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An important research trend in databases is to handle different types of uncertainty at both conceptual and logical levels for various non-traditional applications that may involve imprecision and uncertainty that have been difficult to integrate cohesively in simple database models. In this study we describe how to conceptually model complex and u...
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In this paper a novel conceptual data warehouse (DW) data model, the Conceptual Data Vault (C-DV) Model is presented. The C-DV extends logical and physical Data Vault (DV) Models, to enable conceptual modeling of source data worthy of preserving in a DW staging area as a System of Records. The advantages of DV model, namely flexibility of evolving...
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... In this discussion, we motivate our research and answer to our last research question RQ4, which has to do with the benefits of proposing a conceptual model for Geo-OEDV, trying to identify the most relevant stakeholders of these systems. Conceptual Modeling has been previously used in GIS-related problems [67] also applied to specific fields (e.g., archaeology [68] or management of energy production [69]) but, to the best of our knowledge, this is a novelty for pandemic cases. With this formalism, we stress the importance of understanding in depth the tools that are currently driving the communication to the broad public of the pandemic, in its diffusion and implications. ...
Responding to the recent COVID-19 outbreak, several organizations and private citizens considered the opportunity to design and publish online explanatory data visualization tools for the communication of disease data supported by a spatial dimension. They responded to the need of receiving instant information arising from the broad research community, the public health authorities, and the general public. In addition, the growing maturity of information and mapping technologies, as well as of social networks, has greatly supported the diffusion of web-based dashboards and infographics, blending geographical, graphical, and statistical representation approaches. We propose a broad conceptualization of Web visualization tools for geo-spatial information, exceptionally employed to communicate the current pandemic; to this end, we study a significant number of publicly available platforms that track, visualize, and communicate indicators related to COVID-19. Our methodology is based on (i) a preliminary systematization of actors, data types, providers, and visualization tools, and on (ii) the creation of a rich collection of relevant sites clustered according to significant parameters. Ultimately, the contribution of this work includes a critical analysis of collected evidence and an extensive modeling effort of Geo-Online Exploratory Data Visualization (Geo-OEDV) tools, synthesized in terms of an Entity-Relationship schema. The COVID-19 pandemic outbreak has offered a significant case to study how and how much modern public communication needs spatially related data and effective implementation of tools whose inspection can impact decision-making at different levels. Our resulting model will allow several stakeholders (general users, policy-makers, and researchers/analysts) to gain awareness on the assets of structured online communication and resource owners to direct future development of these important tools.
... Conceptual Modeling of GIS Applications [19] is an important research area to cater for different types of uncertainties in both conceptual and logical levels. The authors propose a method to include imprecision and uncertainty in database models. ...
Design Patterns are recurring solutions to common design problems in a well-defined context and conflicting forces. These are documented best practices which can reduce the complexity of design process of a software system by introducing reusability and maintainability in the design. The aim of this paper is to describe the applicability of design patterns representing well- defined design problem in GIS domain. In this paper, we use GIS map display system to define context of various design problems and system of forces associated with different subsystems.
... Conceptual models apply quasi-natural language or flowcharts to draw the components of the system under investigation and emphasize the linkages between them. This modelling approach is therefore an abstraction process that uses logical concepts, while hiding the details of implementation and data storage (Adnan Yazici and Akkaya, 2000). Conceptual models have been used to structure geographic phenomena and evaluate the relevance of existing rules for data and formats leading to a conceptualisation of the real world, through which it is possible to develop tools to merge heterogeneous datasets. ...
... Applications of GIS in conceptual models can produce fuzzy, null output or incomplete information (such as lack of georeference information in some of locations) due to quantifiers of the spatial domain that might be difficult to manage as a result of certain degrees of uncertainty (Ferrè et al., 2014). So uncertainty should be considered for a powerful conceptual model that can be dealt with through explicit definition of the object identifier (Adnan Yazici and Akkaya, 2000). In addition, simplicity and orthogonality are important criteria for developing a powerful and intuitive conceptual model (Parent et al., 1999). ...
... To represent the semantic uncertainty, typically found for conceptual definitions such as "forest," we use the idea of rough fuzzy sets (Dubois and Prade, 1990) in which combine an explicit representation of both indiscernibility and vagueness of information. There are many prior examples of research describing fuzzy approaches to conceptual models for GIS and remote sensing (Usery, 1996;Cross and Firat, 2000;Yazici and Akkaya, 2000;Morris, 2003), and a recent overview of fuzzy set theory applications in GIS can be found in Robinson (2003). Moreover, rough sets and the concept of rough classification have demonstrated promising applications for handling uncertainty related to the granularity of geographic information (Schneider, 1995;Worboys, 1998;Ahlqvist et al., 2000). ...
We present a rationale and method for representing the vagueness in taxonomic class definitions in cases where classes are described by a set of characteristics, such as those sometimes used as the basis for land-cover category discrimination. We further describe methods to estimate the semantic similarity between any two classes by calculating semantic similitude metrics based on such parameterized class definitions. Our working hypothesis is that a large similitude would predict categories that will be more prone to confusion and hence image or map misclassification. We use two different existing data sets to demonstrate and evaluate the method, and the results support our original hypothesis. Consequently, we argue that classification schemes that are based on parameterized definitions could be assessed for problematic categories during their construction using our approach, and thus, enabling the identification of a thematic vagueness component to supplement the more traditional statistical measures derived from the error matrix.
... Vagueness can be handled using fuzzy sets (Zadeh 1965), and there are several examples that describe fuzzy approaches to conceptual models for GIS (Usery 1996, Cross and Firat 2000, Yazici and Akkaya 2000, Morris 2003. A recent overview of fuzzy set theory applications in GIS can be found in Robinson (2003). ...
To support diversified uses of geographical information there is a need for enhanced spatial data infrastructures to create interoperability between users and producers of geographic data. One important interoperability problem is caused by differences in data semantics, for example heterogeneous land use/land cover classification systems. A critical review of an existing method for semantic mteroperability between land cover classifications is used to motivate and introduce a modified framework based on conceptual spaces and rough fuzzy sets. Land cover categories are defined by a set of defining attributes formally represented as a collection of rough fuzzy membership functions and importance weights. This parameterized representation is used to translate between the US Natural Vegetation Classification Standard and the European CORINE Land Cover system based on evaluations of different aspects of semantic similarity between categories. The results demonstrate that the use of different similarity metrics in a conceptual space, together with the explicit rough fuzzy uncertainty representation, increases the semantic separation between land cover categories. Diagrams and maps illustrate the information that can be gained from the semantic similarity assessment. These developments open new possibilities to explore semantic relationships between concepts, both within a classification and between classifications used in different contexts.
... A major consequence of this emerges as problems with schematic and semantic heterogeneity in multi-user environments ( Figure 3) (Bishr 1997, Ahlqvist 2000. Although a large literature addresses efforts to resolve these problems (Aslan and McLeod 1999), most tend not to account for imprecision (Yazici and Akkaya 2000). ...
... Two important aspects of semantic imprecision, vagueness, and indiscernibility were previously problematic from a representational viewpoint, but work on fuzzy (Zadeh 1965) and rough (Pawlak 1991) extensions of traditional set theory have provided viable techniques to handle those uncertainty types. There are several examples that describe fuzzy conceptual models for GIS (Usery 1996, Cross and Firat 2000, Yazici and Akkaya 2000, Morris 2003) and a recent overview of fuzzy set theory applications in GIS can be found in Robinson (2003). In addition, rough sets and the concept of rough classification have demonstrated promising applications for geographic information handling (Schneider 1995, Worboys 1998. ...
Abstract Most conceptual modeling in geographic information science to date has used a symbolic approach with little or no recognition of the semantic uncertainty often found in geographic concepts. This work describes a concept model based on parameterized concept descriptions that uses a spatial metaphor, the conceptual space, as an organizing structure ( Gärdenfors 2000). This cognitive theory of conceptual spaces is combined with a formal representation of semantic uncertainty based on rough fuzzy sets. The conceptual space then represents each concept as a collection of rough fuzzy property definitions with associated salience weights, where a property itself can be treated as a special case of a concept. Instead of explicitly defining concept hierarchies, we can allow different conceptual structures to emerge through measures of concept inclusion and similarity. A land use/land cover example demonstrates how the model represents concepts, concept similarity, hierarchical structures and the context dependence of concepts. The final section of the paper points to the need for further studies of context effects, concept similarity measures, and uncertainty representation using the proposed model.
... Fuzzy pattern matching is then adopted to define the query evaluation mechanism [7, 12]. The problem of representing imperfect spatial information in databases has have been faced by several authors [2, 3,131415161718. The novelty of our approach is the use of linguistic qualifiers to describe properties of spatial entities. Linguistic qualifiers are a habit in many disciplines and the ability to manage them directly can greatly improve the expressive power of the representation. ...
... The problem of analysing the nature of imperfection in spatial information has been pursued by many researchers [2, 3, 4,131415161718. Although there is an unanimous agreement in considering geographic information affected by different kinds of imperfection, up to date there is not a unique unambiguous vocabulary to identify the kinds of imperfection. ...
... These two models are inadequate for representing the real world spatial phenomena whose information is invariably affected by imperfection. For this reason some authors defined new conceptual models, others extended existing semantic models, mainly the Enhanced Entity-Relationship (EER) model, and the Object Oriented data base model [3,161718. In this section, by adopting a Fuzzy Object Oriented Database approach [11] we define new data types for representing the different kinds of imperfect spatial information identified in the previous section, specifically: − the In this way we introduce a layer above the two conceptual models of spatial data which is functional to linguistically represent the imperfection related to the spatial information (see figure I). ...
In this contribution a representation of imperfect spatial
information is proposed based on linguistic qualifiers within fuzzy set
theory and possibility theory. By adopting a fuzzy object oriented
database approach new data types for representing geographic entities
characterized by either unsharp boundaries or indeterminate boundaries
and relationships are defined. Finally, the evaluation mechanism of
vague topological constraints between fuzzy and indeterminate spatial
objects is described
With the increasing complexity of hydrological systems, hydrological modeling by modelers from different research areas has been regarded as an effective way to solve complex hydrological issues. As the first step of hydrological modeling, conceptual modeling plays an important role in supporting modeling idea communication among interdisciplinary modelers. Currently, conceptual modeling methods usually show the modeling ideas by using the block-based diagrams. However, further research need to be explored to express the spatial and temporal distribution of these modeling elements and their dynamic interaction relationships, thus promoting the communication of modeling ideas and reaching a common understanding of the modeling system among modelers. Visual conceptual scenario is the production of hydrological conceptual modeling and can be used to express modelers’ cognition of modeling system. This article proposes a construction method of visual conceptual scenario. The conceptual component that represents the modeling elements, the rules that constrain the scenario construction, and a dynamic interaction method that supports the visualization of dynamic hydrological process are designed. Finally, a study case of identifying the impact of energy base water project on the groundwater is designed to illustrate the feasibility and practicability of the proposed construction method of visual conceptual scenario.
Geospatial predictive models often require mapping of predefined concepts or categories with various conditioning factors in a given space. This chapter discusses various aspects of uncertainty in predictive modeling by characterizing different typologies of classification uncertainty. It argues that understanding uncertainty semantics is a perquisite for efficient handling and management of predictive models.
In this era of big data, as relational databases are inefficient, NoSQL databases are a workable solution for data storage. In this context, one of the key issues is the veracity and therefore the data quality. Indeed, as with classic data, geospatial big data are generally fuzzy even though they are stored as crisp data (perfect data). Hence, if data are geospatial and fuzzy, additional complexities appear because of the complex syntax and semantic features of such data. The NoSQL databases do not offer strict data consistency. Therefore, new challenges are needed to be overcome to develop efficient methods that simultaneously ensure the performance and the consistency in storing fuzzy geospatial big data. This paper presents a new methodology that tackles the storage issues and validates the fuzzy spatial entities' consistency in a document-based NoSQL system. Consequently, first, to better express the structure of fuzzy geospatial data in such a system, we present a logical model called Fuzzy GeoJSON schema. Second, for consistent storage, we implement a schema-driven pipeline based on the Fuzzy GeoJSON schema and semantic constraints.
