1,490 research outputs found
Recent Developments and Future Trends in Volunteered Geographic Information Research: The Case of OpenStreetMap
User-generated content (UGC) platforms on the Internet have experienced a steep increase in data contributions in recent years. The ubiquitous usage of location-enabled devices, such as smartphones, allows contributors to share their geographic information on a number of selected online portals. The collected information is oftentimes referred to as volunteered geographic information (VGI). One of the most utilized, analyzed and cited VGI-platforms, with an increasing popularity over the past few years, is OpenStreetMap (OSM), whose main goal it is to create a freely available geographic database of the world. This paper presents a comprehensive overview of the latest developments in VGI research, focusing on its collaboratively collected geodata and corresponding contributor patterns. Additionally, trends in the realm of OSM research are discussed, highlighting which aspects need to be investigated more closely in the near future
A review of the role of sensors in mobile context-aware recommendation systems
Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios
Towards Mobility Data Science (Vision Paper)
Mobility data captures the locations of moving objects such as humans,
animals, and cars. With the availability of GPS-equipped mobile devices and
other inexpensive location-tracking technologies, mobility data is collected
ubiquitously. In recent years, the use of mobility data has demonstrated
significant impact in various domains including traffic management, urban
planning, and health sciences. In this paper, we present the emerging domain of
mobility data science. Towards a unified approach to mobility data science, we
envision a pipeline having the following components: mobility data collection,
cleaning, analysis, management, and privacy. For each of these components, we
explain how mobility data science differs from general data science, we survey
the current state of the art and describe open challenges for the research
community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from
the metadata. PDF has not been change
NEW METHODS FOR MINING SEQUENTIAL AND TIME SERIES DATA
Data mining is the process of extracting knowledge from large amounts of data. It covers a variety of techniques aimed at discovering diverse types of patterns on the basis of the requirements of the domain. These techniques include association rules mining, classification, cluster analysis and outlier detection. The availability of applications that produce massive amounts of spatial, spatio-temporal (ST) and time series data (TSD) is the rationale for developing specialized techniques to excavate such data. In spatial data mining, the spatial co-location rule problem is different from the association rule problem, since there is no natural notion of transactions in spatial datasets that are embedded in continuous geographic space. Therefore, we have proposed an efficient algorithm (GridClique) to mine interesting spatial co-location patterns (maximal cliques). These patterns are used as the raw transactions for an association rule mining technique to discover complex co-location rules. Our proposal includes certain types of complex relationships â especially negative relationships â in the patterns. The relationships can be obtained from only the maximal clique patterns, which have never been used until now. Our approach is applied on a well-known astronomy dataset obtained from the Sloan Digital Sky Survey (SDSS). ST data is continuously collected and made accessible in the public domain. We present an approach to mine and query large ST data with the aim of finding interesting patterns and understanding the underlying process of data generation. An important class of queries is based on the flock pattern. A flock is a large subset of objects moving along paths close to each other for a predefined time. One approach to processing a âflock queryâ is to map ST data into high-dimensional space and to reduce the query to a sequence of standard range queries that can be answered using a spatial indexing structure; however, the performance of spatial indexing structures rapidly deteriorates in high-dimensional space. This thesis sets out a preprocessing strategy that uses a random projection to reduce the dimensionality of the transformed space. We use probabilistic arguments to prove the accuracy of the projection and to present experimental results that show the possibility of managing the curse of dimensionality in a ST setting by combining random projections with traditional data structures. In time series data mining, we devised a new space-efficient algorithm (SparseDTW) to compute the dynamic time warping (DTW) distance between two time series, which always yields the optimal result. This is in contrast to other approaches which typically sacrifice optimality to attain space efficiency. The main idea behind our approach is to dynamically exploit the existence of similarity and/or correlation between the time series: the more the similarity between the time series, the less space required to compute the DTW between them. Other techniques for speeding up DTW, impose a priori constraints and do not exploit similarity characteristics that may be present in the data. Our experiments demonstrate that SparseDTW outperforms these approaches. We discover an interesting pattern by applying SparseDTW algorithm: âpairs tradingâ in a large stock-market dataset, of the index daily prices from the Australian stock exchange (ASX) from 1980 to 2002
Enhanced LoD concepts for virtual 3D city models
Virtual 3D city models contain digital three dimensional representations of city objects like buildings, streets or technical infrastructure. Because size and complexity of these models continuously grow, a Level of Detail (LoD) concept effectively supporting the partitioning of a complete model into alternative models of different complexity and providing metadata, addressing informational content, complexity and quality of each alternative model is indispensable. After a short overview on various LoD concepts, this paper discusses the existing LoD concept of the CityGML standard for 3D city models and identifies a number of deficits. Based on this analysis, an alternative concept is developed and illustrated with several examples. It differentiates between first, a Geometric Level of Detail (GLoD) and a Semantic Level of Detail (SLoD), and second between the interior building and ist exterior shell. Finally, a possible implementation of the new concept is demonstrated by means of an UML model
APRIL: Approximating Polygons as Raster Interval Lists
The spatial intersection join an important spatial query operation, due to
its popularity and high complexity. The spatial join pipeline takes as input
two collections of spatial objects (e.g., polygons). In the filter step, pairs
of object MBRs that intersect are identified and passed to the refinement step
for verification of the join predicate on the exact object geometries. The
bottleneck of spatial join evaluation is in the refinement step. We introduce
APRIL, a powerful intermediate step in the pipeline, which is based on raster
interval approximations of object geometries. Our technique applies a sequence
of interval joins on 'intervalized' object approximations to determine whether
the objects intersect or not. Compared to previous work, APRIL approximations
are simpler, occupy much less space, and achieve similar pruning effectiveness
at a much higher speed. Besides intersection joins between polygons, APRIL can
directly be applied and has high effectiveness for polygonal range queries,
within joins, and polygon-linestring joins. By applying a lightweight
compression technique, APRIL approximations may occupy even less space than
object MBRs. Furthermore, APRIL can be customized to apply on partitioned data
and on polygons of varying sizes, rasterized at different granularities. Our
last contribution is a novel algorithm that computes the APRIL approximation of
a polygon without having to rasterize it in full, which is orders of magnitude
faster than the computation of other raster approximations. Experiments on real
data demonstrate the effectiveness and efficiency of APRIL; compared to the
state-of-the-art intermediate filter, APRIL occupies 2x-8x less space, is
3.5x-8.5x more time-efficient, and reduces the end-to-end join cost up to 3
times.Comment: 12 page
Exploring multi-granular documentation strategies for the representation, discovery and use of geographic information
This thesis explores how digital representations of geography and Geographic
Information (GI) may be described, and how these descriptions facilitate the use of
the resources they depict. More specifically, it critically examines existing geospatial
documentation practices and aims to identify opportunities for refinement therein,
whether when used to signpost those data assets documented, for managing and
maintaining information assets, or to assist in resource interpretation and
discrimination. Documentation of GI can therefore facilitate its utilisation; it can be
reasonably expected that by refining documentation practices, GI hold the potential
for being better exploited. The underpinning theme connecting the individual papers
of the thesis is one of multi-granular documentation. GI may be recorded at varying
degrees of granularity, and yet traditional documentation efforts have predominantly
focussed on a solitary level (that of the geospatial data layer). Developing
documentation practices to account for other granularities permits the description of
GI at different levels of detail and can further assist in realising its potential through
better discovery, interpretation and use. One of the aims of the current work is to
establish the merit of such multi-granular practices. Over the course of four research
papers and a short research article, proprietary as well as open source software
approaches are accordingly presented and provide proof-of-concept and conceptual
solutions that aim to enhance GI utilisation through improved documentation
practices. Presented in the context of an existing body of research, the proposed
approaches focus on the technological infrastructure supporting data discovery, the
automation of documentation processes and the implications of describing geospatial
information resources of varying granularity. Each paper successively contributes to the notion that geospatial resources are potentially better exploited when
documentation practices account for the multi-granular aspects of GI, and the
varying ways in which such documentation may be used. In establishing the merit of
multi-granular documentation, it is nevertheless recognised in the current work that
instituting a comprehensive documentation strategy at several granularities may be
unrealistic for some geospatial applications. Pragmatically, the level of effort
required would be excessive, making universal adoption impractical. Considering
however the ever-expanding volumes of geospatial data gathered and the demand for
ways of managing and maintaining the usefulness of potentially unwieldy
repositories, improved documentation practices are required. A system of
hierarchical documentation, of self-documenting information, would provide for
information discovery and retrieval from such expanding resource pools at multiple
granularities, improve the accessibility of GI and ultimately, its utilisation
Integrating Haptic Feedback into Mobile Location Based Services
Haptics is a feedback technology that takes advantage of the human sense of touch by
applying forces, vibrations, and/or motions to a haptic-enabled device such as a mobile
phone. Historically, human-computer interaction has been visual - text and images on
the screen. Haptic feedback can be an important additional method especially in Mobile
Location Based Services such as knowledge discovery, pedestrian navigation and notification
systems. A knowledge discovery system called the Haptic GeoWand is a low
interaction system that allows users to query geo-tagged data around them by using
a point-and-scan technique with their mobile device. Haptic Pedestrian is a navigation
system for walkers. Four prototypes have been developed classified according to
the userâs guidance requirements, the user type (based on spatial skills), and overall
system complexity. Haptic Transit is a notification system that provides spatial information
to the users of public transport. In all these systems, haptic feedback is used
to convey information about location, orientation, density and distance by use of the
vibration alarm with varying frequencies and patterns to help understand the physical
environment. Trials elicited positive responses from the users who see benefit in being
provided with a âheads upâ approach to mobile navigation. Results from a memory recall
test show that the users of haptic feedback for navigation had better memory recall
of the region traversed than the users of landmark images. Haptics integrated into a
multi-modal navigation system provides more usable, less distracting but more effective
interaction than conventional systems. Enhancements to the current work could include
integration of contextual information, detailed large-scale user trials and the exploration
of using haptics within confined indoor spaces
Development of a GIS-based method for sensor network deployment and coverage optimization
Au cours des derniĂšres annĂ©es, les rĂ©seaux de capteurs ont Ă©tĂ© de plus en plus utilisĂ©s dans diffĂ©rents contextes dâapplication allant de la surveillance de lâenvironnement au suivi des objets en mouvement, au dĂ©veloppement des villes intelligentes et aux systĂšmes de transport intelligent, etc. Un rĂ©seau de capteurs est gĂ©nĂ©ralement constituĂ© de nombreux dispositifs sans fil dĂ©ployĂ©s dans une rĂ©gion d'intĂ©rĂȘt. Une question fondamentale dans un rĂ©seau de capteurs est l'optimisation de sa couverture spatiale. La complexitĂ© de l'environnement de dĂ©tection avec la prĂ©sence de divers obstacles empĂȘche la couverture optimale de plusieurs zones. Par consĂ©quent, la position du capteur affecte la façon dont une rĂ©gion est couverte ainsi que le coĂ»t de construction du rĂ©seau. Pour un dĂ©ploiement efficace d'un rĂ©seau de capteurs, plusieurs algorithmes d'optimisation ont Ă©tĂ© dĂ©veloppĂ©s et appliquĂ©s au cours des derniĂšres annĂ©es. La plupart de ces algorithmes reposent souvent sur des modĂšles de capteurs et de rĂ©seaux simplifiĂ©s. En outre, ils ne considĂšrent pas certaines informations spatiales de l'environnement comme les modĂšles numĂ©riques de terrain, les infrastructures construites humaines et la prĂ©sence de divers obstacles dans le processus d'optimisation. L'objectif global de cette thĂšse est d'amĂ©liorer les processus de dĂ©ploiement des capteurs en intĂ©grant des informations et des connaissances gĂ©ospatiales dans les algorithmes d'optimisation. Pour ce faire, trois objectifs spĂ©cifiques sont dĂ©finis. Tout d'abord, un cadre conceptuel est dĂ©veloppĂ© pour l'intĂ©gration de l'information contextuelle dans les processus de dĂ©ploiement des rĂ©seaux de capteurs. Ensuite, sur la base du cadre proposĂ©, un algorithme d'optimisation sensible au contexte local est dĂ©veloppĂ©. L'approche Ă©largie est un algorithme local gĂ©nĂ©rique pour le dĂ©ploiement du capteur qui a la capacitĂ© de prendre en considĂ©ration de l'information spatiale, temporelle et thĂ©matique dans diffĂ©rents contextes d'applications. Ensuite, l'analyse de l'Ă©valuation de la prĂ©cision et de la propagation d'erreurs est effectuĂ©e afin de dĂ©terminer l'impact de l'exactitude des informations contextuelles sur la mĂ©thode d'optimisation du rĂ©seau de capteurs proposĂ©e. Dans cette thĂšse, l'information contextuelle a Ă©tĂ© intĂ©grĂ©e aux mĂ©thodes d'optimisation locales pour le dĂ©ploiement de rĂ©seaux de capteurs. L'algorithme dĂ©veloppĂ© est basĂ© sur le diagramme de VoronoĂŻ pour la modĂ©lisation et la reprĂ©sentation de la structure gĂ©omĂ©trique des rĂ©seaux de capteurs. Dans l'approche proposĂ©e, les capteurs change leur emplacement en fonction des informations contextuelles locales (l'environnement physique, les informations de rĂ©seau et les caractĂ©ristiques des capteurs) visant Ă amĂ©liorer la couverture du rĂ©seau. La mĂ©thode proposĂ©e est implĂ©mentĂ©e dans MATLAB et est testĂ©e avec plusieurs jeux de donnĂ©es obtenus Ă partir des bases de donnĂ©es spatiales de la ville de QuĂ©bec. Les rĂ©sultats obtenus Ă partir de diffĂ©rentes Ă©tudes de cas montrent l'efficacitĂ© de notre approche.In recent years, sensor networks have been increasingly used for different applications ranging from environmental monitoring, tracking of moving objects, development of smart cities and smart transportation system, etc. A sensor network usually consists of numerous wireless devices deployed in a region of interest. A fundamental issue in a sensor network is the optimization of its spatial coverage. The complexity of the sensing environment with the presence of diverse obstacles results in several uncovered areas. Consequently, sensor placement affects how well a region is covered by sensors as well as the cost for constructing the network. For efficient deployment of a sensor network, several optimization algorithms are developed and applied in recent years. Most of these algorithms often rely on oversimplified sensor and network models. In addition, they do not consider spatial environmental information such as terrain models, human built infrastructures, and the presence of diverse obstacles in the optimization process. The global objective of this thesis is to improve sensor deployment processes by integrating geospatial information and knowledge in optimization algorithms. To achieve this objective three specific objectives are defined. First, a conceptual framework is developed for the integration of contextual information in sensor network deployment processes. Then, a local context-aware optimization algorithm is developed based on the proposed framework. The extended approach is a generic local algorithm for sensor deployment, which accepts spatial, temporal, and thematic contextual information in different situations. Next, an accuracy assessment and error propagation analysis is conducted to determine the impact of the accuracy of contextual information on the proposed sensor network optimization method. In this thesis, the contextual information has been integrated in to the local optimization methods for sensor network deployment. The extended algorithm is developed based on point Voronoi diagram in order to represent geometrical structure of sensor networks. In the proposed approach sensors change their location based on local contextual information (physical environment, network information and sensor characteristics) aiming to enhance the network coverage. The proposed method is implemented in MATLAB and tested with several data sets obtained from Quebec City spatial database. Obtained results from different case studies show the effectiveness of our approach
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