A classification of data quality assessment and improvement methods

Woodall, Philip; Oberhofer, Martin; Borek, Alexander

doi:10.1504/ijiq.2014.068656

Cited by 25 publications

(21 citation statements)

References 18 publications

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“…As recommender systems are an important category of decision support systems (Power et al 2015), this research is in line with recent works which have revealed a significant impact of data quality dimensions, especially completeness, on data-driven decision support systems (e.g., Feldman et al 2018;Heinrich et al 2019;Woodall et al 2015).…”

Section: Introductionsupporting

confidence: 86%

“…Mladenić and Grobelnik 2003;Vanaja and Mukherjee 2019), as different data sets (e.g., with more or fewer attributes) may lead to varying results of decision support. Thus, the impact of the data quality of data values on different evaluation criteria of decision support systems such as decision quality or data mining outcome has been studied in existing literature (e.g., Bharati and Chaudhury 2004;Blake and Mangiameli 2011;Feldman et al 2018;Ge 2009;Heinrich et al 2019;Woodall et al 2015). Yet, this research neither focuses on how to systematically achieve more complete item content data sets nor on how to define a wellfounded procedure, but instead tries to explain the relationship between data quality and evaluation criteria of decision support systems.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Blake and Mangiameli (2011) assess the impact of accuracy, completeness, consistency and currency on data mining results in order to support decision-making in companies. Woodall et al (2015) analyze the impact of completeness on classification outcomes used for supporting users in their decision process. Feldman et al (2018) propose an analytical framework to investigate the effects of incomplete data sets on a binary classifier that serves for decision support.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

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Something’s Missing? A Procedure for Extending Item Content Data Sets in the Context of Recommender Systems

et al. 2020

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The rapid development of e-commerce has led to a swiftly increasing number of competing providers in electronic markets, which maintain their own, individual data describing the offered items. Recommender systems are popular and powerful tools relying on this data to guide users to their individually best item choice. Literature suggests that data quality of item content data has substantial influence on recommendation quality. Thereby, the dimension completeness is expected to be particularly important. Herein resides a considerable chance to improve recommendation quality by increasing completeness via extending an item content data set with an additional data set of the same domain. This paper therefore proposes a procedure for such a systematic data extension and analyzes effects of the procedure regarding items, content and users based on real-world data sets from four leading web portals. The evaluation results suggest that the proposed procedure is indeed effective in enabling improved recommendation quality.

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Section: Introductionsupporting

confidence: 86%

Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Theoretical Backgroundmentioning

confidence: 99%

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Something’s Missing? A Procedure for Extending Item Content Data Sets in the Context of Recommender Systems

et al. 2020

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“…Even if the data extraction process is perfect, if the data from the source systems contains errors, then these will propagate to the DT. There are, however, certain types of these errors that can be detected and corrected in the transformation process, such as incorrectly formatted data and invalid data (Woodall et al 2014). DTs may utilise publicly available online data in the city level, such as using weather forecasts etc.…”

Section: Analysis Of the Dts Development From The Perspective Of Data Managementmentioning

confidence: 99%

Developing a Digital Twin at Building and City Levels: Case Study of West Cambridge Campus

et al. 2020

Self Cite

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A Digital Twin (DT) refers to a digital replica of physical assets, processes and systems. DTs integrate artificial intelligence, machine learning and data analytics to create living digital simulation models that are able to learn and update from multiple sources, and to represent and predict the current and future conditions of physical counterparts. However, the current activities related to DTs are still at an early stage with respect to buildings and other infrastructure assets from an architectural and engineering/construction point of view. Less attention has been paid to the operation & maintenance (O&M) phase, which is the longest time span in the asset life cycle. A systematic and clear architecture verified with practical use cases for constructing a DT would be the foremost step for effective operation and maintenance of buildings and cities. To this end, this paper presents a system architecture for DTs, which is specifically designed at both the building and city levels. Based on current research about multi-tier architectures, this proposed DT architecture enables integration of heterogeneous data sources, supports effective data querying and analysing, supports decision-making processes in O&M management, and further bridges the gap between human relationships with buildings/cities. Based on this architecture, a DT demonstrator of the West Cambridge site of the University of Cambridge was developed. This paper aims at going through the whole

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“…At the contrary, most of these methods are focused on DQ assessment or improvement in isolation. Similar to the mentioned review, a most recent study developed by Woodall et al [ 33 ] classified most recent DQ assessment and improvement methods. This work suffers the same problem than the work of Batini et al Apart of these methodologies, there is a lack of comprehensive methodologies for the assessment and improvement of DQ in the domain of SCP operations and their underlaying data.…”

Section: Related Workmentioning

confidence: 99%

DAQUA-MASS: An ISO 8000-61 Based Data Quality Management Methodology for Sensor Data

Pérez‐Castillo

Carretero

Caballero

et al. 2018

Sensors

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The Internet-of-Things (IoT) introduces several technical and managerial challenges when it comes to the use of data generated and exchanged by and between various Smart, Connected Products (SCPs) that are part of an IoT system (i.e., physical, intelligent devices with sensors and actuators). Added to the volume and the heterogeneous exchange and consumption of data, it is paramount to assure that data quality levels are maintained in every step of the data chain/lifecycle. Otherwise, the system may fail to meet its expected function. While Data Quality (DQ) is a mature field, existing solutions are highly heterogeneous. Therefore, we propose that companies, developers and vendors should align their data quality management mechanisms and artefacts with well-known best practices and standards, as for example, those provided by ISO 8000-61. This standard enables a process-approach to data quality management, overcoming the difficulties of isolated data quality activities. This paper introduces DAQUA-MASS, a methodology based on ISO 8000-61 for data quality management in sensor networks. The methodology consists of four steps according to the Plan-Do-Check-Act cycle by Deming.

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A classification of data quality assessment and improvement methods

Cited by 25 publications

References 18 publications

Something’s Missing? A Procedure for Extending Item Content Data Sets in the Context of Recommender Systems

Something’s Missing? A Procedure for Extending Item Content Data Sets in the Context of Recommender Systems

Developing a Digital Twin at Building and City Levels: Case Study of West Cambridge Campus

DAQUA-MASS: An ISO 8000-61 Based Data Quality Management Methodology for Sensor Data

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