A Paradigm for Learning Queries on Big Data
Type de document :
Communication dans un congrès avec actes
DOI :
Titre :
A Paradigm for Learning Queries on Big Data
Auteur(s) :
Bonifati, Angela [Auteur]
Linking Dynamic Data [LINKS ]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Ciucanu, Radu [Auteur correspondant]
Linking Dynamic Data [LINKS ]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Lemay, Aurélien [Auteur]
Linking Dynamic Data [LINKS ]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Staworko, Slawomir [Auteur]
Linking Dynamic Data [LINKS ]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Linking Dynamic Data [LINKS ]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Ciucanu, Radu [Auteur correspondant]
Linking Dynamic Data [LINKS ]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Lemay, Aurélien [Auteur]
Linking Dynamic Data [LINKS ]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Staworko, Slawomir [Auteur]
Linking Dynamic Data [LINKS ]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Titre de la manifestation scientifique :
First International Workshop on Bringing the Value of "Big Data" to Users (Data4U)
Ville :
Hangzhou
Pays :
Chine
Date de début de la manifestation scientifique :
2014-09-01
Discipline(s) HAL :
Informatique [cs]/Base de données [cs.DB]
Résumé en anglais : [en]
Specifying a database query using a formal query language is typically a challenging task for non-expert users. In the context of big data, this problem becomes even harder as it requires the users to deal with database ...
Lire la suite >Specifying a database query using a formal query language is typically a challenging task for non-expert users. In the context of big data, this problem becomes even harder as it requires the users to deal with database instances of big sizes and hence difficult to visualize. Such instances usually lack a schema to help the users specify their queries, or have an incomplete schema as they come from disparate data sources. In this paper, we propose a novel paradigm for interactive learning of queries on big data, without assuming any knowledge of the database schema. The paradigm can be applied to different database models and a class of queries adequate to the database model. In particular, in this paper we present two instantiations that validated the proposed paradigm for learning relational join queries and for learning path queries on graph databases. Finally, we discuss the challenges of employing the paradigm for further data models and for learning cross-model schema mappings.Lire moins >
Lire la suite >Specifying a database query using a formal query language is typically a challenging task for non-expert users. In the context of big data, this problem becomes even harder as it requires the users to deal with database instances of big sizes and hence difficult to visualize. Such instances usually lack a schema to help the users specify their queries, or have an incomplete schema as they come from disparate data sources. In this paper, we propose a novel paradigm for interactive learning of queries on big data, without assuming any knowledge of the database schema. The paradigm can be applied to different database models and a class of queries adequate to the database model. In particular, in this paper we present two instantiations that validated the proposed paradigm for learning relational join queries and for learning path queries on graph databases. Finally, we discuss the challenges of employing the paradigm for further data models and for learning cross-model schema mappings.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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