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Fuzzy Multidimensional Databases
Abstract
The interest for OLAP (standing for On-Line Analytical Processing), working on multidimensional databases is growing dramatically due to its interest
in data analysis and data mining. Recent works (LBMD+00),(LGM00) showed the great interest of integrating fuzzy set theory
in such technologies in the framework of data mining. We now propose to enhance the multidimensional data model to handle
fuzziness. This model then provides the way to apply OLAP Mining methods on Fuzzy Multidimensional Databases, for Fuzzy-OLAP Mining.
Uncertainty extensively exists in data and knowledge intensive applications, in which fuzzy information processing plays a crucial role. Fuzzy sets have been extensively used to enhance various database models for managing fuzzy data or flexibly querying crisp data. This has resulted in numerous contributions in this research area. This paper pays attention to three crucial issues in fuzzy techniques for data management: modeling fuzzy data, querying fuzzy data, and fuzzy queries over crisp data, and provides a full up-to-date survey on the current state of the art in fuzzy data modeling and querying. The paper identifies fuzzy conceptual data models, fuzzy (relational and object-oriented) database models and fuzzy XML model as well as the relationships among these fuzzy data models. For each type of fuzzy data models, the paper summarizes its query processing. The paper also reviews fuzzy querying over classical data models. In addition to providing a generic overview of the approaches for fuzzy data modeling and querying, this survey paper serves for identifying possible research opportunities in the area of fuzzy data processing.
Process of finding unknown and potentially useful patterns from huge geo spatial data sets is called spatial data mining. In spatial data mining the object of interest might have influence of neighboring objects, that is why useful data pattern extraction from geo spatial data sets is much more complex and difficult task than extraction of regular numeric or character data from data stores. Spatial data is kept differently in the data base; also the relationships made upon spatial data are very different in nature. A novel approach of spatial data mining stored in an RDBMS is presented in this research. Spatial data mining if accomplished can be very useful in location prediction and other geographical analysis.
Résumé L'article discute les principaux problèmes que soulève la prise en compte d'informations incomplètes, et présente les approches qui ont été développées en bases de données et en intelligence artificielle. Il fait une place importante à des idées proposées récemment : prise en compte d'informations de complétude et de validité, interrogation portant explicitement sur le caractère incomplet de l'information, modélisation bipolaire de l'information, gestion de vues incomplètes, notamment. Il traite enfin de la génération d'informations synthétiques.
Mining sequential patterns aims at discovering correlations between events through time. However, even if many works have
dealt with sequential pattern mining, none of them considers frequent sequential patterns involving several dimensions in
the general case. In this paper, we propose a novel approach, called M
2
SP, to mine multidimensional sequential patterns. The main originality of our proposition is that we obtain not only intra-pattern
sequences but also inter-pattern sequences. Moreover, we consider generalized multidimensional sequential patterns, called
jokerized patterns, in which some of the dimension values may not be instanciated. Experiments on synthetic data are reported
and show the scalability of our approach.
KeywordsData Mining-Sequential Patterns-Multidimensional Rules
With the recent and continuing advances in areas such as wireless communications and positioning technologies, mobile, location-based services are becoming possible.
Such services deliver location-dependent content to their users. More specifically, these services may capture the movements and requests of their users in multidimensional databases, i.e., data warehouses, and content delivery may be based on the results of complex queries on these data warehouses. Such queries aggregate detailed data in order to find useful patterns, e.g., in the interaction of a particular user with the services.
The application of multidimensional technology in this context poses a range of new challenges. The specific challenge addressed here concerns the provision of an appropriate multidimensional data model. In particular, the paper extends an existing multidimensional data model and algebraic query language to accommodate spatial values that exhibit partial containment relationships instead of the total containment relationships normally assumed in multidimensional data models. Partial containment introduces imprecision in aggregation paths. The paper proposes a method for evaluating the imprecision of such paths. The paper also offers transformations of dimension hierarchies with partial containment relationships to simple hierarchies, to which existing precomputation techniques are applicable.
Multiagent systems and data mining have recently attracted considerable attention in the field of computing. Reinforcement learning is the most commonly used learning process for multiagent systems. However, it still has some drawbacks, including modeling other learning agents present in the domain as part of the state of the environment, and some states are experienced much less than others, or some state-action pairs are never visited during the learning phase. Further, before completing the learning process, an agent cannot exhibit a certain behavior in some states that may be experienced sufficiently. In this study, we propose a novel multiagent learning approach to handle these problems. Our approach is based on utilizing the mining process for modular cooperative learning systems. It incorporates fuzziness and online analytical processing (OLAP) based mining to effectively process the information reported by agents. First, we describe a fuzzy data cube OLAP architecture which facilitates effective storage and processing of the state information reported by agents. This way, the action of the other agent, not even in the visual environment. of the agent under consideration, can simply be predicted by extracting online association rules, a well-known data mining technique, from the constructed data cube. Second, we present a new action selection model, which is also based on association rules mining. Finally, we generalize not sufficiently experienced states, by mining multilevel association rules from the proposed fuzzy data cube. Experimental results obtained on two different versions of a well-known pursuit domain show the robustness and effectiveness of the proposed fuzzy OLAP mining based modular learning approach. Finally, we tested the scalability of the approach presented in this paper and compared it with our previous work on modular-fuzzy Q-learning and ordinary Q-learning.
Most real world databases consist of historical and numerical data such as sensor, scientific or even demographic data. In this context, classical algorithms extracting sequential patterns, which are well adapted to the temporal aspect of data, do not allow numerical information processing. Therefore, the data are pre-processed to be transformed into a binary representation, which leads to a loss of information. Fuzzy algorithms have been proposed to process numerical data using intervals, particularly fuzzy intervals, but none of these methods is satisfactory. Therefore this paper completely defines the concepts linked to fuzzy sequential pattern mining. Using different fuzzification levels, we propose three methods to mine fuzzy sequential patterns and detail the resulting algorithms (SpeedyFuzzy, MiniFuzzy, and TotallyFuzzy). Finally, we assess them through different experiments, thus revealing the robustness and the relevancy of this work.
On-Line Analytical Processing (OLAP) technologies are being used widely, but the lack of effective means of handling data imprecision, which occurs when exact values are not known precisely or are entirely missing, represents a major obstacle in applying these technologies in many domains. This paper develops techniques for handling imprecision that aim to maximally reuse existing OLAP modeling constructs such as dimension hierarchies and granularities. With imprecise data available in the database, queries are tested to determine whether or not they may be answered precisely given the available data; if not, alternative queries unaffected by the imprecision are suggested. When processing queries affected by imprecision, techniques are proposed that take into account the imprecision in the grouping of the data, in the subsequent aggregate computation, and in the presentation of the imprecise result to the user. The approach is capable of exploiting existing OLAP query processing techniques such as pre-aggregation, yielding an effective approach with low computational overhead and that may be implemented using current technology.
A multidimensional database is a data repository that supports the efficient execution of complex business decision queries. Query response can be significantly improved by storing an appropriate set of materialized views. These views are selected from the multidimensional lattice whose elements represent the solution space of the problem. Several techniques have been proposed in the past to perform the selection of materialized views for databases with a reduced number of dimensions. When the number and complexity of dimensions increase, the proposed techniques do not scale well. The technique we are proposing reduces the solution space by considering only the relevant elements of the multidimensional lattice. An additional statistical analysis allows a further reduction of the solution space. 1 Introduction A multidimensional database (MDDB) is a data repository that provides an integrated environment for decision support queries that require complex aggregations ...
On-Line Analytical Processing (OLAP) is a trend in database technology, which was recently introduced and has attracted the interest of a lot of research work. OLAP is based on the multidimensional view of data, supported either by multidimensional databases (MOLAP) or relational engines (ROLAP).
. Most of the existing learning systems work on data that are stored in poorly structured files. This approach prevents them from dealing with data from real world, which is often heterogeneous and massive and which requires database management tools. In this article, we propose an original solution to data mining which integrates a fuzzy learning tool that constructs fuzzy decision trees with a multidimensional database management system. 1 Introduction Fuzzy decision tree based methods provide good tools to discover knowledge from data. They are equivalent to a set of if-then rules and are declarative since the classification they propose may be explained. Moreover the use of fuzzy set theory allows the treatment of numerical values in a more natural way. But most existing solutions to construct decision trees use files and it is wellknown that this approach is reasonable only if the amount of data used for knowledge discovery is rather small (e.g. fits in core memory). Often, thes...
We present a multi-dimensional database model, which we believe can serve as a conceptual model for On-Line Analytical Processing (OLAP)-based applications. Apart from providing the functionalities necessary for OLAP-based applications, the main feature of the model we propose is a clear separation between structural aspects and the contents. This separation of concerns allows us to define data manipulation languages in a reasonably simple, transparent way. In particular, we show that the data cube operator can be expressed easily. Concretely, we define an algebra and a calculus and show them to be equivalent. We conclude by comparing our approach to related work. The conceptual multi-dimensional database model developed here is orthogonal to its implementation, which is not a subject of the present paper. 1 Introduction Currently, there is significant interest in multidimensional database systems for developing business analysis and decision support applications. Cod...
Systems for On-Line Analytical Processing (OLAP) considerably ease the process of analyzing business data and have become widely used in industry. OLAP systems primarily employ multidimensional data models to structure their data. However, current multidimensional data models fall short in their ability to model the complex data found in some real-world application domains. The paper presents nine requirements to multidimensional data models, each of which is exemplified by a real-world, clinical case study. A survey of the existing models reveals that the requirements not currently met include support for many-to-many relationships between facts and dimensions, built-in support for handling change and time, and support for uncertainty as well as different levels of granularity in the data. The paper defines an extended multidimensional data model, which addresses all nine requirements. Along with the model, we present an associated algebra, and outline how to implement the model using...
this paper we provided a categorization of the work in the area of OLAP logical models by surveying some major efforts, from commercial tools, benchmarks and standards, and academic efforts. We have also attempted a comparison of the various models along several dimensions, including representation and querying aspects.
The volume "Fuzziness in Database Management Systems" is a highly informative, well-organized and up-to-date collection of contributions authored by many of the leading experts in its field. Among the contributors are the editors, Professors Patrick Bose and Janusz Kacprzyk, both of whom are known internationally. The book is like a movie with an all-star cast. The issue of fuzziness in database management systems has a long history. It begins in 1968 and 1971, when I spent my sabbatical leaves at the IBM Research Laboratory in San Jose, California, as a visiting scholar. During these periods I was associated with Dr. E.F. Codd, the father of relational models of database systems, and came in contact with the developers ofiBMs System Rand SQL. These associations and contacts at a time when the methodology of relational models of data was in its formative stages, made me aware of the basic importance of such models and the desirability of extending them to fuzzy database systems and fuzzy query languages. This perception was reflected in my 1973 ffiM report which led to the paper on the concept of a linguistic variable and later to the paper on the meaning representation language PRUF (Possibilistic Relational Universal Fuzzy). More directly related to database issues during that period were the theses of my students V. Tahani, J. Yang, A. Bolour, M. Shen and R. Sheng, and many subsequent reports by both graduate and undergraduate students at Berkeley.
Data warehouses are now well recognized as the way to store historical data that will then be available for future queries and analysis. In this context, some challenges are still open, among which the problem of mining such data. OLAP mining, introduced by J. Han in 1997, aims at coupling data mining techniques and data warehousing. These techniques have to take the specificities of such data into account. One of the specificities that is often not addressed by classical methods for data mining is the fact that data warehouses describe data through several dimen- sions. Moreover, the data are stored through time, and we thus argue that sequential patterns are one of the best ways to summarize the trends from such databases. Se- quential pattern mining aims at discovering correlations among events through time. However, the number of patterns can become very important when taking several analysis dimensions into account, as it is the case in the framework of multidimen- sional databases. This is why we propose here to define a condensed representation without loss of information: closed multidimensional sequential patterns. This rep- resentation introduces properties that allow to deeply prune the search space. In this paper, we also define algorithms that do not require candidate set maintenance. Ex- periments on synthetic and real data are reported and emphasize the interest of our proposal.
Proper management of multidimensional aggregates is a fundamental prerequisite for efficient OLAP. The experimental OLAP server CubeStar, which concepts are described in this paper, was designed exactly for that purpose. All logical query processing is based solely on a specific algebra for multidimensional data. However, a relational database system is used for the physical storage of the data. Therefore, in popular terms CubeStar can be classified as a ROLAP system. In comparison to commercially available systems, CubeStar is superior in two aspects: First, the implemented multidimensional data model allows more adequate modeling of hierarchical dimensions, because properties which apply only to certain dimensional elements can be modeled context-sensitively. This fact is reflected by an extended star schema on the relational side. Second, CubeStar supports multidimensional query optimization by caching multidimensional aggregates. Since summary tables are not created in advance but as needed, hot spots can be adequately represented. The dynamic and partition-oriented caching method allows cost reductions of up to 60% with space requirements of less than 10% of the size of the fact table.
Geographic information systems manage a wide variety of data. Some of these data are stored in traditional databases, but much more is not. We developed a model, based on the concept of aggregating data into sets, to manage a wide variety of diverse data formats as a single logical entity. Because it manages descriptive information (metadata) about sets rather than the data files themselves, this model will be able to accommodate new data formats as they are developed in the future. The model was initially annotated as an entity relation diagram. It was then extended with concepts from fuzzy set theory in order to deal with problems that occur when selecting among similar sets with overlapping data. The new fuzzy notations developed in this research are generally applicable to all entity relation data modeling and provide the basis for a new, more robust and descriptive type of modeling methodology.
We propose a classification of measures enabling to compare fuzzy characterizations of objects, according to their properties and the purpose of their utilization. We establish the difference between measures of satisfiability, resemblance, inclusion and dissimilarity. We base our study on concepts analogous to those developed by A. Tversky for his general work on similarities.
A data warehouse integrates large amounts of extracted and summarized data from multiple sources for direct querying and analysis.
While it provides decision makers with easy access to such historical and aggregate data, the real meaning of the data has
been ignored. For example, “Whether a total sales amount 1000 items indicates a good or bad sales performance is still unclear.”
From the decision makers’ point of view, the semantics rather than raw numbers which convey the meaning of the data is very
important. In this paper, we explore fuzzy technology to provide this semantics for the summarizations and aggregates developed
in data warehousing systems. A three-layered data summarization architecture, namely, quantitative (numerical) summarization,
qualitative (categorical) summarization, and quantifier summarization, is proposed. To facilitate the construction of these
three summarization levels, two operators are introduced. We provide query capabilities against such enhanced data warehouses
by extensions of SQL.
Data analysis applications typically aggregate data across many dimensions looking for unusual patterns. The SQL aggregate functions and the GROUP BY operator produce zero-dimensional or one-dimensional answers. Applications need the N-dimensional generalization of these operators. The paper defines that operator, called the data cube or simply cube. The cube operator generalizes the histogram, cross-tabulation, roll-up, drill-down, and sub-total constructs found in most report writers. The cube treats each of the N aggregation attributes as a dimension of N-space. The aggregate of a particular set of attribute values is a point in this space. The set of points forms an N-dimensionaI cube. Super-aggregates are computed by aggregating the N-cube to lower dimensional spaces. Aggregation points are represented by an “infinite value”: ALL, so the point (ALL,ALL,...,ALL, sum(*)) represents the global sum of all items. Each ALL value actually represents the set of values contributing to that aggregation
Fuzzy sets were introduced by Zadeh [9] in 1965 to represent/manipu-late data and information possessing nonstatistical uncertainties. Fuzzy sets serve as a means
of representing and manipulating data that are not precise, but rather fuzzy.
Intelligent data analysis faces the problem of the huge amounts of data. More and more, database management systems are required to deal with this large repositories. In this framework, multidimensional databases are particularly adapted. They have emerged to support the OLAP framework. OLAP, standing for On Line Analytical Processing, is devoted to the fast analysis of multidimensional data. This model has been recently extended to the treatment of imperfect data and flexible queries. In this paper, we propose a new architecture based on fuzzy multidimensional databases to generate fuzzy summaries. This approach offers two main advantages. First, it provides a scalable framework due to the use of a database management system. Second, the introduction of fuzziness provides a theoretical framework to handle data from the real world and flexible queries. The chosen data mining tool is the generation of linguistic summaries. This kind of rules is a more understandable knowledge for the user than classical association rules. A user-friendly system is provided. This approach is compared to existing frameworks devoted to data analysis with association rules or fuzzy summaries. We insist on the fact that this model generalizes the classical one. It provides a framework to handle all classical crisp cases, since fuzzy set theory provides means to handle imperfect and classical data. Thus this method may be applied on classical data to generate fuzzy summaries.
Great efforts have been paid in the Intelligent Database Systems Research Lab for the research and development of efficient data mining methods and construction of on-line analytical data mining systems.Our work has been focused on the integration of data mining and OLAP technologies and the development of scalable, integrated, and multiple data mining functions. A data mining system, DBMiner, has been developed for interactive mining of multiple-level knowledge in large relational databases and data warehouses. The system implements a wide spectrum of data mining functions, including characterization, comparison, association, classification, prediction, and clustering. It also builds up a user-friendly, interactive data mining environment and a set of knowledge visualization tools. In-depth research has been performed on the efficiency and scalability of data mining methods. Moreover, the research has been extended to spatial data mining, multimedia data mining, text mining, and Web mining with several new data mining system prototypes constructed or under construction, including GeoMiner, MultiMediaMiner, and WebLogMiner.This article summarizes our research and development activities in the last several years and shares our experiences and lessons with the readers.
In this paper, we study the properties of flexible queries in the OLAP (On-Line Analytical Processing) framework, focusing on unary operators. For this purpose, we consider the model we have defined for fuzzy multidimensional databases. This model provides means to handle fuzzy data and flexible queries. The operators defined in this model are closed on the set of fuzzy hypercubes (hereafter cubes), which means that the result of each operator on a fuzzy cube is a cube. Thus, these operators can be nested into expressions. In this paper, the combination of several queries is investigated in order to study the possibility for the definition of an algebra to manipulate fuzzy cubes. This would provide a framework for query rewriting and, as a result, for query optimization.
The authors propose a data model and a few algebraic operations
that provide semantic foundation to multidimensional databases. The
distinguishing feature of the proposed model is the symmetric treatment
not only of all dimensions but also measures. The model provides support
for multiple hierarchies along each dimension and support for ad hoc
aggregates. The proposed operators are composable, reorderable, and
closed in application. These operators are also minimal in the sense
that none can be expressed in terms of others nor can any one be dropped
without sacrificing functionality. They make possible the declarative
specification and optimization of multidimensional database queries that
are currently specified operationally. The operators have been designed
to be translated to SQL and can be implemented either on top of a
relational database system or within a special purpose multidimensional
database engine. In effect, they provide an algebraic application
programming interface (API) that allows the separation of the front end
from the back end. Finally, the proposed model provides a framework in
which to study multidimensional databases and opens several new research
problems
As a result of the use of OLAP technology in new fields of knowledge and the merging of data from different sources, it has become necessary for models to support this technology. In this paper, we shall propose a new multidimensional model that can manage imprecision in both dimensions and facts and hide the complexity to the end user. The multidimensional structure is therefore able to model data imprecision resulting from the integration of data from different sources or even information from experts, which it does by means of fuzzy logic
On-Line Analytical Processing (OLAP) systems considerably ease the process of analyzing business data and have become widely used in industry. Such systems primarily employ multidimensional data models to structure their data. However, current multidimensional data models fall short in their abilities to model the complex data found in some real-world application domains. The paper presents nine requirements to multidimensional data models, each of which is exemplified by a real-world, clinical case study. A survey of the existing models reveals that the requirements not currently met include support for many-to-many relationships between facts and dimensions, built-in support for handling change and time, and support for uncertainty as well as different levels of granularity in the data. The paper defines an extended multidimensional data model, and an associated algebra, which address all nine requirements.
: Data analysis applications typically aggregate data across many dimensions looking for unusual patterns. The SQL aggregate functions and the GROUP BY operator produce zero-dimensional or one-dimensional answers. Applications need the N-dimensional generalization of these operators. This paper defines that operator, called the data cube or simply cube. The cube operator generalizes the histogram, cross-tabulation, roll-up, drill-down, and sub-total constructs found in most report writers. The cube treats each of the N aggregation attributes as a dimension of N-space. The aggregate of a particular set of attribute values is a point in this space. The set of points forms an N-dimensional cube. Super-aggregates are computed by aggregating the N-cube to lower dimensional spaces. Aggregation points are represented by an "infinite value", ALL. For example, the point (ALL,ALL,ALL,...,ALL, sum(*)) would represent the global sum of all items. Each ALL value actually represents the set of...
Decision support applications involve complex queries on very large databases. Since response times should be small, query optimization is critical. Users typically view the data as multidimensional data cubes. Each cell of the data cube is a view consisting of an aggregation of interest, like total sales. The values of many of these cells are dependent on the values of other cells in the data cube. A common and powerful query optimization technique is to materialize some or all of these cells rather than compute them from raw data each time. Commercial systems differ mainly in their approach to materializing the data cube. In this paper, we investigate the issue of which cells (views) to materialize when it is too expensive to materialize all views. A lattice framework is used to express dependencies among views. We then present greedy algorithms that work off this lattice and determine a good set of views to materialize. The greedy algorithm performs within a small constant...
We propose a data model and a few algebraic operations that provide semantic foundation to multidimensional databases. The distinguishing feature of the proposed model is the symmetric treatment not only of all dimensions but also measures. The model provides support for multiple hierarchies along each dimension and support for adhoc aggregates. The proposed operators are composable, reorderable, and closed in application. These operators are also minimal in the sense that none can be expressed in terms of others nor can any one be dropped without sacrificing functionality. They make possible the declarative specification and optimization of multidimensional database queries that are currently specified operationally. The operators have been designed to be translated to SQL and can be implemented either on top of a relational database system or within a special purpose multidimensional database engine. In effect, they provide an algebraic application programming interface (API) that al...
In this manuscript we present the mathematical aggregation operators and their application to the video querying. This work is divided in three parts. The first one offers the definition of mathematical aggregation operators and some properties, followed by an extensive overview of the existing operators. The second part is dedicated to the study of the aggregation under uncertainty. We present a deep study on t-norms and t-conorms, pursued by a study on aggregation of truth and falsity values in non-phrase calculus way. We also introduce a non-axiomatic way, based on the metaphor of a balance, which in the one hand allows the visualization of the global behavior and of the sensitivity of an operator and in the other hand offers a guide for the construction of additive generated operators. The third part is devoted to the illustration of the theoretical results in the framework of video querying. We expound two complementary approaches. The first one based on "computing with words" explains how to browse a video with temporal queries. The second one makes obvious how to aggregate criteria pointing to the same conclusion. We prove the feasibility of the approach with real search engine and we expound the used technology (Java, XML, etc.) Keywords : Aggregation Multimedia Fuzzy logic Data Fusion Video Truth and Falsity Mathematical Operators Query Balance Metaphor 7 Rsum Dans ce manuscrit nous prsentons les oprateurs mathmatiques ddis l'agrgation et leur application la recherche d'information dans la vido. Cet ouvrage est divise en trois parties. La premire prsente d'abord la dfinition d'un oprateur mathmatique d'agrgation accompagne de quelques proprits souhaitables. Ensuite suit une vue d'ensemble des oprateurs existants. La deuxime partie est ddie l'investigati...
. In this paper we present MD, a logical model for OLAP systems, and show how it can be used in the design of multidimensional databases. Unlike other models for multidimensional databases, MD is independent of any specific implementation (relational or proprietary multidimensional) and as such it provides a clear separation between practical and conceptual aspects. In this framework, we present a design methodology, to obtain an MD scheme from an operational database. We then show how an MD database can be implemented, describing translations into relational tables and into multidimensional arrays. 1 Introduction An enterprise can achieve a great competitive advantage from the analysis of its historical data. For instance, the identification of unusual trends in sales can suggest opportunities for new business, whereas the analysis of past consumer demand can be useful for forecasting production needs. A data warehouse is an integrated collection of enterprise-wide data, ori...
A database application, called "on-line analytical processing" (or OLAP) and aimed at providing business intelligence through on-line multidimensional data analysis, has become increasingly important due to the existence of huge amounts of on-line data. This paper formalizes a multidimensional data (MDD) model for OLAP, and develops an algebraic query language called grouping algebra. The basic component of the MDD model is a multidimensional cube, consisting of a number of relations (called dimensions) and for each combination of tuples (called a coordinate), one from each dimension, there is an associated data value. Each dimension is viewed as a basic grouping, i.e., each tuple in the dimension corresponds to the group consisting of all the coordinates that contain this tuple. In order to express user queries, relational algebra expressions are then extended to those on basic groupings for obtaining complex groupings, including orderoriented groupings (for expressing, e.g., cumula...
De l’olap mining au f-olap mining
- A Laurent
Flexible unary multidimensional queries and their combinations
- A Laurent
- B Bouchon-Meunier
- A Doucet
Coopération entre un système d’extraction de connaissances floues et un système de gestion de bases de données multidimensionnelles
- A Laurent
- S Gançarski
- C Marsala