A Data Quality Methodology for Heterogeneous Data

Batini, Carlo; Barone, Daniele; Cabitza, Federico; Grega, Simone

doi:10.5121/ijdms.2011.3105

Cited by 33 publications

(13 citation statements)

References 26 publications

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“…Similarly, in the data quality literature, the data is assessed based on data metadata and properties. These data attributes are called data quality dimensions, which are used to measure the quality of data [15,24,25]. The measurement procedure of these data quality dimensions is called data quality metrics or indicators [15,25].…”

Section: Background and Related Workmentioning

confidence: 99%

“…These data attributes are called data quality dimensions, which are used to measure the quality of data [15,24,25]. The measurement procedure of these data quality dimensions is called data quality metrics or indicators [15,25]. The assessment of data quality is very crucial when it comes to choose one dataset over the other or rank the dataset for specific information need and application context [26][27][28].…”

Section: Background and Related Workmentioning

confidence: 99%

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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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Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

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

Jarwar

Chong²

2020

Applied Sciences

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“…There are, in fact, methodologies in place like the Heterogeneous Data Quality Management (HDQM) methodology that deals with the assessment of Heterogeneous data [7]. In HDQM the authors describe heterogeneity in the context of different data sets contained inside an organisation.…”

Section: Engineering Data Contextmentioning

confidence: 99%

“…This approach is one considering data composition on a data set wide basis. Our breakdown, which is related to data used for type of data quality analysis, provides for a more distinct deeper understanding of relationships between data, while in [7] the methodology links into developing a unified conceptual representation of all the data considered then assessing quality based on the homogeneous unified representation.…”

Section: Engineering Data Contextmentioning

confidence: 99%

Towards a Data Quality Framework for Heterogeneous Data

Micic

Neagu

Campean

et al. 2017

2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and I

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Every industry has significant data output as a product of their working process, and with the recent advent of big data mining and integrated data warehousing it is the case for a robust methodology for assessing the quality for sustainable and consistent processing. In this paper a review is conducted on Data Quality (DQ) in multiple domains in order to propose connections between their methodologies. This critical review suggests that within the process of DQ assessment of heterogeneous data sets, not often are they treated as separate types of data in need of an alternate data quality assessment framework. We discuss the need for such a directed DQ framework and the opportunities that are foreseen in this research area and propose to address it through degrees of heterogeneity.

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“…A survey of various techniques have been undertaken for addressing the duplicate data problem from Ahmed et al [11], Data Cleaning as proposed in Rehm et al [12] and Ali et al [13] , rule-based taxonomy from Li et al [14], Heterogenous Data Quality M ethodology (HDQM ) from Carlo et al [15] , metamodel extension to CWM in Gomes et al [16] ,Data Profiling in Sankar et al [17] and DeM aio [18].Here in this paper focus has been given primarily on exploring and extending the benefits of Data Profiling.…”

Section: Ongoing Countermeasurementioning

confidence: 99%

Analysis of Data Quality and Performance Issues in Data Warehousing and Business Intelligence

Debbarma¹,

Nath²,

Das³

2013

IJCA

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A Data Warehouse is an integral part of those enterprises which want to have a clear business insights from customer and operational data. It includes collection of technologies aimed at enabling the knowledge worker (executive, manager, analyst) to make better and faster decisions. It is expected to present the right information in the right place at the right time with the right cost in order to support the right decision. Over the years ,the practice of Data warehousing proved that the traditional online Transaction Processing (OLTP) systems are not fully appropriate for decision support. From the survey and evaluation of the literature related to Data Warehouse and with consultation and feedback of the data warehouse practitioners working in renowned IT giants ,it has been observed that the fundamental problems arise in populating a warehouse with quality data. . This paper mainly focuses on the study of the issues that hinder the data quality and performance of the Data warehouse and some of the means that may be opted to realize a better performance with respect to accuracy and quality to meet the challenging and dynamic needs of the corporate world.

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A Data Quality Methodology for Heterogeneous Data

Abstract: We present a Heterogenous Data Quality Methodology (HDQM)

Cited by 33 publications

References 26 publications

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

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

Towards a Data Quality Framework for Heterogeneous Data

Analysis of Data Quality and Performance Issues in Data Warehousing and Business Intelligence

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