An Hybrid Approach to Quality Evaluation Across Big Data Value Chain (Serhani et al., 2016Serhani, M. A., El Kassabi, H. T., Taleb, I., & Nujum, A. (2016). A hybrid approach to quality evaluation across big data value chain. In2016
IEEE International Congress on Big Data
(BigData Congress)(pp. 418-425). IEEE.https://ieeexplore.ieee.org/abstract/document/7584971
https://ieeexplore.ieee.org/abstract/doc...
) |
2 Categories, 4 Dimensions |
Contextual: Accuracy Intrinsic: Timeliness, Completeness, Consistency |
An Investigation of How Data Quality is Affected by Dataset Size in the Context of Big Data Analytics (Woodall et al., 2014Woodall, P. et al., (2014). An Investigation of How Data Quality is Affected by Dataset Size in the Context of Big Data Analytics. In 19
th
International Conference on Information Quality
(ICIQ), Xi’an, China. https://is.gd/Woodall_et_al_big_data
https://is.gd/Woodall_et_al_big_data...
) |
1 Dimension |
Completeness |
Big Data Pre-Processing: Closing the Data Quality Enforcement Loop (Taleb & Serhani, 2017Taleb, I., & Serhani, M. A. (2017). Big Data Pre-Processing: Closing the Data Quality Enforcement Loop. In 2017
IEEE International Congress on Big Data
(BigData Congress) (pp. 498-501). https://ieeexplore.ieee.org/abstract/document/8029366
https://ieeexplore.ieee.org/abstract/doc...
) |
3 Dimensions |
Accuracy, Completeness, Consistency |
Big Data Quality: A Quality Dimensions Evaluation (Taleb et al., 2016Taleb, I., El Kassabi, H. T., Serhani, M. A., Dssouli, R., & Bouhaddioui, C. (2016). Big data quality: A quality dimensions evaluation. In2016
Intl IEEE Conferences on Ubiquitous Intelligence & Computing, (pp. 759-765).) |
3 Dimensions |
Accuracy, Completeness, Consistency |
Big Data Quality: A Survey (Taleb et al., 2018Taleb, I., Serhani, M. A., & Dssouli, R. (2018). Big data quality: A survey. In2018
IEEE International Congress on Big Data
(BigData Congress)(pp. 166-173). https://ieeexplore.ieee.org/abstract/document/8457745
https://ieeexplore.ieee.org/abstract/doc...
) |
4 Categories 18 Dimensions |
Intrinsic: Accuracy, Timeliness, Consistency, Completeness Contextual: Reputation, Relevancy, Accessibility, Quantity, Value-added, Believability Representational: Interpretability, Representational, Conciseness of representation, Consistency, Manipulability, Ease of understanding Accessibility: Access, Security |
Big Data Validation Case Study (Xie et al., 2017Xie, C., Gao, J., & Tao, C. (2017). Big data validation case study. In 2017
IEEE third international conference on big data computing service and applications
(BigDataService)(pp. 281-286). https://ieeexplore.ieee.org/abstract/document/7944952
https://ieeexplore.ieee.org/abstract/doc...
) |
4 Dimensions |
Validity, Completeness, Consistency, Accuracy |
Big Data, Big Data Quality Problem (Becker et al., 2015Becker, D., King, T. D., & McMullen, B. (2015). Big data, big data quality problem. In2015
IEEE International Conference on Big Data
(Big Data)(pp. 2644-2653). https://ieeexplore.ieee.org/abstract/document/7364064
https://ieeexplore.ieee.org/abstract/doc...
) |
7 Dimensions |
Accuracy, Precision, Completeness, Consistency, Timeliness, Lineage/Pedigree and Relevance |
Context-aware data quality assessment for big data (Ardagna et al., 2018Ardagna, D., Cappiello, C., Samá, W., & Vitali, M. (2018). Context-aware data quality assessment for big data.Future Generation Computer Systems
,
89, 548-562. https://re.public.polimi.it/retrieve/handle/11311/1057520/295709/FutureGeneration.pdf
https://re.public.polimi.it/retrieve/han...
) |
7 Dimensions |
Accuracy, Completeness, Consistency, Distinctness, Precision, Timeliness, Volume |
Data quality assessment: The Hybrid Approach (Woodall et al., 2013Woodall, P., Borek, A. & Kumar Parlikad, A., (2013). Data quality assessment: The Hybrid Approach. Information & Management, 50(7), 396-382. https://www.sciencedirect.com/science/article/abs/pii/S0378720613000517
https://www.sciencedirect.com/science/ar...
) |
2 Dimensions |
Completeness, Accuracy |
Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications (Hazen et al., 2014Hazen, B. T., Boone, C. A., Ezell, J. D. & Jones-Farmer, L. A., (2014). Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72-80. https://www.sciencedirect.com/science/article/abs/pii/S0925527314001339
https://www.sciencedirect.com/science/ar...
) |
2 Categories, 4 Dimensions |
Contextual: Accuracy Intrinsic: Timeliness, Completeness, Consistency |
Data quality in big data processing: Issues, solutions and open problems (Zhang et al., 2017Zhang, P., Xiong, F., Gao, J., & Wang, J. (2017). Data quality in big data processing: Issues, solutions and open problems. In2017
IEEE Smart World, Ubiquitous Intelligence & Computing. (pp. 1-7). https://ieeexplore.ieee.org/abstract/document/8397554
https://ieeexplore.ieee.org/abstract/doc...
) |
4 Dimensions |
Availability, Usability, Reliability, Relevance |
Data Quality Issues in Big Data (Rao et al., 2015Rao, D., Gudivada, V. N., & Raghavan, V. V. (2015). Data quality issues in big data. In2015
IEEE International Conference on Big Data
(Big Data)(pp. 2654-2660). https://ieeexplore.ieee.org/abstract/document/7364065
https://ieeexplore.ieee.org/abstract/doc...
) |
5 Dimensions |
Accuracy, Confidentiality, Completeness, Volume, Timeliness |
Data quality management, data usage experience and acquisition intention of big data analytics (Kwon et al., 2014Kwon, O., Lee, N. & Shin, B. (2014). Data quality management, data usage experience and acquisition intention of big data analytics. International Journal of Information Management, 34, 387-394. https://www.sciencedirect.com/science/article/pii/S0268401214000127
https://www.sciencedirect.com/science/ar...
) |
2 Dimensions |
Consistency, Completeness |
From Data Quality to Big Data Quality (Batini et al., 2015Batini, C., Rula, A., Scannapieco, M., & Viscusi, G. (2015). From Data Quality to Big Data Quality. Journal of Database Management, 26(1), 60-82. https://www.igi-global.com/article/from-data-quality-to-big-data-quality/140546
https://www.igi-global.com/article/from-...
) |
7 Clusters 17 Dimensions |
Accuracy: Correctness, Validity and Precision Completeness: Pertinence, Relevance Redundancy: Minimality, Compactness and Conciseness Readability: Comprehensibility, Clarity and Simplicity Accessibility: Availability Consistency: Cohesion and Coherence Trust: Believability, Reliability and Reputation |
My (Fair) Big Data (Catarci et al., 2017Catarci, T., Scannapieco, M., Console, M., & Demetrescu, C. (2017). My (fair) big data. In 2017
IEEE International Conference on Big Data
(Big Data) (pp. 2974-2979). https://ieeexplore.ieee.org/abstract/document/8258267
https://ieeexplore.ieee.org/abstract/doc...
) |
3 Dimensions |
Consistency, Accuracy, Confidentiality |
The Challenges of Data Quality and Data Quality Assessment in the Big Data Era (Cai & Zhu, 2015Cai, L. & Zhu, Y.. (2015). The Challenges of Data Quality and Data Quality Assessment in the Big Data Era. Data Science Journal, 14(2), 1-10. https://datascience.codata.org/articles/10.5334/dsj-2015-002/
https://datascience.codata.org/articles/...
) |
5 Dimensions |
Availability, Usability, Reliability, Relevance, and Presentation quality |
Big Data Quality Metrics for Sentiment Analysis Approaches (El Alaoui, Gahi & Messoussi 2019El Alaoui, I., Gahi, Y., & Messoussi, R. (2019). Big Data Quality Metrics for Sentiment Analysis Approaches. InProceedings of the
2019
International Conference on Big Data Engineering(pp. 36-43). https://dl.acm.org/citation.cfm?id=3341629
https://dl.acm.org/citation.cfm?id=33416...
) |
11 dimensions |
Real-time analyzability, accuracy, completeness, uniqueness, transformation, conformity, normalization, referential integrity, consistency, credibility, freshness |