1. Data Quality Alerting Model for Big Data Analytics
- Author
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Eliza Gyulgyulyan, Hrachya Astsatryan, Franck Ravat, Julien Aligon, National Academy of Sciences of the Republic of Armenia [Yerevan] (NAS RA), Systèmes d’Informations Généralisées (IRIT-SIG), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), and National Academy of Sciences of the Republic of Armenia (ARMENIA)
- Subjects
Swift ,050101 languages & linguistics ,Computer science ,Process (engineering) ,media_common.quotation_subject ,Big data ,Context (language use) ,02 engineering and technology ,Component (UML) ,0202 electrical engineering, electronic engineering, information engineering ,0501 psychology and cognitive sciences ,Quality (business) ,computer.programming_language ,media_common ,Big Data analytics ,Point (typography) ,business.industry ,05 social sciences ,Data quality ,Recherche d'information ,Data science ,[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR] ,020201 artificial intelligence & image processing ,business ,computer ,Quality model - Abstract
International audience; During Big Data analytics, correcting all the problems of large, heterogeneous and swift data, in a reasonable time, is a challenge and a costly process. Therefore, organizations are confronted with performing analysis on massive data, potentially of poor quality. This context is the starting point of our current research: how to identify data quality issues and how to notify users without solving these quality issues in advance? To this end, we propose a quality model, as the main component of an alert system, which allow to inform users about data quality issues, during their analysis. This paper discusses about the conceptual and implementation frameworks of the quality model, as well as examples of usage.
- Published
- 2019