459 research outputs found
A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web
Over the past decade, rapid advances in web technologies, coupled with
innovative models of spatial data collection and consumption, have generated a
robust growth in geo-referenced information, resulting in spatial information
overload. Increasing 'geographic intelligence' in traditional text-based
information retrieval has become a prominent approach to respond to this issue
and to fulfill users' spatial information needs. Numerous efforts in the
Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the
Linking Open Data initiative have converged in a constellation of open
knowledge bases, freely available online. In this article, we survey these open
knowledge bases, focusing on their geospatial dimension. Particular attention
is devoted to the crucial issue of the quality of geo-knowledge bases, as well
as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic
Network, is outlined as our contribution to this area. Research directions in
information integration and Geographic Information Retrieval (GIR) are then
reviewed, with a critical discussion of their current limitations and future
prospects
Automatic Geospatial Data Conflation Using Semantic Web Technologies
Duplicate geospatial data collections and maintenance are an extensive problem across Australia government organisations. This research examines how Semantic Web technologies can be used to automate the geospatial data conflation process. The research presents a new approach where generation of OWL ontologies based on output data models and presenting geospatial data as RDF triples serve as the basis for the solution and SWRL rules serve as the core to automate the geospatial data conflation processes
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
A conceptual framework and a risk management approach for interoperability between geospatial datacubes
De nos jours, nous observons un intĂ©rĂȘt grandissant pour les bases de donnĂ©es gĂ©ospatiales multidimensionnelles. Ces bases de donnĂ©es sont dĂ©veloppĂ©es pour faciliter la prise de dĂ©cisions stratĂ©giques des organisations, et plus spĂ©cifiquement lorsquâil sâagit de donnĂ©es de diffĂ©rentes Ă©poques et de diffĂ©rents niveaux de granularitĂ©. Cependant, les utilisateurs peuvent avoir besoin dâutiliser plusieurs bases de donnĂ©es gĂ©ospatiales multidimensionnelles. Ces bases de donnĂ©es peuvent ĂȘtre sĂ©mantiquement hĂ©tĂ©rogĂšnes et caractĂ©risĂ©es par diffĂ©rent degrĂ©s de pertinence par rapport au contexte dâutilisation. RĂ©soudre les problĂšmes sĂ©mantiques liĂ©s Ă lâhĂ©tĂ©rogĂ©nĂ©itĂ© et Ă la diffĂ©rence de pertinence dâune maniĂšre transparente aux utilisateurs a Ă©tĂ© lâobjectif principal de lâinteropĂ©rabilitĂ© au cours des quinze derniĂšres annĂ©es. Dans ce contexte, diffĂ©rentes solutions ont Ă©tĂ© proposĂ©es pour traiter lâinteropĂ©rabilitĂ©. Cependant, ces solutions ont adoptĂ© une approche non systĂ©matique. De plus, aucune solution pour rĂ©soudre des problĂšmes sĂ©mantiques spĂ©cifiques liĂ©s Ă lâinteropĂ©rabilitĂ© entre les bases de donnĂ©es gĂ©ospatiales multidimensionnelles nâa Ă©tĂ© trouvĂ©e. Dans cette thĂšse, nous supposons quâil est possible de dĂ©finir une approche qui traite ces problĂšmes sĂ©mantiques pour assurer lâinteropĂ©rabilitĂ© entre les bases de donnĂ©es gĂ©ospatiales multidimensionnelles. Ainsi, nous dĂ©finissons tout dâabord lâinteropĂ©rabilitĂ© entre ces bases de donnĂ©es. Ensuite, nous dĂ©finissons et classifions les problĂšmes dâhĂ©tĂ©rogĂ©nĂ©itĂ© sĂ©mantique qui peuvent se produire au cours dâune telle interopĂ©rabilitĂ© de diffĂ©rentes bases de donnĂ©es gĂ©ospatiales multidimensionnelles. Afin de rĂ©soudre ces problĂšmes dâhĂ©tĂ©rogĂ©nĂ©itĂ© sĂ©mantique, nous proposons un cadre conceptuel qui se base sur la communication humaine. Dans ce cadre, une communication sâĂ©tablit entre deux agents systĂšme reprĂ©sentant les bases de donnĂ©es gĂ©ospatiales multidimensionnelles impliquĂ©es dans un processus dâinteropĂ©rabilitĂ©. Cette communication vise Ă Ă©changer de lâinformation sur le contenu de ces bases. Ensuite, dans lâintention dâaider les agents Ă prendre des dĂ©cisions appropriĂ©es au cours du processus dâinteropĂ©rabilitĂ©, nous Ă©valuons un ensemble dâindicateurs de la qualitĂ© externe (fitness-for-use) des schĂ©mas et du contexte de production (ex., les mĂ©tadonnĂ©es). Finalement, nous mettons en Ćuvre lâapproche afin de montrer sa faisabilitĂ©.Today, we observe wide use of geospatial databases that are implemented in many forms (e.g., transactional centralized systems, distributed databases, multidimensional datacubes). Among those possibilities, the multidimensional datacube is more appropriate to support interactive analysis and to guide the organizationâs strategic decisions, especially when different epochs and levels of information granularity are involved. However, one may need to use several geospatial multidimensional datacubes which may be semantically heterogeneous and having different degrees of appropriateness to the context of use. Overcoming the semantic problems related to the semantic heterogeneity and to the difference in the appropriateness to the context of use in a manner that is transparent to users has been the principal aim of interoperability for the last fifteen years. However, in spite of successful initiatives, today's solutions have evolved in a non systematic way. Moreover, no solution has been found to address specific semantic problems related to interoperability between geospatial datacubes. In this thesis, we suppose that it is possible to define an approach that addresses these semantic problems to support interoperability between geospatial datacubes. For that, we first describe interoperability between geospatial datacubes. Then, we define and categorize the semantic heterogeneity problems that may occur during the interoperability process of different geospatial datacubes. In order to resolve semantic heterogeneity between geospatial datacubes, we propose a conceptual framework that is essentially based on human communication. In this framework, software agents representing geospatial datacubes involved in the interoperability process communicate together. Such communication aims at exchanging information about the content of geospatial datacubes. Then, in order to help agents to make appropriate decisions during the interoperability process, we evaluate a set of indicators of the external quality (fitness-for-use) of geospatial datacube schemas and of production context (e.g., metadata). Finally, we implement the proposed approach to show its feasibility
Low-latency, query-driven analytics over voluminous multidimensional, spatiotemporal datasets
2017 Summer.Includes bibliographical references.Ubiquitous data collection from sources such as remote sensing equipment, networked observational devices, location-based services, and sales tracking has led to the accumulation of voluminous datasets; IDC projects that by 2020 we will generate 40 zettabytes of data per year, while Gartner and ABI estimate 20-35 billion new devices will be connected to the Internet in the same time frame. The storage and processing requirements of these datasets far exceed the capabilities of modern computing hardware, which has led to the development of distributed storage frameworks that can scale out by assimilating more computing resources as necessary. While challenging in its own right, storing and managing voluminous datasets is only the precursor to a broader field of study: extracting knowledge, insights, and relationships from the underlying datasets. The basic building block of this knowledge discovery process is analytic queries, encompassing both query instrumentation and evaluation. This dissertation is centered around query-driven exploratory and predictive analytics over voluminous, multidimensional datasets. Both of these types of analysis represent a higher-level abstraction over classical query models; rather than indexing every discrete value for subsequent retrieval, our framework autonomously learns the relationships and interactions between dimensions in the dataset (including time series and geospatial aspects), and makes the information readily available to users. This functionality includes statistical synopses, correlation analysis, hypothesis testing, probabilistic structures, and predictive models that not only enable the discovery of nuanced relationships between dimensions, but also allow future events and trends to be predicted. This requires specialized data structures and partitioning algorithms, along with adaptive reductions in the search space and management of the inherent trade-off between timeliness and accuracy. The algorithms presented in this dissertation were evaluated empirically on real-world geospatial time-series datasets in a production environment, and are broadly applicable across other storage frameworks
Dwelling on ontology - semantic reasoning over topographic maps
The thesis builds upon the hypothesis that the spatial arrangement of topographic
features, such as buildings, roads and other land cover parcels, indicates how land is
used. The aim is to make this kind of high-level semantic information explicit within
topographic data. There is an increasing need to share and use data for a wider range of
purposes, and to make data more definitive, intelligent and accessible. Unfortunately,
we still encounter a gap between low-level data representations and high-level concepts
that typify human qualitative spatial reasoning. The thesis adopts an ontological
approach to bridge this gap and to derive functional information by using standard
reasoning mechanisms offered by logic-based knowledge representation formalisms. It
formulates a framework for the processes involved in interpreting land use information
from topographic maps. Land use is a high-level abstract concept, but it is also an
observable fact intimately tied to geography. By decomposing this relationship, the
thesis correlates a one-to-one mapping between high-level conceptualisations
established from human knowledge and real world entities represented in the data.
Based on a middle-out approach, it develops a conceptual model that incrementally
links different levels of detail, and thereby derives coarser, more meaningful
descriptions from more detailed ones. The thesis verifies its proposed ideas by
implementing an ontology describing the land use âresidential areaâ in the ontology
editor Protégé. By asserting knowledge about high-level concepts such as types of
dwellings, urban blocks and residential districts as well as individuals that link directly
to topographic features stored in the database, the reasoner successfully infers instances
of the defined classes. Despite current technological limitations, ontologies are a
promising way forward in the manner we handle and integrate geographic data,
especially with respect to how humans conceptualise geographic space
Personalized City Tours - An Extension of the OGC OpenLocation Specification
A business trip to London last month , a day visit in Cologne next saturday and romantic weekend in Paris in autumn â this example exhibits one of the central characteristics of todayâs tourism. People in the western hemisphere take much pleasure in frequent and repeated short term visits of cities. Every city visitor faces the general problems of where to go and what to see in the diverse microcosm of a metropolis. This thesis presents a framework for the generation of personalized city tours - as extension of the Open Location Specification of the Open Geospatial Consortium. It is founded on context-awareness and personalization while at the same time proposing a combined approach to allow for adaption to the user. This framework considers TimeGeography and its algorithmic implementations to be able to cope with spatio-temporal constraints of a city tour. Traveling salesmen problems - for which a heuristic approache is proposed â are subjacent to the tour generation. To meet the requirements of todayâs distributed and heterogeneous computing environments, the tour framework comprises individual services that expose standard-compliant interfaces and allow for integration in service oriented architectures
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
- âŠ