Analysing imperfect temporal information in GIS using the Triangular Model

Rough set and fuzzy set are two frequently used approaches for modelling and reasoning about imperfect time intervals. In this paper, we focus on imperfect time intervals that can be modelled by rough sets and use an innovative graphic model [i.e. the triangular model (TM)] to represent this kind of imperfect time intervals. This work shows that TM is potentially advantageous in visualizing and querying imperfect time intervals, and its analytical power can be better exploited when it is implemented in a computer application with graphical user interfaces and interactive functions. Moreover, a probabilistic framework is proposed to handle the uncertainty issues in temporal queries. We use a case study to illustrate how the unique insights gained by TM can assist a geographical information system for exploratory spatio-temporal analysis

Research on Innovation and Strategic Risk Management in Manufacturing Firms

Chinese manufacturing companies in Bangladesh are committed to achieving optimal investment policy for investors in different industries. The purpose of this research is the strategic risk assessment; where the research approach involves collecting qualitative data through questionnaire survey and compute variables with programmed Rough Set Theory. Researchers have identified a set of key internal and external strategic uncertainties and also accessed the most important attributes from strategic risks. Here, Sector regulation, changing the tax law and organizational governance as the most degree of risk factors in strategic risk analysis. Overall, the focus of our research is to identify strategic risk attributes and proposed a risk assessment framework by demonstrating empirical study analysis of specific industries in Bangladesh those are directly invested by Chinese investors

Review and prioritization of investment projects in the Waste Management organization of Tabriz Municipality with a Rough Sets Theory approach

Purpose: Prioritization of investment projects is a key step in the process of planning the investment activities of organizations. Choosing the suitable projects has a direct impact on the profitability and other strategic goals of organizations. Factors affecting the prioritization of investment projects are complex and the use of traditional methods alone cannot be useful, so there is a need to use a suitable model for prioritizing projects and investment plans. The purpose of this study is to prioritize projects and investment methods for projects (10 projects) considered by the Waste Management Organization of Tabriz Municipality. Methodology: The method of analysis used is the theory of rough, so that first the important investment projects in the field of waste management were determined using the research background and opinion of experts and the weight and priority of the projects were obtained using the Rough Sets Theory. Then, the priority of appropriate investment methods (out of 6 methods) of each project was obtained using Rough numbers, the opinion of experts and other aspects. Findings: The result of the research has been that construction project of a specialized recycling town, plastic recycling project, and recycled tire recycling project are three priority projects of Tabriz Municipality Waste Management Organization, respectively. Three investment methods, civil partnership agreements, BOT, and BOO can be used for them. Originality/Value: Tabriz Municipality Waste Management is an important and influential organization in the activities of the city, in which the investment methods in its projects are mostly based on common contracts and are performed in the same way for all projects. This research offers new methods for projects and their diversity according to Rough Sets technique

Variable Precision Rough Set Model for Incomplete Information Systems and Its Beta-Reducts

As the original rough set model is quite sensitive to noisy data, Ziarko proposed the variable precision rough set (VPRS) model to deal with noisy data and uncertain information. This model allowed for some degree of uncertainty and misclassification in the mining process. In this paper, the variable precision rough set model for an incomplete information system is proposed by combining the VPRS model and incomplete information system, and the beta-lower and beta-upper approximations are defined. Considering that classical VPRS model lacks a feasible method to determine the precision parameter beta when calculating the beta-reducts, we present an approach to determine the parameter beta. Then, by calculating discernibility matrix and discernibility functions based on beta-lower approximation, the beta-reducts and the generalized decision rules are obtained. Finally, a concrete example is given to explain the validity and practicability of beta-reducts which is proposed in this paper

Towards development of fuzzy spatial datacubes : fundamental concepts with example for multidimensional coastal erosion risk assessment and representation

Les systèmes actuels de base de données géodécisionnels (GeoBI) ne tiennent généralement pas compte de l'incertitude liée à l'imprécision et le flou des objets; ils supposent que les objets ont une sémantique, une géométrie et une temporalité bien définies et précises. Un exemple de cela est la représentation des zones à risque par des polygones avec des limites bien définies. Ces polygones sont créés en utilisant des agrégations d'un ensemble d'unités spatiales définies sur soit des intérêts des organismes responsables ou les divisions de recensement national. Malgré la variation spatio-temporelle des multiples critères impliqués dans l’analyse du risque, chaque polygone a une valeur unique de risque attribué de façon homogène sur l'étendue du territoire. En réalité, la valeur du risque change progressivement d'un polygone à l'autre. Le passage d'une zone à l'autre n'est donc pas bien représenté avec les modèles d’objets bien définis (crisp). Cette thèse propose des concepts fondamentaux pour le développement d'une approche combinant le paradigme GeoBI et le concept flou de considérer la présence de l’incertitude spatiale dans la représentation des zones à risque. En fin de compte, nous supposons cela devrait améliorer l’analyse du risque. Pour ce faire, un cadre conceptuel est développé pour créer un model conceptuel d’une base de donnée multidimensionnelle avec une application pour l’analyse du risque d’érosion côtier. Ensuite, une approche de la représentation des risques fondée sur la logique floue est développée pour traiter l'incertitude spatiale inhérente liée à l'imprécision et le flou des objets. Pour cela, les fonctions d'appartenance floues sont définies en basant sur l’indice de vulnérabilité qui est un composant important du risque. Au lieu de déterminer les limites bien définies entre les zones à risque, l'approche proposée permet une transition en douceur d'une zone à une autre. Les valeurs d'appartenance de plusieurs indicateurs sont ensuite agrégées basées sur la formule des risques et les règles SI-ALORS de la logique floue pour représenter les zones à risque. Ensuite, les éléments clés d'un cube de données spatiales floues sont formalisés en combinant la théorie des ensembles flous et le paradigme de GeoBI. En plus, certains opérateurs d'agrégation spatiale floue sont présentés. En résumé, la principale contribution de cette thèse se réfère de la combinaison de la théorie des ensembles flous et le paradigme de GeoBI. Cela permet l’extraction de connaissances plus compréhensibles et appropriées avec le raisonnement humain à partir de données spatiales et non-spatiales. Pour ce faire, un cadre conceptuel a été proposé sur la base de paradigme GéoBI afin de développer un cube de données spatiale floue dans le system de Spatial Online Analytical Processing (SOLAP) pour évaluer le risque de l'érosion côtière. Cela nécessite d'abord d'élaborer un cadre pour concevoir le modèle conceptuel basé sur les paramètres de risque, d'autre part, de mettre en œuvre l’objet spatial flou dans une base de données spatiales multidimensionnelle, puis l'agrégation des objets spatiaux flous pour envisager à la représentation multi-échelle des zones à risque. Pour valider l'approche proposée, elle est appliquée à la région Perce (Est du Québec, Canada) comme une étude 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 stakeholders’ 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

What If People Learn Requirements Over Time? A Rough Introduction to Requirements Economics

The overall objective of Requirements Engineering is to specify, in a systematic way, a system that satisfies the expectations of its stakeholders. Despite tremendous effort in the field, recent studies demonstrate this is objective is not always achieved. In this paper, we discuss one particularly challenging factor to Requirements Engineering projects, namely the change of requirements. We proposes a rough discussion of how learning and time explain requirements changes, how it can be introduced as a key variable in the formulation of the Requirements Engineering Problem, and how this induces costs for a requirements engineering project. This leads to a new discipline of requirements economics

Study on the Rough-set-based Clustering Algorithm for Sensor Networks

The traditional clustering algorithm is a very typical level routing algorithm in wireless sensor networks (WSN). On the basis of the classical LEACH (Low Energy Adaptive Clustering Hierarchy) algorithm, this paper proposes an energy efficient clustering algorithm in WSN. Through the introduction of rough set, the new algorithm mainly introduces how to confirm an optimized strategy to choose the cluster head effectively by the simplified decision table. That is to say, by discrete normalized data preprocessing of attribute value, getting discretization decision table. Finally, the results from simulated experiments show that the clustering algorithm based on rough set theory can optimize the clustering algorithm in network data. That is to say, the rough-set-based clustering algorithm can effectively choose the cluster head, balance the energy of the nodes in the cluster and prolong the lifetime of sensor networks