Geospatial Business Intelligence Research Articles

Towards Semantic Geo/BI: A Novel Approach for Semantically Enriching Geo/BI Data with OWL Ontological Layers (OOLAP and ODW) to Enable Semantic Exploration, Analysis and Discovery of Geospatial Business Intelligence Knowledge

Towards Semantic Geo/BI: A Novel Approach for Semantically Enriching Geo/BI Data with OWL Ontological Layers (OOLAP and ODW) to Enable Semantic Exploration, Analysis and Discovery of Geospatial Business Intelligence Knowledge

A new integrated methodology using modified Delphi-fuzzy AHP-PROMETHEE for Geospatial Business Intelligence selection

Geospatial Business Intelligence (Geospatial BI) is a system that combines multidimensional analysis and cartographic visualization. It plays an important role in decision making process for enterprises. Adopting such a comprehensive solution may result in the great investment decision for them, so great deal of attention should be given in the selection of the optimal system. As there are many impacting factors in the selection of Geospatial BI system, the same process is considered as a complex multi-criteria decision making problem. In this paper, we explore the application of an integrated methodology for the evaluation of various Geospatial BI alternatives. The proposed methodology integrates the three well-known decision-making techniques, namely Modified Delphi, fuzzy analytic hierarchical process (fuzzy-AHP), and preference ranking organization method for enrichment evaluations (PROMETHEE). In this respect, the modified Delphi is used to select the most impacting factors by a few decision-makers. The fuzzy-AHP is employed to analyze the structure of the problem and to obtain the weights of the qualitative and quantitative criteria, by incorporating the uncertainty values. Then, the PROMETHEE technique is used for optimal ranking of the alternative system choices. A step-by-step, numerical study is illustrated by using the proposed methodology on the decision making problem of a company that is faced to five Geospatial BI solutions. The results demonstrate that the proposed methodology can successfully accomplish our goal of this study.

An OWL-Based Mobile GeoBI Context Ontology Enabling Location-Based and Context-Based Reasoning and Supporting Contextual Business Analysis

Mobile professionals need to be assisted with suitable mobile GeoBI (Geospatial Business Intelligence) systems, which are able to capture, organize and structure the user’s reality into a relevant context model and reason on it. GeoBI context modelling and reasoning are still research issues since there is not yet either a model or a relevant taxonomy regarding GeoBI contextual information. To fill this gap, this paper proposes an extended and detailed OWL-based mobile GeoBI context ontology to provide context-aware applications and users with relevant contextual information and context-based reasoning capabilities. Context quality issues are handled an implementation architecture which is provided.

Context-based mobile GeoBI: enhancing business analysis with contextual metrics/statistics and context-based reasoning

Business professionals are increasingly mobile and should be supported by suitable mobile Decision Support Systems (DSS). In our previous work, we have established that such suitable mobile DSS should be (i) GeoBI(Geospatial Business Intelligence)-enabled and (ii) context-based, and have addressed issues regarding context characterization and context modeling. The present paper deals with mobile GeoBI context-based reasoning. Through realistic scenarios, it highlights (i) the requirement for context-based reasoning to enhance mobile GeoBI experience, (ii) the need for contextual metrics/statistics to help mobile business professionals discover their local context, (iii) the need for crossing business performance metrics with contextual metrics to help mobile business professionals in discovering the context hidden behind business performance figures, and proposes convenient solutions to tackle these needs.

Using geospatial business intelligence to support regional infrastructure governance

In many developed countries including Australia, infrastructure services at local and state levels are being provided by an increasing number of disjointed public and private agencies. There is an urgent need for an integrated view on the provision and use of these services for better governance and productivity. Developing an integrated view is challenging due to the dispersion of relevant data sets and the underlying complexity of increasingly interconnected infrastructure networks. Using a case study in New South Wales (Australia), we demonstrate how tools and processes in Geospatial Business Intelligence (Geo-BI) can be harnessed using a collective design approach to develop an integrated solution; the SMART Infrastructure Dashboard (SID). While providing a much needed planning and policy support tool for the local governance of infrastructure services, SID pushes the boundary of Geo-BI beyond its traditional use.

Using geospatial business intelligence paradigm to design a multidimensional conceptual model for efficient coastal erosion risk assessment

One of the main challenges in Coastal Erosion Risk Assessment (CERA) is integrating and analysis of conflicting data in various time periods and spatial scales through dissimilar environmental, social, and economic criteria. Currently, Geographical Information Systems (GIS) are widely used in risk assessment despite their drawbacks and limitations as transactional systems for multi-scales, multi-epochs, and multi-themes analysis. Hence, an analytical conceptual framework is proposed in this paper based on geospatial business intelligence paradigm to develop a Spatial Multidimensional Conceptual Model (SMCM) to assess coastal erosion risk. The model is designed based on Spatial On-Line Analytical Processing (SOLAP) platform, on the top of both analytical and transactional paradigms, to allow fast synthesis of cross-tabulated data and easy comparisons over space, scales, epochs, and themes. This objective is achieved through a comprehensive integration of multiple environmental, social, and economic criteria as well as their interactions at various scales. It also takes into account multiple elements at risk such as people, infrastructure, and built environment as different dimensions of analysis. Using this solution allows decision makers to benefit from on-demand, interactive, and comprehensive information in a way that is not possible using GIS alone. The developed model can easily be adapted for any other coastal region through the proposed framework to perform risk assessment. The advantages and drawbacks of the proposed framework are also discussed and new research perspectives are presented.

Mobile and Context-Aware GeoBI Applications: A Multilevel Model for Structuring and Sharing of Contextual Information

With the requirements for high performance results in the today’s mobile, global, highly competitive, and technology-based business world, business professionals have to get supported by convenient mobile decision support systems (DSS). To give an improved support to mobile business professionals, it is necessary to go further than just allowing a simple remote access to a Business Intelligence platform. In this paper, the need for actual context-aware mobile Geospatial Business Intelligence (GeoBI) systems that can help capture, filter, organize and structure the user mobile context is exposed and justified. Furthermore, since capturing, structuring, and modeling mobile contextual information is still a research issue, a wide inventory of existing research work on context and mobile context is provided. Then, step by step, we methodologically identify relevant contextual information to capture for mobility purposes as well as for BI needs, organize them into context-dimensions, and build a hierarchical mobile GeoBI context model which (1) is geo-spatial-extended, (2) fits with human perception of mobility, (3) takes into account the local context interactions and information-sharing with remote contexts, and (4) matches with the usual hierarchical aggregated structure of BI data.

Business Intelligence a supporto del GIS: ultime novità dal mondo GFOSS

GeoBI (GeoSpatial Business Intelligence) open source project GeoBI (GeoSpatial Business Intelligence), a new open source project coming from Italy, look at the Geographic Information Systems (GIS) from a different point of view because it proposes to manage the alphanumeric component of cartographic data through Business Intelligence tools. This implies multidimensional webGIS usage in order to display charts, dials and diagrams on the map and spatially navigate OLAP cubes.