Interactive Inference of Join Queries

Bonifati, Angela; Ciucanu, Radu; Staworko, Slawomir

Document type :

Communication dans un congrès avec actes

Title :

Interactive Inference of Join Queries

Author(s) :

Bonifati, Angela [Auteur]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Linking Dynamic Data [LINKS]
Ciucanu, Radu [Auteur correspondant]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Linking Dynamic Data [LINKS]
Staworko, Slawomir [Auteur]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Linking Dynamic Data [LINKS]

Conference title :

Gestion de Données - Principes, Technologies et Applications (BDA)

City :

Grenoble-Autrans

Country :

France

Start date of the conference :

2014-10-14

HAL domain(s) :

Informatique [cs]/Base de données [cs.DB]

English abstract : [en]

We investigate the problem of inferring join queries from user interactions. The user is presented with a set of candidate tuples and is asked to label them as positive or negative depending on whether or not she would ...
Show more >We investigate the problem of inferring join queries from user interactions. The user is presented with a set of candidate tuples and is asked to label them as positive or negative depending on whether or not she would like the tuples as part of the join result. The goal is to quickly infer an arbitrary n-ary join predicate across two relations by keeping the number of user interactions as minimal as possible. We assume no prior knowledge of the integrity constraints between the involved relations. This kind of scenario occurs in several application settings, such as data integration, reverse engineering of database queries, and constraint inference. In such scenarios, the database instances may be too big to be skimmed. We explore the search space by using a set of strategies that let us prune what we call "uninformative" tuples, and directly present to the user the informative ones i.e., those that allow to quickly find the goal query that the user has in mind. In this paper, we focus on the inference of joins with equality predicates and we show that for such joins deciding whether a tuple is uninformative can be done in polynomial time. Next, we propose several strategies for presenting tuples to the user in a given order that lets minimize the number of interactions. We show the efficiency and scalability of our approach through an experimental study on both benchmark and synthetic datasets. Finally, we prove that adding projection to our queries makes the problem intractable.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Nationale

Popular science :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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