E-CITY KNOWWARE: KNOWLEDGE MIDDLEWARE FOR COORDINATED MANAGEMENT OF SUSTAINABLE CITIES

The realization of e-city is a necessary component for achieving the green city. This paper outlines a vision for an e-city platform that is based on knowledge brokerage in the green city. The proposed platform will be a venue for creating dynamic virtual organizations to harness collective intelligence of knowledge hubs to analyze and manage sustainability knowledge in urban areas. Knowledge assets of participating organizations will be presented in three dimensions: process structures, human profile and software systems. These three facets of knowledge will be accessible and viewable through a self-describing mechanism. Cities can post their geospatial and real-time data on the net. Relevant environmental and energy-use data will be extracted using topic maps and data extraction services. Local decision makers can synchronize work processes (from participating hubs) to create an integrated workflow for a new ad hoc virtual organization to collaboratively analyze the multifaceted nature of sustainable decision making. An e-city platform is envisioned in this paper that will be realized through intelligent, agent-like, domain-specific middleware (KnowWare). Through triangulation between people, software and processes, these KnowWare will discover, negotiate, integrate, reason and communicate knowledge (related to energy and environment) from across organizations to the right person at the right time. KnowWare is fundamentally, a portal of social semantic services that resides on a cloud computing infrastructure. Knowware exploits thee main tools: 1) existing ontologies to represent knowledge in a semantic manner, 2) topic maps to profile sources of knowledge and match these to the complex needs of sustainability analysis, 3) domain-specific middleware for knowledge integration and reasoning

Global Challenges and Innovative Technologies Geared Toward New Markets: Prospects for Virtual and Augmented Reality

AbstractEffective applied research is based on close collaboration between research and industry, which, taking the findings of basic research on customer demands as its starting point, creates new means to develop and market innovative products. What is more, growing demands for innovative and sustainable results of research and development are prompting the examination of global trends such as demographic change, growing megacities, rising energy consumption and increasing traffic and the resultant social challenges. These trends and increasing traffic in particular are giving rise to new fields of work, especially for digital technologies, as a social responsibility, e.g. on driver assistance and traffic control systems that increase safety. The social challenges are increasingly affecting markets and requiring new innovative products, efficient production processes and integrative forms of human resource development and training and qualification. The virtualization and digitization of objects and processes is becoming an enabler of the development of new strategies and concepts such as smart cities, green energy, electric vehicle networks, smart manufacturing and smart logistics.This paper examines means by which digital engineering and virtual and augmented reality technologies can support the creation of sustainable smart manufacturing and smart logistics processes as well as on-the-job training and qualification and knowledge transfe

The Federal Big Data Research and Development Strategic Plan

This document was developed through the contributions of the NITRD Big Data SSG members and staff. A special thanks and appreciation to the core team of editors, writers, and reviewers: Lida Beninson (NSF), Quincy Brown (NSF), Elizabeth Burrows (NSF), Dana Hunter (NSF), Craig Jolley (USAID), Meredith Lee (DHS), Nishal Mohan (NSF), Chloe Poston (NSF), Renata Rawlings-Goss (NSF), Carly Robinson (DOE Science), Alejandro Suarez (NSF), Martin Wiener (NSF), and Fen Zhao (NSF). A national Big Data1 innovation ecosystem is essential to enabling knowledge discovery from and confident action informed by the vast resource of new and diverse datasets that are rapidly becoming available in nearly every aspect of life. Big Data has the potential to radically improve the lives of all Americans. It is now possible to combine disparate, dynamic, and distributed datasets and enable everything from predicting the future behavior of complex systems to precise medical treatments, smart energy usage, and focused educational curricula. Government agency research and public-private partnerships, together with the education and training of future data scientists, will enable applications that directly benefit society and the economy of the Nation. To derive the greatest benefits from the many, rich sources of Big Data, the Administration announced a “Big Data Research and Development Initiative” on March 29, 2012.2 Dr. John P. Holdren, Assistant to the President for Science and Technology and Director of the Office of Science and Technology Policy, stated that the initiative “promises to transform our ability to use Big Data for scientific discovery, environmental and biomedical research, education, and national security.” The Federal Big Data Research and Development Strategic Plan (Plan) builds upon the promise and excitement of the myriad applications enabled by Big Data with the objective of guiding Federal agencies as they develop and expand their individual mission-driven programs and investments related to Big Data. The Plan is based on inputs from a series of Federal agency and public activities, and a shared vision: We envision a Big Data innovation ecosystem in which the ability to analyze, extract information from, and make decisions and discoveries based upon large, diverse, and real-time datasets enables new capabilities for Federal agencies and the Nation at large; accelerates the process of scientific discovery and innovation; leads to new fields of research and new areas of inquiry that would otherwise be impossible; educates the next generation of 21st century scientists and engineers; and promotes new economic growth. The Plan is built around seven strategies that represent key areas of importance for Big Data research and development (R&D). Priorities listed within each strategy highlight the intended outcomes that can be addressed by the missions and research funding of NITRD agencies. These include advancing human understanding in all branches of science, medicine, and security; ensuring the Nation’s continued leadership in research and development; and enhancing the Nation’s ability to address pressing societal and environmental issues facing the Nation and the world through research and development

The Federal Big Data Research and Development Strategic Plan

This document was developed through the contributions of the NITRD Big Data SSG members and staff. A special thanks and appreciation to the core team of editors, writers, and reviewers: Lida Beninson (NSF), Quincy Brown (NSF), Elizabeth Burrows (NSF), Dana Hunter (NSF), Craig Jolley (USAID), Meredith Lee (DHS), Nishal Mohan (NSF), Chloe Poston (NSF), Renata Rawlings-Goss (NSF), Carly Robinson (DOE Science), Alejandro Suarez (NSF), Martin Wiener (NSF), and Fen Zhao (NSF). A national Big Data1 innovation ecosystem is essential to enabling knowledge discovery from and confident action informed by the vast resource of new and diverse datasets that are rapidly becoming available in nearly every aspect of life. Big Data has the potential to radically improve the lives of all Americans. It is now possible to combine disparate, dynamic, and distributed datasets and enable everything from predicting the future behavior of complex systems to precise medical treatments, smart energy usage, and focused educational curricula. Government agency research and public-private partnerships, together with the education and training of future data scientists, will enable applications that directly benefit society and the economy of the Nation. To derive the greatest benefits from the many, rich sources of Big Data, the Administration announced a “Big Data Research and Development Initiative” on March 29, 2012.2 Dr. John P. Holdren, Assistant to the President for Science and Technology and Director of the Office of Science and Technology Policy, stated that the initiative “promises to transform our ability to use Big Data for scientific discovery, environmental and biomedical research, education, and national security.” The Federal Big Data Research and Development Strategic Plan (Plan) builds upon the promise and excitement of the myriad applications enabled by Big Data with the objective of guiding Federal agencies as they develop and expand their individual mission-driven programs and investments related to Big Data. The Plan is based on inputs from a series of Federal agency and public activities, and a shared vision: We envision a Big Data innovation ecosystem in which the ability to analyze, extract information from, and make decisions and discoveries based upon large, diverse, and real-time datasets enables new capabilities for Federal agencies and the Nation at large; accelerates the process of scientific discovery and innovation; leads to new fields of research and new areas of inquiry that would otherwise be impossible; educates the next generation of 21st century scientists and engineers; and promotes new economic growth. The Plan is built around seven strategies that represent key areas of importance for Big Data research and development (R&D). Priorities listed within each strategy highlight the intended outcomes that can be addressed by the missions and research funding of NITRD agencies. These include advancing human understanding in all branches of science, medicine, and security; ensuring the Nation’s continued leadership in research and development; and enhancing the Nation’s ability to address pressing societal and environmental issues facing the Nation and the world through research and development

MANAGEMENT PLANS AND WEB-GIS SOFTWARE APPLICATIONS AS ACTIVE AND DYNAMIC TOOLS TO CONSERVE AND VALORIZE HISTORIC PUBLIC GARDENS

Abstract. Historic gardens are artefacts that evolve in a continuous and unavoidable way, and, at the same time, they are heritage and cultural sites that need to be conserved: the recognition of this dual nature motivates us to seek for new approaches to their management issues. Whilst it is necessary to follow site mutations and valorize its features while dynamically changes the appearance of the garden and the needs of the society, at the same time it is fundamental that an appropriate strategic plan sets a target for the garden, a midmid-long term vision, in order to preserve botanic and documentary value and maintain historic and artistic significance. The paper analyzes historical sources, surveys, thematic maps and interpretations to study historic public gardens, considering complexity and vulnerability of the components an d issues involved in historic gardens and consequent multidisciplinary approach. In order to identify conservation and management criteria it underlines analysis and evaluation of the environmental, architectural, land scape and perceptive features of the historic garden and its surroundings, demonstrating the importance to study the site historic stratification and the site context in order to define preservation goals to prevent decay, to mitigate impacts, to set up maintenance programs and management plans. The aim of this essay is also to highlight the role of GIS and WebGIS applications – targeted at public administrations – that integrate the spatial component (topographic map, ortophoto, physical plans, cadastral maps) and databases about botanic inventories and conservation and valorization treatments of historic public of public gardens.</p

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

The U.S. Arctic Observing Viewer: A Web-Mapping Application for Enhancing Environmental Observation of the Changing Arctic

Although much progress has been made with various Arctic Observing efforts, assessing that progress can be difficult. What data collection efforts are established or underway? Where? By whom? To help meet the strategic needs of programs such as the U.S. Study of Environmental Arctic Change (SEARCH), the Arctic Observing Network (AON), Sustaining Arctic Observing Networks (SAON) and related initiatives, an update has been released for the Arctic Observing Viewer (AOV; http://ArcticObservingViewer.org). This web mapping application and information system has begun to compile the who, what, where, and when for thousands of data collection sites (such as boreholes, ship tracks, buoys, towers, sampling stations, sensor networks, vegetation sites, stream gauges, and observatories) wherever marine, terrestrial, or atmospheric data are collected. Contributing partners for this collaborative resource include the U.S. NSF, ACADIS, ADIwg, AOOS, a2dc, AON, ARMAP, BAID, CAFF, IASOA, INTERACT, and others. While focusing on U.S. activities, the AOV welcomes information exchange with international groups for mutual benefit. Users can visualize, navigate, select, search, draw, print, and more. AOV is founded on principles of interoperability, with open metadata and web service standards, so that agencies and organizations can use AOV tools and services for their own purposes. In this way, AOV will reinforce and complement other distributed yet interoperable cyber-resources and will help science planners, funding agencies, researchers, data specialists, and others to assess status, identify overlap, fill gaps, optimize sampling design, refine network performance, clarify directions, access data, coordinate logistics, collaborate, and more in order to meet Arctic Observing goals.Malgré les progrès réalisés dans le cadre de nombreux efforts d’observation de l’Arctique, les progrès peuvent être difficiles à évaluer. Quelles initiatives de collecte de données sont en cours ou sont établies? À quel endroit? Et qui gère ces initiatives? Pour aider à répondre aux besoins stratégiques de programmes comme ceux de l’organisme américain Study of Environmental Arctic Change (SEARCH), du réseau Arctic Observing Network (AON), des réseaux Sustaining Arctic Observing Networks (SAON) et d’autres programmes connexes, on a procédé à la mise à jour de l’Arctic Observing Viewer (AOV; http://ArcticObservingViewer.org). Ce système d’information jumelé à une application de mappage sur le Web a amorcé la compilation des coordonnées et des renseignements se rapportant à des milliers de sites de collecte de données (comme les trous de forage, les trajets de navires, les bouées, les tours, les stations d’échantillonnage, les réseaux de capteurs, les sites de végétation, les fluviomètres et les observatoires) où des données marines, terrestres ou atmosphériques sont prélevées. Parmi les partenaires qui collaborent à cette ressource, notons U.S. NSF, ACADIS, ADIwg, AOOS, a2dc, AON, ARMAP, BAID, CAFF, IASOA, INTERACT et d’autres encore. Bien que l’AOV se concentre sur les activités américaines, il accepte l’échange d’information avec des groupes internationaux lorsqu’il existe des avantages mutuels. Les utilisateurs peuvent visualiser les données, naviguer dans le système, faire des sélections et des recherches, dessiner, imprimer et ainsi de suite. L’AOV fonctionne moyennant des principes d’interopérabilité, avec des métadonnées ouvertes et des normes de service sur le Web afin que les organismes et les organisations puissent utiliser les outils et les services de l’AOV pour leurs propres fins. De cette façon, l’AOV sera en mesure de consolider et de compléter d’autres cyberressources à la fois réparties et interopérables, en plus d’aider les planificateurs de la science, les bailleurs de fonds, les chercheurs, les spécialistes des données et d’autres encore à évaluer les statuts, à repérer les dédoublements, à combler les écarts, à optimiser les plans d’échantillonnage, à raffiner le rendement des réseaux, à clarifier les consignes, à accéder aux données, à coordonner la logistique, à collaborer et ainsi de suite afin de répondre aux objectifs d’observation de l’Arctique

Integrating Spatial Data Infrastructures (SDIs) with Volunteered Geographic Information (VGI) creating a Global GIS platform

Spatial Data Infrastructures (SDIs) are a special category of data hubs that involve technological and human resources and follow well defined legal and technical procedures to collect, store, manage and distribute spatial data. INSPIRE is the EU’s authoritative SDI in which each Member State provides access to their spatial data across a wide spectrum of data themes to support policy-making. In contrast, Volunteered Geographic Information (VGI) is one type of user-generated geographic information (GI) where volunteers use the web and mobile devices to create, assemble and disseminate spatial information. There are similarities and differences between SDIs and VGI, as well as advantages and disadvantages to both. Thus, the integration of these two data sources will enhance what is offered to end users to facilitate decision-making. This idea of integration is in its early stages, because several key issues need to be considered and resolved first. Therefore, this chapter discusses the challenges of integrating VGI with INSPIRE and outlines a generic framework for a global integrated GIS platform, similar in concept to Digital Earth and Virtual Geographic Environments (VGEs), as a realistic scenario for advancements in the short term