Indexing Uncertain Categorical Data over Distributed Environment

Adel Benaissa (1) , Salima Benbernou (1) , Mourad Ouziri (1) , Soror Sahri (1)

1 LIPADE - EA 2517 - Laboratoire d'Informatique Paris Descartes

Today, a large amount of uncertain data is produced by several applications where the management systems of traditional databases incuding indexing methods are not suitable to handle such type of data. In this paper, we propose an inverted based index method for effciently searching uncertain categorical data over distributed environments. We adress two kinds of query over the distributed uncertain databases, one a distributed probabilis-tic thresholds query, where all results sastisfying the query with probablities that meet a probablistic threshold requirement are returned, and another a distributed top k-queries, where all results optimizing the transfer of the tuples and the time treatment are returned.

Mots clés: Uncertain database; indexing; distributed environment; top-k query; query optimization; threshold query

Domaines: Base de données [cs.DB]; Calcul parallèle, distribué et partagé [cs.DC]

Adel Benaissa, Salima Benbernou, Mourad Ouziri, Soror Sahri. Indexing Uncertain Categorical Data over Distributed Environment. The 16th World Congress of the International Fuzzy Systems Association (IFSA) and the 9th Conference of the European Society for Fuzzy Logic and Technology, European Centre for Soft Computing, Jun 2015, Gijon, Spain. ⟨10.2991/ifsa-eusflat-15.2015.197⟩. ⟨hal-01224226⟩