A Geospatial Decision Support System Tool for Supporting Integrated Forest Knowledge at the Landscape Scale

Forests are part of a complex landscape mosaic and play a crucial role for people living both in rural and urbanized spaces. Recent progresses in modelling and Decision Support System (DSS) applied to the forestry sector promise to improve public participative forest management and decision-making in planning and conservation issues. However, most DSS are not open-source systems, being in many cases software designed for site-specific applications in forest ecosystems. Furthermore, some of these systems often miss challenging the integration of other land uses within the landscape matrix, which is a key issue in modern forestry planning aiming at linking recent developments in open-source Spatial-DSS systems to sectorial forest knowledge. This paper aims at demonstrating that a new type of S-DSS, developed within the Life+ project SOILCONSWEB over an open-source Geospatial Cyber-Infrastructure (GCI) platform, can provide a strategic web-based operational tool for forest resources management and multi-purpose planning. In order to perform simulation modelling, all accessible via the Web, the GCI platform supports acquisition and processing of both static and dynamic data (e.g., spatial distribution of soil and forest types, growing stock and yield), data visualization and computer on-the-fly applications. The DSS forestry tool has been applied to a forest area of 5,574 ha in the southern Apennines of Peninsular Italy, and it has been designed to address forest knowledge and management providing operational support to private forest owners and decision-makers involved in management of forest landscape at different levels. Such a geospatial S-DSS tool for supporting integrated forest knowledge at landscape represents a promising tool to implement sustainable forest management and planning. Results and output of the platform will be shown through a short selection of practical case studies

Heterogeneous data source integration for smart grid ecosystems based on metadata mining

The arrival of new technologies related to smart grids and the resulting ecosystem of applications andmanagement systems pose many new problems. The databases of the traditional grid and the variousinitiatives related to new technologies have given rise to many different management systems with several formats and different architectures. A heterogeneous data source integration system is necessary toupdate these systems for the new smart grid reality. Additionally, it is necessary to take advantage of theinformation smart grids provide. In this paper, the authors propose a heterogeneous data source integration based on IEC standards and metadata mining. Additionally, an automatic data mining framework isapplied to model the integrated information.Ministerio de Economía y Competitividad TEC2013-40767-

Design and implementation of a secure and user-friendly broker platform supporting the end-to-end provisioning of e-homecare services

We designed a broker platform for e-homecare services using web service technology. The broker allows efficient data communication and guarantees quality requirements such as security, availability and cost-efficiency by dynamic selection of services, minimizing user interactions and simplifying authentication through a single user sign-on. A prototype was implemented, with several e-homecare services (alarm, telemonitoring, audio diary and video-chat). It was evaluated by patients with diabetes and multiple sclerosis. The patients found that the start-up time and overhead imposed by the platform was satisfactory. Having all e-homecare services integrated into a single application, which required only one login, resulted in a high quality of experience for the patients

Principles and Concepts of Agent-Based Modelling for Developing Geospatial Simulations

The aim of this paper is to outline fundamental concepts and principles of the Agent-Based Modelling (ABM) paradigm, with particular reference to the development of geospatial simulations. The paper begins with a brief definition of modelling, followed by a classification of model types, and a comment regarding a shift (in certain circumstances) towards modelling systems at the individual-level. In particular, automata approaches (e.g. Cellular Automata, CA, and ABM) have been particularly popular, with ABM moving to the fore. A definition of agents and agent-based models is given; identifying their advantages and disadvantages, especially in relation to geospatial modelling. The potential use of agent-based models is discussed, and how-to instructions for developing an agent-based model are provided. Types of simulation / modelling systems available for ABM are defined, supplemented with criteria to consider before choosing a particular system for a modelling endeavour. Information pertaining to a selection of simulation / modelling systems (Swarm, MASON, Repast, StarLogo, NetLogo, OBEUS, AgentSheets and AnyLogic) is provided, categorised by their licensing policy (open source, shareware / freeware and proprietary systems). The evaluation (i.e. verification, calibration, validation and analysis) of agent-based models and their output is examined, and noteworthy applications are discussed.Geographical Information Systems (GIS) are a particularly useful medium for representing model input and output of a geospatial nature. However, GIS are not well suited to dynamic modelling (e.g. ABM). In particular, problems of representing time and change within GIS are highlighted. Consequently, this paper explores the opportunity of linking (through coupling or integration / embedding) a GIS with a simulation / modelling system purposely built, and therefore better suited to supporting the requirements of ABM. This paper concludes with a synthesis of the discussion that has proceeded. The aim of this paper is to outline fundamental concepts and principles of the Agent-Based Modelling (ABM) paradigm, with particular reference to the development of geospatial simulations. The paper begins with a brief definition of modelling, followed by a classification of model types, and a comment regarding a shift (in certain circumstances) towards modelling systems at the individual-level. In particular, automata approaches (e.g. Cellular Automata, CA, and ABM) have been particularly popular, with ABM moving to the fore. A definition of agents and agent-based models is given; identifying their advantages and disadvantages, especially in relation to geospatial modelling. The potential use of agent-based models is discussed, and how-to instructions for developing an agent-based model are provided. Types of simulation / modelling systems available for ABM are defined, supplemented with criteria to consider before choosing a particular system for a modelling endeavour. Information pertaining to a selection of simulation / modelling systems (Swarm, MASON, Repast, StarLogo, NetLogo, OBEUS, AgentSheets and AnyLogic) is provided, categorised by their licensing policy (open source, shareware / freeware and proprietary systems). The evaluation (i.e. verification, calibration, validation and analysis) of agent-based models and their output is examined, and noteworthy applications are discussed.Geographical Information Systems (GIS) are a particularly useful medium for representing model input and output of a geospatial nature. However, GIS are not well suited to dynamic modelling (e.g. ABM). In particular, problems of representing time and change within GIS are highlighted. Consequently, this paper explores the opportunity of linking (through coupling or integration / embedding) a GIS with a simulation / modelling system purposely built, and therefore better suited to supporting the requirements of ABM. This paper concludes with a synthesis of the discussion that has proceeded

Active Ontology: An Information Integration Approach for Dynamic Information Sources

In this paper we describe an ontology-based information integration approach that is suitable for highly dynamic distributed information sources, such as those available in Grid systems. The main challenges addressed are: 1) information changes frequently and information requests have to be answered quickly in order to provide up-to-date information; and 2) the most suitable information sources have to be selected from a set of different distributed ones that can provide the information needed. To deal with the first challenge we use an information cache that works with an update-on-demand policy. To deal with the second we add an information source selection step to the usual architecture used for ontology-based information integration. To illustrate our approach, we have developed an information service that aggregates metadata available in hundreds of information services of the EGEE Grid infrastructure