78 research outputs found
Treatment of imprecision in data repositories with the aid of KNOLAP
Traditional data repositories introduced for the needs of business
processing, typically focus on the storage and querying of crisp
domains of data. As a result, current commercial data repositories
have no facilities for either storing or querying imprecise/
approximate data.
No significant attempt has been made for a generic and applicationindependent
representation of value imprecision mainly as a
property of axes of analysis and also as part of dynamic
environment, where potential users may wish to define their âownâ
axes of analysis for querying either precise or imprecise facts. In
such cases, measured values and facts are characterised by
descriptive values drawn from a number of dimensions, whereas
values of a dimension are organised as hierarchical levels.
A solution named H-IFS is presented that allows the representation
of flexible hierarchies as part of the dimension structures. An
extended multidimensional model named IF-Cube is put forward,
which allows the representation of imprecision in facts and
dimensions and answering of queries based on imprecise
hierarchical preferences. Based on the H-IFS and IF-Cube
concepts, a post relational OLAP environment is delivered, the
implementation of which is DBMS independent and its performance
solely dependent on the underlying DBMS engine
A Flexible Approach to the Multidimensional Model: The Fuzzy Datacube
As a result of the use of OLAP technology in new fields of
knowledge and the merge of data from different sources, it has become
necessary for models to support this technology. In this paper, we propose a
new multidimensional model that can manage imprecision both in dimensions
and facts. Consequently, the multidimensional structure is 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
Towards development of fuzzy spatial datacubes : fundamental concepts with example for multidimensional coastal erosion risk assessment and representation
Les systĂšmes actuels de base de donnĂ©es gĂ©odĂ©cisionnels (GeoBI) ne tiennent gĂ©nĂ©ralement pas compte de l'incertitude liĂ©e Ă l'imprĂ©cision et le flou des objets; ils supposent que les objets ont une sĂ©mantique, une gĂ©omĂ©trie et une temporalitĂ© bien dĂ©finies et prĂ©cises. Un exemple de cela est la reprĂ©sentation des zones Ă risque par des polygones avec des limites bien dĂ©finies. Ces polygones sont crĂ©Ă©s en utilisant des agrĂ©gations d'un ensemble d'unitĂ©s spatiales dĂ©finies sur soit des intĂ©rĂȘts des organismes responsables ou les divisions de recensement national. MalgrĂ© la variation spatio-temporelle des multiples critĂšres impliquĂ©s dans lâanalyse du risque, chaque polygone a une valeur unique de risque attribuĂ© de façon homogĂšne sur l'Ă©tendue du territoire. En rĂ©alitĂ©, la valeur du risque change progressivement d'un polygone Ă l'autre. Le passage d'une zone Ă l'autre n'est donc pas bien reprĂ©sentĂ© avec les modĂšles dâobjets bien dĂ©finis (crisp). Cette thĂšse propose des concepts fondamentaux pour le dĂ©veloppement d'une approche combinant le paradigme GeoBI et le concept flou de considĂ©rer la prĂ©sence de lâincertitude spatiale dans la reprĂ©sentation des zones Ă risque. En fin de compte, nous supposons cela devrait amĂ©liorer lâanalyse du risque. Pour ce faire, un cadre conceptuel est dĂ©veloppĂ© pour crĂ©er un model conceptuel dâune base de donnĂ©e multidimensionnelle avec une application pour lâanalyse du risque dâĂ©rosion cĂŽtier. Ensuite, une approche de la reprĂ©sentation des risques fondĂ©e sur la logique floue est dĂ©veloppĂ©e pour traiter l'incertitude spatiale inhĂ©rente liĂ©e Ă l'imprĂ©cision et le flou des objets. Pour cela, les fonctions d'appartenance floues sont dĂ©finies en basant sur lâindice de vulnĂ©rabilitĂ© qui est un composant important du risque. Au lieu de dĂ©terminer les limites bien dĂ©finies entre les zones Ă risque, l'approche proposĂ©e permet une transition en douceur d'une zone Ă une autre. Les valeurs d'appartenance de plusieurs indicateurs sont ensuite agrĂ©gĂ©es basĂ©es sur la formule des risques et les rĂšgles SI-ALORS de la logique floue pour reprĂ©senter les zones Ă risque. Ensuite, les Ă©lĂ©ments clĂ©s d'un cube de donnĂ©es spatiales floues sont formalisĂ©s en combinant la thĂ©orie des ensembles flous et le paradigme de GeoBI. En plus, certains opĂ©rateurs d'agrĂ©gation spatiale floue sont prĂ©sentĂ©s. En rĂ©sumĂ©, la principale contribution de cette thĂšse se rĂ©fĂšre de la combinaison de la thĂ©orie des ensembles flous et le paradigme de GeoBI. Cela permet lâextraction de connaissances plus comprĂ©hensibles et appropriĂ©es avec le raisonnement humain Ă partir de donnĂ©es spatiales et non-spatiales. Pour ce faire, un cadre conceptuel a Ă©tĂ© proposĂ© sur la base de paradigme GĂ©oBI afin de dĂ©velopper un cube de donnĂ©es spatiale floue dans le system de Spatial Online Analytical Processing (SOLAP) pour Ă©valuer le risque de l'Ă©rosion cĂŽtiĂšre. Cela nĂ©cessite d'abord d'Ă©laborer un cadre pour concevoir le modĂšle conceptuel basĂ© sur les paramĂštres de risque, d'autre part, de mettre en Ćuvre lâobjet spatial flou dans une base de donnĂ©es spatiales multidimensionnelle, puis l'agrĂ©gation des objets spatiaux flous pour envisager Ă la reprĂ©sentation multi-Ă©chelle des zones Ă risque. Pour valider l'approche proposĂ©e, elle est appliquĂ©e Ă la rĂ©gion Perce (Est du QuĂ©bec, Canada) comme une Ă©tude de cas.Current Geospatial Business Intelligence (GeoBI) systems typically do not take into account the uncertainty related to vagueness and fuzziness of objects; they assume that the objects have well-defined and exact semantics, geometry, and temporality. Representation of fuzzy zones by polygons with well-defined boundaries is an example of such approximation. This thesis uses an application in Coastal Erosion Risk Analysis (CERA) to illustrate the problems. CERA polygons are created using aggregations of a set of spatial units defined by either the stakeholdersâ interests or national census divisions. Despite spatiotemporal variation of the multiple criteria involved in estimating the extent of coastal erosion risk, each polygon typically has a unique value of risk attributed homogeneously across its spatial extent. In reality, risk value changes gradually within polygons and when going from one polygon to another. Therefore, the transition from one zone to another is not properly represented with crisp object models. The main objective of the present thesis is to develop a new approach combining GeoBI paradigm and fuzzy concept to consider the presence of the spatial uncertainty in the representation of risk zones. Ultimately, we assume this should improve coastal erosion risk assessment. To do so, a comprehensive GeoBI-based conceptual framework is developed with an application for Coastal Erosion Risk Assessment (CERA). Then, a fuzzy-based risk representation approach is developed to handle the inherent spatial uncertainty related to vagueness and fuzziness of objects. Fuzzy membership functions are defined by an expert-based vulnerability index. Instead of determining well-defined boundaries between risk zones, the proposed approach permits a smooth transition from one zone to another. The membership values of multiple indicators (e.g. slop and elevation of region under study, infrastructures, houses, hydrology network and so on) are then aggregated based on risk formula and Fuzzy IF-THEN rules to represent risk zones. Also, the key elements of a fuzzy spatial datacube are formally defined by combining fuzzy set theory and GeoBI paradigm. In this regard, some operators of fuzzy spatial aggregation are also formally defined. The main contribution of this study is combining fuzzy set theory and GeoBI. This makes spatial knowledge discovery more understandable with human reasoning and perception. Hence, an analytical conceptual framework was proposed based on GeoBI paradigm to develop a fuzzy spatial datacube within Spatial Online Analytical Processing (SOLAP) to assess coastal erosion risk. This necessitates developing a framework to design a conceptual model based on risk parameters, implementing fuzzy spatial objects in a spatial multi-dimensional database, and aggregating fuzzy spatial objects to deal with multi-scale representation of risk zones. To validate the proposed approach, it is applied to Perce region (Eastern Quebec, Canada) as a case study
A Decathlon in Multidimensional Modeling: Open Issues and Some Solutions
The concept of multidimensional modeling has proven extremely successful in the area of Online Analytical Processing (OLAP) as one of many applications running on top of a data warehouse installation. Although many different modeling techniques expressed in extended multidimensional data models were proposed in the recent past, we feel that many hot issues are not properly reflected. In this paper we address ten common problems reaching from defects within dimensional structures over multidimensional structures to new analytical requirements and more
Integrating Fuzzy Concepts to Design a Fuzzy Data Warehouse
In this study, we attempt to design a fuzzy data warehouse. The classification elements to design the concerned fuzzy data warehouse are presented through the following tasks: identification of the target attribute, the linguistic terms, attribute of class membership, definition of the functions of membership, degree of attribute membership, definition of table of fuzzy classification and fuzzy membership. From all the above tasks, we present a method allowing to design a fuzzy data warehouse data. What enables us to design, starting from a traditional data warehouse, a fuzzy data warehouse, which takes into account, inaccuracy and other uncertainties inherent in the digital data constituting the data warehouse. The Fuzzy logic enables us to deal with this type of data without affecting the base of this data warehouse
A practical framework for assessing business intelligence competencies of enterprise systems using fuzzy ANP approach
As traditional concept in management, decision support had a remarkable
role in competitiveness or survival of organisations and following, as modern impression,
nowadays business intelligence (BI) has various applications in achieving desirable
decision supports. Consequently, assessing BI competencies of enterprise systems can
enable decision support in firms. This paper presents a practical framework for
assessing the business intelligence capabilities of enterprise systems based on a set of
novel factors and utilising fuzzy analytic network process (FANP). Through this, the
construct of BI competency is decomposed into three main competency parts including
âmanagerialâ, âtechnicalâ and âsystem enablerâ sub-goals, five main factors and 26
criteria. Using this framework, the BI competency level of enterprise systems can be
determined which can help the decision makers to select the enterprise system that best
suits organisationsâ intelligence decision support needs. In order to validate the proposed
model, it is applied to a real Iranian international offshore engineering and construction
company in the oil industry to select and acquire ERP system. This research provides a
complete frame (factors, criteria and procedures) for firms to assess their proposed
software and systems in the field of BI competencies and functions
Developing a model and a language to identify and specify the integrity constraints in spatial datacubes
La qualité des données dans les cubes de données spatiales est importante étant donné que ces données sont utilisées comme base pour la prise de décision dans les grandes organisations. En effet, une mauvaise qualité de données dans ces cubes pourrait nous conduire à une mauvaise prise de décision. Les contraintes d'intégrité jouent un rÎle clé pour améliorer la cohérence logique de toute base de données, l'un des principaux éléments de la qualité des données. Différents modÚles de cubes de données spatiales ont été proposés ces derniÚres années mais aucun n'inclut explicitement les contraintes d'intégrité. En conséquence, les contraintes d'intégrité de cubes de données spatiales sont traitées de façon non-systématique, pragmatique, ce qui rend inefficace le processus de vérification de la cohérence des données dans les cubes de données spatiales. Cette thÚse fournit un cadre théorique pour identifier les contraintes d'intégrité dans les cubes de données spatiales ainsi qu'un langage formel pour les spécifier. Pour ce faire, nous avons d'abord proposé un modÚle formel pour les cubes de données spatiales qui en décrit les différentes composantes. En nous basant sur ce modÚle, nous avons ensuite identifié et catégorisé les différents types de contraintes d'intégrité dans les cubes de données spatiales. En outre, puisque les cubes de données spatiales contiennent typiquement à la fois des données spatiales et temporelles, nous avons proposé une classification des contraintes d'intégrité des bases de données traitant de l'espace et du temps. Ensuite, nous avons présenté un langage formel pour spécifier les contraintes d'intégrité des cubes de données spatiales. Ce langage est basé sur un langage naturel contrÎlé et hybride avec des pictogrammes. Plusieurs exemples de contraintes d'intégrité des cubes de données spatiales sont définis en utilisant ce langage. Les designers de cubes de données spatiales (analystes) peuvent utiliser le cadre proposé pour identifier les contraintes d'intégrité et les spécifier au stade de la conception des cubes de données spatiales. D'autre part, le langage formel proposé pour spécifier des contraintes d'intégrité est proche de la façon dont les utilisateurs finaux expriment leurs contraintes d'intégrité. Par conséquent, en utilisant ce langage, les utilisateurs finaux peuvent vérifier et valider les contraintes d'intégrité définies par l'analyste au stade de la conception
Data Sharing in P2P Systems
To appear in Springer's "Handbook of P2P Networking"In this chapter, we survey P2P data sharing systems. All along, we focus on the evolution from simple file-sharing systems, with limited functionalities, to Peer Data Management Systems (PDMS) that support advanced applications with more sophisticated data management techniques. Advanced P2P applications are dealing with semantically rich data (e.g. XML documents, relational tables), using a high-level SQL-like query language. We start our survey with an overview over the existing P2P network architectures, and the associated routing protocols. Then, we discuss data indexing techniques based on their distribution degree and the semantics they can capture from the underlying data. We also discuss schema management techniques which allow integrating heterogeneous data. We conclude by discussing the techniques proposed for processing complex queries (e.g. range and join queries). Complex query facilities are necessary for advanced applications which require a high level of search expressiveness. This last part shows the lack of querying techniques that allow for an approximate query answering
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