About Inclusion-Based Generalized Yes/No Queries in a Possibilistic Database Context

Bosc, Patrick; Liétard, Nadia; Pivert, Olivier

doi:10.1007/11875604_32

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…Ultimately, the procedure delivers the best (in terms of possibility degree) vector V . An adaptation of this algorithm to the case of inclusion-based yes-no queries is presented in , 19 where it is also shown that the algorithms for type 1 and type 2 generalized yes-no queries can be straightforwardly modified so as to make them work in a probabilistic database framework.…”

Section: Type 2 Generalized Yes-no Queriesmentioning

confidence: 99%

Modeling and Querying Uncertain Relational Databases: A Survey of Approaches Based on the Possible Worlds Semantics

Bosc

Pivert

2010

Int. J. Unc. Fuzz. Knowl. Based Syst.

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In this paper, we give an overview of the most representative approaches aimed at querying databases containing ill-known data, starting from the pioneering works by Codd and Lipski and up to very recent proposals. This study focuses on approaches with a clear and sound semantics, based on the notion of possible worlds. Three types of queries are considered: (i) those about attribute values (in an algebraic or SQL-like framework), (ii) those about the properties satisfied by a given set of worlds (i.e., a set of instances of an imprecise database), and (iii) those about the representation of uncertain data. For the first two types, it is emphasized that a trade-off has to be found between expressivity (of the model) and tractability (of the queries in the context of a given model).

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Section: Type 2 Generalized Yes-no Queriesmentioning

confidence: 99%

Modeling and Querying Uncertain Relational Databases: A Survey of Approaches Based on the Possible Worlds Semantics

Bosc

Pivert

2010

Int. J. Unc. Fuzz. Knowl. Based Syst.

Self Cite

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“…There are several types of indexing, but in general, the principle is to convert the data sources into electronic data. Indexing methods have been widely studied and are a very important option for Big Data [6] Most of the research proposed in the literature considers that indexing is already defined on a single data problem. The data that exists today presents a set of challenges and risks that researchers want to address [6].…”

Section: Introductionmentioning

confidence: 99%

“…Indexing methods have been widely studied and are a very important option for Big Data [6] Most of the research proposed in the literature considers that indexing is already defined on a single data problem. The data that exists today presents a set of challenges and risks that researchers want to address [6]. One of these challenges is the uncertainty of data sources and data.…”

Section: Introductionmentioning

confidence: 99%

Towards an Efficient Big Data Indexing Approach under an Uncertain Environment

Omri¹,

Omri²

2022

IJISA

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It is generally accepted that data production has experienced spectacular growth for several years due to the proliferation of new technologies such as new mobile devices, smart meters, social networks, cloud computing and sensors. In fact, this data explosion should continue and even accelerate. To find all of the documents responding to a request, any information search system develops a methodology to confirm whether or not the terms of each document correspond to those of the user's request. Most systems are based on the assumption that the terms extracted from the documents have been certain and precise. However, there are data in which this assumption is difficult to apply. The main objective of the work carried out within the framework of this article is to propose a new model of data service indexing in an uncertain environment, meaning that the data they contain can be untrustworthy, or they can be contradictory to another data source, due to failure in collection or integration mechanisms. The solution we have proposed is characterized by its Intelligent side ensured by an efficient fuzzy module capable of reasoning in an environment of uncertain and imprecise data. Concretely, our proposed approach is articulated around two main phases: (i) a first phase ensures the processing of uncertain data in a textual document and, (ii) the second phase makes it possible to determine a new method of uncertain syntactic indexing. We carried out a series of experiments, on different bases of standard tests, in order to evaluate our solution while comparing it to the approaches studied in the literature. We used different standard performance measures, namely precision, recall and F_measure. The results found showed that our solution is more efficient and more efficient than the main approaches proposed in the literature. The results show that the proposed approach realizes an efficient Big Data indexing solution in an Uncertain Environment that increases the Precision, the Recall and the F_measure measurements. Experimental results present that the proposed uncertain model obtained the best precision accuracy 0.395 with KDD database and the best recall accuracy 0.254 with the same database.

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Querying Data Services in an Uncertain Environment: A Possibilistic-Based Approach

Omri¹,

Benouaret²,

Omri³

et al. 2016

2016 12th International Conference on Signal-Image Technology &Amp; Internet-Based Systems (SITIS)

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About Inclusion-Based Generalized Yes/No Queries in a Possibilistic Database Context

Cited by 3 publications

References 6 publications

Modeling and Querying Uncertain Relational Databases: A Survey of Approaches Based on the Possible Worlds Semantics

Modeling and Querying Uncertain Relational Databases: A Survey of Approaches Based on the Possible Worlds Semantics

Towards an Efficient Big Data Indexing Approach under an Uncertain Environment

Querying Data Services in an Uncertain Environment: A Possibilistic-Based Approach

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