1,627 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
A study on Analysis and Utilization of Crowd-sourced Spatio-temporal Contexts from Social Media
兵庫県立大学大学院201
Mobility-awareness in complex event processing systems
The proliferation and vast deployment of mobile devices and sensors over the last couple of years enables a huge number of Mobile Situation Awareness (MSA) applications. These applications need to react in near real-time to situations in the environment of mobile objects like vehicles, pedestrians, or cargo. To this end, Complex Event Processing (CEP) is becoming increasingly important as it allows to scalably detect situations “on-the-fly” by continously processing distributed sensor data streams. Furthermore, recent trends in communication networks promise high real-time conformance to CEP systems by processing sensor data streams on distributed computing resources at the edge of the network, where low network latencies can be achieved. Yet, supporting MSA applications with a CEP middleware that utilizes distributed computing resources proves to be challenging due to the dynamics of mobile devices and sensors. In particular, situations need to be efficiently, scalably, and consistently detected with respect to ever-changing sensors in the environment of a mobile object. Moreover, the computing resources that provide low latencies change with the access points of mobile devices and sensors.
The goal of this thesis is to provide concepts and algorithms to i) continuously detect situations that recently occurred close to a mobile object, ii) support bandwidth and computational efficient detections of such situations on distributed computing resources, and iii) support consistent, low latency, and high quality detections of such situations. To this end, we introduce the distributed Mobile CEP (MCEP) system which automatically adapts the processing of sensor data streams according to a mobile object’s location. MCEP provides an expressive, location-aware query model for situations that recently occurred at a location close to a mobile object. MCEP significantly reduces latency, bandwidth, and processing overhead by providing on-demand and opportunistic adaptation algorithms to dynamically assign event streams to queries of the MCEP system. Moreover, MCEP incorporates algorithms to adapt the deployment of MCEP queries in a network of computing resources. This way, MCEP supports latency-sensitive, large-scale deployments of MSA applications and ensures a low network utilization while mobile objects change their access points to the system. MCEP also provides methods to increase the scalability in terms of deployed MCEP queries by reusing event streams and computations for detecting common situations for several mobile objects
ORÁCULO: Detection of Spatiotemporal Hot Spots of Conflict-Related Events Extracted from Online News Sources
Dissertation presented as the partial requirement for obtaining a Master's degree in Geographic Information Systems and ScienceAchieving situational awareness in peace operations requires understanding
where and when conflict-related activity is most intense. However, the irregular nature
of most factions hinders the use of remote sensing, while winning the trust of the host
populations to allow the collection of wide-ranging human intelligence is a slow process.
Thus, our proposed solution, ORÁCULO, is an information system which detects
spatiotemporal hot spots of conflict-related activity by analyzing the patterns of events
extracted from online news sources, allowing immediate situational awareness. To do so,
it combines a closed-domain supervised event extractor with emerging hot spots analysis
of event space-time cubes. The prototype of ORÁCULO was tested on tweets scraped
from the Twitter accounts of local and international news sources covering the Central
African Republic Civil War, and its test results show that it achieved near state-of-theart
event extraction performance, significant overlap with a reference event dataset, and
strong correlation with the hot spots space-time cube generated from the reference event
dataset, proving the viability of the proposed solution. Future work will focus on
improving the event extraction performance and on testing ORÁCULO in cooperation
with peacekeeping organizations.
Keywords: event extraction, natural language understanding, spatiotemporal analysis,
peace operations, open-source intelligence.Atingir e manter a consciência situacional em operações de paz requer o
conhecimento de quando e onde é que a atividade relacionada com o conflito é mais
intensa. Porém, a natureza irregular da maioria das fações dificulta o uso de deteção
remota, e ganhar a confiança das populações para permitir a recolha de informações é
um processo moroso. Assim, a nossa solução proposta, ORÁCULO, consiste num sistema
de informações que deteta “hot spots” espácio-temporais de atividade relacionada com o
conflito através da análise dos padrões de eventos extraídos de fontes noticiosas online,
(incluindo redes sociais), permitindo consciência situacional imediata. Nesse sentido, a
nossa solução combina um extrator de eventos de domínio limitado baseado em
aprendizagem supervisionada com a análise de “hot spots” emergentes de cubos espaçotempo
de eventos. O protótipo de ORÁCULO foi testado em tweets recolhidos de fontes
noticiosas locais e internacionais que cobrem a Guerra Civil da República Centro-
Africana. Os resultados dos seus testes demonstram que foram conseguidos um
desempenho de extração de eventos próximo do estado da arte, uma sobreposição
significativa com um conjunto de eventos de referência e uma correlação forte com o
cubo espaço-tempo de “hot spots” gerado a partir desse conjunto de referência,
comprovando a viabilidade da solução proposta. Face aos resultados atingidos, o
trabalho futuro focar-se-á em melhorar o desempenho de extração de eventos e em testar
o sistema ORÁCULO em cooperação com organizações que conduzam operações paz
Proceedings of the Academic Track at State of the Map 2019 - Heidelberg (Germany), September 21-23, 2019
State of the Map featured a full day of academic talks. Building upon the motto of SotM 2019 in "Bridging the Map" the Academic Track session was aimed to provide the bridge to join together the experience, understanding, ideas, concepts and skills from different groups of researchers, academics and scientists from around the world. In particular, the Academic Track session was meant to build this bridge that connects members of the OpenStreetMap community and the academic community by providing an open passage for exchange of ideas, communication and opportunities for increased collaboration. These proceedings include 14 abstracts accepted as oral presentations and 6 abstracts presented as posters. Contributions were received from different academic fields, for example geography, remote sensing, computer and information sciences, geomatics, GIScience, the humanities and social sciences, and even from industry actors. We are particularly delighted to have included abstracts from both experienced researchers and students. Overall, it is our hope that these proceedings accurately showcase the ongoing innovation and maturity of scientific investigations and research into OpenStreetMap, showing how it as a research object converges multiple research areas together. Our aim is to show how the sum total of investigations of issues like Volunteered Geographic Information, geo-information, and geo-digital processes and representation shed light on the relations between crowds, real-world applications, technological developments, and scientific research
A Methodology for Extracting Human Bodies from Still Images
Monitoring and surveillance of humans is one of the most prominent applications of today and it is expected to be part of many future aspects of our life, for safety reasons, assisted living and many others. Many efforts have been made towards automatic and robust solutions, but the general problem is very challenging and remains still open. In this PhD dissertation we examine the problem from many perspectives. First, we study the performance of a hardware architecture designed for large-scale surveillance systems. Then, we focus on the general problem of human activity recognition, present an extensive survey of methodologies that deal with this subject and propose a maturity metric to evaluate them.
One of the numerous and most popular algorithms for image processing found in the field is image segmentation and we propose a blind metric to evaluate their results regarding the activity at local regions. Finally, we propose a fully automatic system for segmenting and extracting human bodies from challenging single images, which is the main contribution of the dissertation. Our methodology is a novel bottom-up approach relying mostly on anthropometric constraints and is facilitated by our research in the fields of face, skin and hands detection. Experimental results and comparison with state-of-the-art methodologies demonstrate the success of our approach
Multiple-Aspect Analysis of Semantic Trajectories
This open access book constitutes the refereed post-conference proceedings of the First International Workshop on Multiple-Aspect Analysis of Semantic Trajectories, MASTER 2019, held in conjunction with the 19th European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, in Würzburg, Germany, in September 2019. The 8 full papers presented were carefully reviewed and selected from 12 submissions. They represent an interesting mix of techniques to solve recurrent as well as new problems in the semantic trajectory domain, such as data representation models, data management systems, machine learning approaches for anomaly detection, and common pathways identification
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