The goal of this master thesis is to suggest a methodology for a visualization history of changes in cadastral maps. The suggested methodology takes into account a selection of original historical and contemporary data, processing workflow and follow-up visualization using existing open source web technologies. The greatest contribution of this thesis is in the design of the spatio-temporal database, because currently there does not exist workable editor similar to it, which could be used for creating such data. The suggested procedure is based on database updating method of ISKN database utilizing amendment records. However this method is significantly simplified with the use of PostgreSQL/PostGIS geospatial functions. The available literature and other informational sources to the topic are overviewed in the first part of the master thesis. The term "spatio-temporal data" is thoroughly defined and also the ways of integrating temporal features into spatial data, methods of visualization of spatio-temporal data and recent state of their implementations into desktop platforms and web applications are noted. Furthermore historical and current data sources and their usability for the master thesis are described. The full methodology of pre-processing and processing of data and a subsequent visualization using...

Cette thèse aborde la problématique de la classification non supervisée de données lorsque les caractéristiques des classes sont susceptibles d'évoluer au cours du temps. On parlera également, dans ce cas, de classification dynamique de données temporelles non stationnaires. Le cadre applicatif des travaux concerne le diagnostic par reconnaissance des formes de systèmes complexes dynamiques dont les classes de fonctionnement peuvent, suite à des phénomènes d'usures, des déréglages progressifs ou des contextes d'exploitation variables, évoluer au cours du temps. Un modèle probabiliste dynamique, fondé à la fois sur les mélanges de lois et sur les modèles dynamiques à espace d'état, a ainsi été proposé. Compte tenu de la structure complexe de ce modèle, une variante variationnelle de l'algorithme EM a été proposée pour l'apprentissage de ses paramètres. Dans la perspective du traitement rapide de flux de données, une version séquentielle de cet algorithme a également été développée, ainsi qu'une stratégie de choix dynamique du nombre de classes. Une série d'expérimentations menées sur des données simulées et des données réelles acquises sur le système d'aiguillage des trains a permis d'évaluer le potentiel des approches proposées / Nowadays, diagnosis and monitoring for predictive maintenance of railway components are important key subjects for both operators and manufacturers. They seek to anticipate upcoming maintenance actions, reduce maintenance costs and increase the availability of rail network. In order to maintain the components at a satisfactory level of operation, the implementation of reliable diagnostic strategy is required. In this thesis, we are interested in a main component of railway infrastructure, the railway switch; an important safety device whose failure could heavily impact the availability of the transportation system. The diagnosis of this system is therefore essential and can be done by exploiting sequential measurements acquired successively while the state of the system is evolving over time. These measurements consist of power consumption curves that are acquired during several switch operations. The shape of these curves is indicative of the operating state of the system. The aim is to track the temporal dynamic evolution of railway component state under different operating contexts by analyzing the specific data in order to detect and diagnose problems that may lead to functioning failure. This thesis tackles the problem of temporal data clustering within a broader context of developing innovative tools and decision-aid methods. We propose a new dynamic probabilistic approach within a temporal data clustering framework. This approach is based on both Gaussian mixture models and state-space models. The main challenge facing this work is the estimation of model parameters associated with this approach because of its complex structure. In order to meet this challenge, a variational approach has been developed. The results obtained on both synthetic and real data highlight the advantage of the proposed algorithms compared to other state of the art methods in terms of clustering and estimation accuracy

L’évolution des référentiels spatio-temporels et les dernières avancées des systèmes d’information géographique ont favorisé l’apparition de nouveaux types de services et d’applications liés à la localisation et à la mobilité d’entités d’intérêt dont la supervision et le contrôle d’objets mobiles en temps réel.Ces constats nous ont conduit à nous intéresser en tout premier lieu aux évolutions des objets qui peuvent être envisagées sous forme de trajectoires et offrent de nouvelles perspectives quant à l’analyse en temps réel de leurs comportements particuliers individuels et/ou collectifs.Dans le contexte industriel de l’entreprise Intactile Design, un enjeu majeur émerge : il s’agit de mettre à la disposition de tout expert, amené à prendre une décision au cours d’opérations de surveillance, le plus d'informations possibles relatives au contexte environnant les objets mobiles afin d’en extraire celles permettant la détection de comportements remarquables.L’objectif est donc d'analyser et d'exploiter la masse de données acquises à partir du suivi d'objets mobiles de divers types, et plongés dans des contextes différents de supervision. Pour ce faire, nous proposons une approche générique déclinable sur divers cas de supervision partant de l'hypothèse qui consiste à envisager, pour tout objet mobile, une seule et même trajectoire tout au long de sa vie.L'une des problématiques principales de cette recherche relève des difficultés d'interprétation des données recueillies en temps réel issues de l'observation des objets. En effet celles-ci sont massives, de compositions variables et parfois incomplètes, possiblement redondantes, voire sémantiquement hétérogènes. L'idée est de s’affranchir du manque de sémantique contextuelle et de l’absence de maîtrise des informations liées à l’analyse et à l’exploitation de ces données.L’approche consiste à proposer le recours à une ontologie cadre à des fins d’enrichissements des observations et analyses, et ce pour aider à la détection de comportements d'objets mobiles. L’ontologie cadre représente l'objet mobile et sa trajectoire au sein de tout contexte de supervision et ce de manière générique. Ce modèle s'inspire de travaux existants autour de la modélisation de données spatiales comme temporelles et les étend pour répondre à la spécificité de l’analyse sémantique en temps réel de la mobilité des objets. Pour rendre compte de la spécificité des différents contextes de supervision, l'ontologie est complétée par des règles métiers construites avec l'aide des experts du domaine. L'idée est tout à la fois de disposer d'une représentation de connaissances la plus expressive possible sans augmenter pour autant le coût du raisonnement ; et de rendre l'approche adaptable à toute thématique liée à la supervision.L'approche modulaire spécifiée a ensuite été mise en application au sein d'un prototype logiciel général qui fonctionne comme un système à base de connaissances. Il assure la structuration, enrichissement, extraction et analyse spatio-temporelle des connaissances conformes à notre modèle ontologique et donc offre les éléments nécessaires à la compréhension de comportements remarquables définis par les experts des domaines ciblés.Nous illustrons notre approche au travers d’un cas d’étude concret relatif au domaine des systèmes de supervision des opérations de défense terrestre. / The recent evolution of gazetteers and the latest advances in geographic information systems have promoted new types of services related to the location and mobility of entities of interest, including the real-time supervision and control of moving objects.On the strength of these facts, we are considering the motion of an object based on its trajectory (i.e., the path followed by this object in motion). At our sense, the modeling of trajectories of a number of moving objects offers new insights into real-time analysis of their individual and/or group specific behaviors.Within the industrial context of the company Intactile Design, enhancing decision making in real time for supervision purposes, turns out to be a major challenge. At the same time, much emphasis is being placed on making sure any accessible data, related to the context of the moving objects, are available to experts in order to fully support decision making processes. More importantly, the key idea is to find a strategy that enhances capabilities of detecting unusual behaviors whilst integrating some kind of valuable information related to the context.Consequently, the main objective is to collect and analyze all of the data acquired from the tracking of many different types of moving objects in a variety of supervision contexts.At this effect, we propose a generic and innovative approach that can be applied to any case of supervision based on the assumption that considers for every mobile object, a single and unique trajectory constantly changing over time.One of the main obstacles of this research is the difficulty of real-time interpreting of all of the collected data as these data are mostly complex, voluminous, semantically heterogeneous and incomplete.In this way, the idea is to overcome the lack of contextual semantics (i.e., semantics captured from the observations of the objects evolving within their contexts).To address these challenges, we propose a top domain ontology for moving objects and their trajectories, which is expressed in OWL 2 DL. The ontology attempts to describe the starting categories for the field of mobility and therefore is applicable to all supervision and control contexts.Additionally, this ontology is building upon a few number of existing ontologies that all refer to spatio-temporal knowledge, including GeoSPARQL and OWL Time.Moreover, the ontology and a set of business rules, provided by the experts on a domain of interest, are combined to fully capture the contextual semantics of the domain under consideration.The aim is double: on one side, to benefit from a knowledge representation as expressive as possible that offers a cost-effective reasoning, and on the other side to efficiently adapt the approach to any context related to supervision.Our modular approach is implemented through a general software prototype that runs as a knowledge-based system.The prototype ensures the sustainability, extraction and spatio-temporal reasoning of information that complies with our ontology, and therefore it offers the necessary elements to understand behaviors defined by the experts of the targeted areas.We illustrate our approach through a concrete case study of monitoring systems dedicated to land defense.

This work deals with the and preemptive road users behaviour safety analysis problem. Safety analysis is based on a processing of road users trajectories obtained from processed aerial videos captured by drons. A system for traffic conflicts detection from spatial-temporal data is presented in this work. The standard approach for pro-active traffic conflict indicators evaluation was extended by simulating traffic objects movement in the scene using Ackerman steering geometry in order to get more accurate results.

This diploma thesis deals with automatic soccer event detection . Its goal is to introduce reader to this issue , discuss possible ways of solution of this task and then implement event detection . This work aims at event recognition using spatio - temporal data of gaming objects . Introduced way of dealing with event detection lies in its converting to sequence labeling task . Then such task is solved using LSTM recurrent neural networks . Lastly , result of sequence labeling is interpreted as detected events . Library for event detection has been created as the output of this work . This library allow user to experiment with different variants how to formulate event detection as sequence labeling task .

This thesis deals with the domain indexes in Oracle Database 11g. It describes the database architecture and discusses the available methods of indexing. There are explained concrete ways of the implementation and use of domain indexes, also discussed ways of indexing spatio-temporal data especially the TB-tree structure, which is then implemented as a domain index. Along with the domain index operators are also implemented by means of which the index is subsequently used and tested.

Aujourd'hui, les progrès dans le développement d'appareils mobiles et des capteurs embarqués ont permis un essor sans précédent de services à l'utilisateur. Dans le même temps, la plupart des appareils mobiles génèrent, enregistrent et de communiquent une grande quantité de données personnelles de manière continue. La gestion sécurisée des données personnelles dans les appareils mobiles reste un défi aujourd’hui, que ce soit vis-à-vis des contraintes inhérentes à ces appareils, ou par rapport à l’accès et au partage sûrs et sécurisés de ces informations. Cette thèse adresse ces défis et se focalise sur les traces de localisation. En particulier, s’appuyant sur un serveur de données relationnel embarqué dans des appareils mobiles sécurisés, cette thèse offre une extension de ce serveur à la gestion des données spatio-temporelles (types et operateurs). Et surtout, elle propose une méthode d'indexation spatio-temporelle (TRIFL) efficace et adaptée au modèle de stockage en mémoire flash. Par ailleurs, afin de protéger les traces de localisation personnelles de l'utilisateur, une architecture distribuée et un protocole de collecte participative préservant les données de localisation ont été proposés dans PAMPAS. Cette architecture se base sur des dispositifs hautement sécurisés pour le calcul distribué des agrégats spatio-temporels sur les données privées collectées. / Nowadays, the advances in the development of mobile devices, as well as embedded sensors have permitted an unprecedented number of services to the user. At the same time, most mobile devices generate, store and communicate a large amount of personal information continuously. While managing personal information on the mobile devices is still a big challenge, sharing and accessing these information in a safe and secure way is always an open and hot topic. Personal mobile devices may have various form factors such as mobile phones, smart devices, stick computers, secure tokens or etc. It could be used to record, sense, store data of user's context or environment surrounding him. The most common contextual information is user's location. Personal data generated and stored on these devices is valuable for many applications or services to user, but it is sensitive and needs to be protected in order to ensure the individual privacy. In particular, most mobile applications have access to accurate and real-time location information, raising serious privacy concerns for their users.In this dissertation, we dedicate the two parts to manage the location traces, i.e. the spatio-temporal data on mobile devices. In particular, we offer an extension of spatio-temporal data types and operators for embedded environments. These data types reconcile the features of spatio-temporal data with the embedded requirements by offering an optimal data presentation called Spatio-temporal object (STOB) dedicated for embedded devices. More importantly, in order to optimize the query processing, we also propose an efficient indexing technique for spatio-temporal data called TRIFL designed for flash storage. TRIFL stands for TRajectory Index for Flash memory. It exploits unique properties of trajectory insertion, and optimizes the data structure for the behavior of flash and the buffer cache. These ideas allow TRIFL to archive much better performance in both Flash and magnetic storage compared to its competitors.Additionally, we also investigate the protect user's sensitive information in the remaining part of this thesis by offering a privacy-aware protocol for participatory sensing applications called PAMPAS. PAMPAS relies on secure hardware solutions and proposes a user-centric privacy-aware protocol that fully protects personal data while taking advantage of distributed computing. For this to be done, we also propose a partitioning algorithm an aggregate algorithm in PAMPAS. This combination drastically reduces the overall costs making it possible to run the protocol in near real-time at a large scale of participants, without any personal information leakage.

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This thesis contains a review of previous work in the fields of encoding sequential healthcare data and predicting graft- versus- host disease, a medical condition, based on patient history using machine learning. A new encoding of such data is proposed for machine learning purposes. The proposed encoding, called bag of binned weighted events, is a combination of two strategies proposed in previous work, called bag of binned events and bag of weighted events. An empirical experiment is designed to evaluate the predictive performance of the proposed encoding over various binning windows to that of the previous encodings, based on the area under the receiver operating characteristic curve (AUC) metric. The experiment is carried out on real- world healthcare data obtained from Swedish registries, using the random forest and the logistic regression algorithms. After filtering the data, solving quality issues and tuning hyperparameters of the models, final results are obtained. These results indicate that the proposed encoding strategy performs on par, or slightly better than the bag of weighted events, and outperforms the bag of binned events in most cases. However, differences in metrics show small differences. It is also observed that the proposed encoding usually performs better with longer binning windows which may be attributed to data noise. Future work is proposed in the form of repeating the experiment with different datasets and models, as well as changing the binning window length of the baseline algorithms. / Denna avhandling innehåller en recension av tidigare arbete inom områden av kodning av sekventiell sjukvårdsdata och förutsägelse av transplantat- mot- värdsjukdom, ett medicinskt tillstånd, baserat på patienthistoria med maskininlärning. En ny kodning av sådan data föreslås i maskininlärningssyfte. Den föreslagna kodningen, kallad bag of binned weighted events, är en kombination av två strategier som föreslagits i tidigare arbete, kallad bag of binned events och bag of weighted events. Ett empiriskt experiment är utformat för att utvärdera den föreslagna prestandan för den föreslagna kodningen över olika binningfönster jämfört med tidigare kodningar, baserat på AUC- måttet. Experimentet utförs på verkliga sjukvårdsdata som erhållits från svenska register, med random forest och logistic regression. Efter filtrering av data, lösning av kvalitetsproblem och justering av hyperparametrar för modellerna, erhålls slutliga resultat. Dessa resultat indikerar att den föreslagna kodningsstrategin presterar i nivå med, eller något bättre än bag of weighted events, och överträffar i de flesta fall bag of binned events. Skillnader i mått är dock små. Det observeras också att den föreslagna kodningen vanligtvis fungerar bättre med längre binningfönster som kan tillskrivas dataljud. Framtida arbete föreslås i form av att upprepa experimentet med olika datamängder och modeller, samt att ändra binningfönstrets längd för basalgoritmerna.

A variety of research exists for the processing of continuous queries in large, mobile environments. Each method tries, in its own way, to address the computational bottleneck of constantly processing so many queries. For this research, we present a two-pronged approach at addressing this problem. Firstly, we introduce an efficient and scalable system for monitoring traditional, continuous queries by leveraging the parallel processing capability of the Graphics Processing Unit. We examine a naive CPU-based solution for continuous range-monitoring queries, and we then extend this system using the GPU. Additionally, with mobile communication devices becoming commodity, location-based services will become ubiquitous. To cope with the very high intensity of location-based queries, we propose a view oriented approach of the location database, thereby reducing computation costs by exploiting computation sharing amongst queries requiring the same view. Our studies show that by exploiting the parallel processing power of the GPU, we are able to significantly scale the number of mobile objects, while maintaining an acceptable level of performance. Our second approach was to view this research problem as one belonging to the domain of data streams. Several works have convincingly argued that the two research fields of spatiotemporal data streams and the management of moving objects can naturally come together. [IlMI10, ChFr03, MoXA04] For example, the output of a GPS receiver, monitoring the position of a mobile object, is viewed as a data stream of location updates. This data stream of location updates, along with those from the plausibly many other mobile objects, is received at a centralized server, which processes the streams upon arrival, effectively updating the answers to the currently active queries in real time. iv For this second approach, we present GEDS, a scalable, Graphics Processing Unit (GPU)-based framework for the evaluation of continuous spatio-temporal queries over spatiotemporal data streams. Specifically, GEDS employs the computation sharing and parallel processing paradigms to deliver scalability in the evaluation of continuous, spatio-temporal range queries and continuous, spatio-temporal kNN queries. The GEDS framework utilizes the parallel processing capability of the GPU, a stream processor by trade, to handle the computation required in this application. Experimental evaluation shows promising performance and shows the scalability and efficacy of GEDS in spatio-temporal data streaming environments. Additional performance studies demonstrate that, even in light of the costs associated with memory transfers, the parallel processing power provided by GEDS clearly counters and outweighs any associated costs. Finally, in an effort to move beyond the analysis of specific algorithms over the GEDS framework, we take a broader approach in our analysis of GPU computing. What algorithms are appropriate for the GPU? What types of applications can benefit from the parallel and stream processing power of the GPU? And can we identify a class of algorithms that are best suited for GPU computing? To answer these questions, we develop an abstract performance model, detailing the relationship between the CPU and the GPU. From this model, we are able to extrapolate a list of attributes common to successful GPU-based applications, thereby providing insight into which algorithms and applications are best suited for the GPU and also providing an estimated theoretical speedup for said GPU-based applications