/benefit tradeoffs, physical environment, risk management, storage management, usage of data, internal control, input control, staff participation, middle management's commitment, the role of data quality and data quality manager, audit, and personnel relation. It is expected that the findings of this study will contribute to a deeper understanding of the factors that will lead to an improved master data quality.

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Section: A Data Qualitymentioning

confidence: 99%

Factors Influencing Master Data Quality: A Systematic Review

Ibrahim¹,

Mohamed²,

Safie³

2021

IJACSA

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“…For example, if the weight list is like: [1,2,3,5,10] and sum of these are (1 + 2 + 3 + 5 + 10) = 21. Given two chromosomes, say [2,3,5] and [1,2,3], deleting them both satisfies the constraint, then their respective fitness score is: (2 + 3 + 5) = 10 and (1 + 2 + 3) = 6. Any candidates which do not fall into this range, are assigned a score larger than 21 so that it is discarded.…”

Section: Genetic Algorithms Stepsmentioning

confidence: 99%

“…To get minimal weights we can take the common atoms among the two parent chromosomes. For example, if the two feasible parent chromosomes are: [1,2,3,4] and [2,3,5,7,8], then there is a possibility that the multi-set with the common items [2,3], is the minimal chromosome which has better fitness (2+3) = 5. So, we randomly choose two parents from the max-priority queue and produce a new chromosome by selecting the common predicates between them.…”

Section: Genetic Algorithms Stepsmentioning

confidence: 99%

“…A database can be expressed as a logical theory, a context for it can be another logical theory and there can be a logical mapping between them to embed the database into the contextual theory or ontology. Contextual ontologies can be realized as multidimensional (MD) ontologies, due to the multidimensional nature of contexts [1,2]. These MD ontologies allow representation of dimensions as shown in the Figure : 1 the "Person" dimensional schema, which is similar to the multidimensional databases along with data tables under quality assessment.…”

Section: Introductionmentioning

confidence: 99%

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A Data Science Framework for Data Quality Assessment and Inconsistency Detection

Ramasamy¹,

Sisay²,

Bahiru³

2021

IJACSA

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The accurate analysis of data requires high-quality data. However, inconsistencies occur frequently in the actual data and lead to untrustworthy decisions in the downstream data analysis pipeline. In this research, we examine the problem of the detection of incoherence and the repair of the OMD data model (OMD). We propose a framework for data quality evaluation and an OMD repair framework. We formally define a weight-based semantile repair by deletion and have an automated weight generation system that takes into account multiple input criteria. We use multi-criteria decisions based on the correlation, contrast and conflict between multiple criteria that are often necessary in the field of data cleaning. After weight generation, we present a Min-Sum dynamic programming algorithm to find the minimum weight solution. Then we apply evolutionary optimisation techniques and use medical datasets to show improved performance that is practically feasible.

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“…The quality of data should be analyzed in the application or business context impartially [17,18]. Normally the data quality is assessed with some business rules that can be applied to the data.…”

Section: Introductionmentioning

confidence: 99%

Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application

Jarwar

Chong²

2020

Applied Sciences

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Due to the convergence of advanced technologies such as the Internet of Things, Artificial Intelligence, and Big Data, a healthcare platform accumulates data in a huge quantity from several heterogeneous sources. The adequate usage of this data may increase the impact of and improve the healthcare service quality; however, the quality of the data may be questionable. Assessing the quality of the data for the task in hand may reduce the associated risks, and increase the confidence of the data usability. To overcome the aforementioned challenges, this paper presents the web objects based contextual data quality assessment model with enhanced classification metric parameters. A semantic ontology of virtual objects, composite virtual objects, and services is also proposed for the parameterization of contextual data quality assessment of web objects data. The novelty of this article is the provision of contextual data quality assessment mechanisms at the data acquisition, assessment, and service level for the web objects enabled semantic data applications. To evaluate the proposed data quality assessment mechanism, web objects enabled affective stress and teens’ mood care semantic data applications are designed, and a deep data quality learning model is developed. The findings of the proposed approach reveal that, once a data quality assessment model is trained on web objects enabled healthcare semantic data, it could be used to classify the incoming data quality in various contextual data quality metric parameters. Moreover, the data quality assessment mechanism presented in this paper can be used to other application domains by incorporating data quality analysis requirements ontology.

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Ontological Multidimensional Data Models and Contextual Data Quality

Cited by 18 publications

References 81 publications

Factors Influencing Master Data Quality: A Systematic Review

Factors Influencing Master Data Quality: A Systematic Review

A Data Science Framework for Data Quality Assessment and Inconsistency Detection

Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application

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