Flexible Query Answering in Data Cubes

Naouali, Sami; Missaoui, Rokia

doi:10.1007/11546849_22

Cited by 10 publications

(5 citation statements)

References 8 publications

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“…Yet another challenge -analogous to the one encountered by search engines -is that available data items and query conditions often do not perfectly match each other. In such situations, one may wish to retrieve approximately fitting items (Naouali and Missaoui 2005). However, such approaches usually require modifications of query syntax.…”

Section: Literature Overviewmentioning

confidence: 99%

A new approximate query engine based on intelligent capture and fast transformations of granulated data summaries

Ślęzak

Glick

Betliński³

et al. 2017

J Intell Inf Syst

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We outline the processes of intelligent creation and utilization of granulated data summaries in the engine aimed at fast approximate execution of analytical SQL statements. We discuss how to use the introduced engine for the purposes of ad-hoc data exploration over large and quickly increasing data collected in a heterogeneous or distributed fashion. We focus on mechanisms that transform input data summaries into result sets representing query outcomes. We also illustrate how our computational principles can be put together with other paradigms of scaling and harnessing data analytics.

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Section: Literature Overviewmentioning

confidence: 99%

A new approximate query engine based on intelligent capture and fast transformations of granulated data summaries

Ślęzak

Glick

Betliński³

et al. 2017

J Intell Inf Syst

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“…In the literature related to the quality of OLAP systems, there have also been proposals dealing with data fuzziness in particular. Some of the authors in this field advocated the use of fuzzy set theory (Delgado, Molina, Sánchez, Ariza, & Vila, 2004;Fasel & Zumstein, 2009;Laurent, 2002) or rough set theory (Naouali & Missaoui, 2005, 2006 to handle the data incompleteness and imprecision. The first ones proposed fuzzy multidimensional database models and fuzzy slices, dices and aggregations while (Naouali & Missaoui, 2005, 2006 contributions involve data approximation techniques by means of rough sets (upper and lower approximations).…”

Section: Spatial Vagueness Management In Solap Systemsmentioning

confidence: 99%

“…Some of the authors in this field advocated the use of fuzzy set theory (Delgado, Molina, Sánchez, Ariza, & Vila, 2004;Fasel & Zumstein, 2009;Laurent, 2002) or rough set theory (Naouali & Missaoui, 2005, 2006 to handle the data incompleteness and imprecision. The first ones proposed fuzzy multidimensional database models and fuzzy slices, dices and aggregations while (Naouali & Missaoui, 2005, 2006 contributions involve data approximation techniques by means of rough sets (upper and lower approximations). Also, the authors Pitarch, Favre, Laurent, and Poncelet (2012) exploited the fuzzy logic in proposing new contextual hierarchies to allow the consideration of data experts knowledge for the definition of the hierarchies.…”

Section: Spatial Vagueness Management In Solap Systemsmentioning

confidence: 99%

New Design Approach to Handle Spatial Vagueness in Spatial OLAP Datacubes

Edoh-Alove

Bimonte²,

Pinet³

et al. 2015

International Journal of Agricultural and Environmental Information Systems

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Spatial-OLAP (SOLAP) technologies are dedicated to multidimensional analysis of large volumes of (spatial) data. Spatial data are subject to different types of uncertainty, in particular spatial vagueness. Although several researches propose new models to cope with spatial vagueness, their integration in SOLAP systems is still in an embryonic state. Also, analyzing multidimensional data with metadata brought by the exploitation of the new models can be too complex and demanding for decision-makers. To help reduce spatial vagueness consequences on the exactness of SOLAP analysis queries, the authors present a new approach for designing SOLAP datacubes based on end-users' tolerance to the risks of misinterpretation of fact data. An experimentation of the new approach on agri-environmental data is also proposed.

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“…Rough sets turned out to be useful also in other fields of science and industry. For instance, they were adopted in design of database engines and solutions, with respect to query language extensions (which can be referred as rough querying) and acceleration of commercial database software performance [27], [28]. In all above scenarios, rough set methods (or mechanisms that adopt rough set principles) require an input to derive approximations of concepts, relations, etc.…”

Section: Introduction and Basic Conceptsmentioning

confidence: 99%

Rough Sets Turn 40: From Information Systems to Intelligent Systems

Skowron¹,

Ślęzak²

2022

Annals of Computer Science and Information Systems

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The theory of rough sets was founded by Zdzisław Pawlak as a framework for data and knowledge exploration. His seminal paper titled "Rough Sets" was published in 1982, in International Journal of Computer and Information Sciences. One of the key aspects that lets us use rough sets in practical scenarios is the notion of information system, which comes from even earlier Professor Pawlak's works. Information systems are the means for data and knowledge representation. They constitute the input to rough set mechanisms aimed at computing approximations of concepts and deriving compacted, interpretable decision models. In particular, the fundamental notion of the indiscernibility relation is defined on the basis of a given information system. Accordingly, we discuss to what extent information systems can serve as the basis for intelligent systems. We claim that in many cases it is not enough to treat a data set -represented as an information system -as a purely abstract object with no linkage to the data origins. Oppositely, we should give ourselves a technical possibility to construct information systems dynamically, taking into account interaction with physical environments where the data comes from. With this respect, we refer to the notions of interactive granular computing and we generally consider together the paradigms of rough sets, information systems, and information granulation.

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Flexible Query Answering in Data Cubes

Cited by 10 publications

References 8 publications

A new approximate query engine based on intelligent capture and fast transformations of granulated data summaries

A new approximate query engine based on intelligent capture and fast transformations of granulated data summaries

New Design Approach to Handle Spatial Vagueness in Spatial OLAP Datacubes

Rough Sets Turn 40: From Information Systems to Intelligent Systems

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