Big data of type spatial is growing exponentially with the highest rate due to extensive growth in usage of sensors, IoT and mobile devices’ spatial data generation, therefore maintaining, processing and using such data efficiently, effectively with high performance has become one of the top priorities for Database management system providers, hence spatial database features and datatypes have become serious criteria in evaluating database management systems that are supposed to work as the back-end for spatial applications and services. With exponential growth of data and introducing of new types of data, “Big Data” has become strongly focused area that has gained the attention of different sectors e.g. academia, industries and governments to other organizations and studies. The rising trend in high resolution and large-scale geographical information systems have resulted in more companies providing location-based applications and services, therefore finding a proper database management system solution that can support spatial big data features, with multi-model big data support that is reliable and affordable has become a business need for many companies. Concerning the fact that choosing proper solution for any software project can be crucial due to the total cost and desired functionalities that any product could possibly bring into the solution. Migration is also a very complicated and costly procedure that many companies should avoid, which justifies the criticality of choosing the right solution based on the specific needs of any organization. Companies providing spatial applications and services are growing with the common concern of providing successful solutions and robust services. One of the most significant elements that ensures services’ and hence the providers’ reputation and positive depiction is services’ high availability. The possible future work for the thesis could be to develop the framework into a decision support solution for IT businesses with emphasize on spatial features. Another possibility for the future works would be to evaluate the framework by testing the evaluation framework on many other different DBMSs.

O termo de Banco de Dados Espacial tem sido usado frequentemente nas aplicações relativas a Sistemas de Informações Geográficas. Isto demonstra a aplicação de uma ferramenta computacional poderosa (Sistemas de Gerenciamento de Banco de Dados) na solução de problemas de Planejamento Urbano e Regional. Para que o Banco de Dados possa operar necessita uma Fonte de Dados ou Base de Dados que alimente a sua estrutura e garanta a qualidade e precisão dessas informações. Daí em diante se podeaplicar as suas próprias capacidades na solução dos problemas dos seus usuários. Reduzindo a escala podemos traçar um paralelo entre os Projetos de Planejamento Urbano e em Projeto de Edificações, logo as mesmas ferramentas podem ser utilizadas neste segundo tipo de projetos. Preparando caminho à utilização destas ferramentas, este trabalho relata o caminho escolhido, a implementação e a aplicação de uma abordagem de formação de Base de Dados Espaciais a partir do projeto de Edificações. / Spatial database is a term frequently used in Geographic Information Systems applications. This means the use of a powerful computing tool (Database Management System) in solving Regional and Urban Planning problems. A Database Management System needs a Data Source or Base of Data for the Data entry and quality and precise information guaranteening. Then we can use its own capacities in the solution of the problems of the users. Reducing the scale we can make a parallel between Urban Planning Design and Building Design and the same tools can be used for the second. Preparing the way of the utilization of these tools, this work speak about the choosing way, the implementation and an approach application of Base of Data formation from Building Design.

¡@¡@Due to wireless communication technologies, positioning technologies, and mobile computing develop quickly, mobile services are becoming practical and important on big spatiotemporal databases management. Mobile service users move only inside a spatial space, e:g: a country. They often issue the K Nearest Neighbor (kNN) query to obtain data objects reachable through the spatial database. The challenge problem of mobile services is how to efficiently answer the data objects which users interest to the corresponding mobile users. One type of kNN query problems is the order-sensitive moving kNN (order-sensitive MkNN) query problem. In the order-sensitive MkNN query problem, the query point is dynamic and unpredictable, the kNN answers should be responded in real time and sorted by the distance in the ascending order. Therefore, how to respond the kNN answers effectively, incrementally and correctly is an important issue. Nutanong et al: have proposed the V*-kNN algorithm to process the order-sensitive MkNN query. The V*-kNN algorithm uses their the V*-diagram algorithm to generate the safe region. It also uses the Incremental Rank Updates algorithm (IRU) to handle the events while the query point passing the bisectors or the boundary of the safe region. However, the V*-kNN algorithm uses the BF-kNN algorithm to retrieve NNs, which is non-incremental. This makes the search time increase while the density of the object increases. Moreover, they do not consider the situation that there are multiple objects at the same order, and the situation that there are multiple events happen in a single step. These situations may cause that the kNN answers are incorrect. Therefore, in this thesis, we propose the Hilbert curve-based kNN algorithm (HC-kNN) algorithm to process the ordersensitive MkNN query. The HC-kNN algorithm can handle the situation that there are multiple events happen in a single step. We also propose new data structure of the kNN answers. Next, we propose the Intersection of Perpendicular Bisectors algorithm (IPB) in order to handle order update events of the kNN answers. The IPB algorithm handles the situation which there are multiple objects at the same order. Finally, based on the Hilbert curve index, we propose the ONHC-kNN algorithm to get NNs incrementally and to generate the safe region. The safe region will not be affected while the density of the object increases. The safe region of our algorithm is larger than that of the V*-kNN algorithm. From our simulation result, we show that the HC-kNN algorithm provides better performance than the V*-kNN algorithm.

Queries on spatial data commonly concern a certain range or area, for example, queries related to intersections, containment and nearest neighbors. The Continuous Nearest Neighbor (CNN) query is one kind of the nearest neighbor queries. For example, people may want to know where those gas stations are along the super highway from the starting position to the ending position. Due to that there is no total ordering of spatial proximity among spatial objects, the space filling curve (SFC) approach has proposed to preserve the spatial locality. Chen and Chang have proposed efficient algorithms based on SFC to answer nearest neighbor queries, so we may perform a sequence of individually nearest neighbor queries to answer such a CNN query in the centralized system by one of Chen and Chang's algorithms. However, each searched range of these nearest neighbor queries could be overlapped, and these queries may access several same pages on the disk, resulting in many redundant disk accesses. On the other hand, Zheng et al. have proposed an algorithm based on the Hilbert curve for the CNN query for the wireless broadcast environment, and it contains two phases. In the first phase, Zheng et al.'s algorithm designs a searched range to find candidate objects. In the second phase, it uses some heuristics to filter the candidate objects for the final answer. However, Zheng et al.'s algorithm may check some data blocks twice or some useless data blocks, resulting in some redundant disk accesses. Therefore, in this thesis, to avoid these disadvantages in the first phase of Zheng et al.'s algorithm, we propose a local expansion approach based on the Peano curve for the CNN query in the centralized system. In the first phase, we determine the searched range to obtain all candidate objects. Basically, we first calculate the route between the starting point and the ending point. Then, we move forward one block from the starting point to the ending point, and locally spread the searched range to find the candidate objects. In the second phase, we use heuristics mentioned in Zheng et al.'s algorithm to filter the candidate objects for the final answer. Based on such an approach, we proposed two algorithms: the forward moving (FM) algorithm and the forward moving* (FM*) algorithm. The FM algorithm assumes that each object is in the center of a block, and the FM* algorithm assumes that each object could be in any place of a block. Our local expansion approach can avoid the duplicated check in Zheng et al.'s algorithm, and determine a searched range with higher accuracy than that of Zhenget al.'s algorithm. From our simulation results, we show that the performance of the FM or FM* algorithm is better than that of Zheng et al.'s algorithm, in terms of the accuracy and the processing time.

<p>Real-time strategy (RTS) games constitute one of the largest game genres today and have done so for the past decade. A central feature of real-time strategy games is opponent AI which is suggestively the “last frontier” of game development because the focus of research has primarily been on other components, graphics in particular. This has led to AI research being largely ignored within the commercial game industry but several methods have recently been suggested for improving the strategic ability of AI agents in real-time strategy games.</p><p>The aim of this project is to evaluate how a method called qualitative spatial reasoning can improve AI on a tactical level in a selected RTS game. An implementation of an AI agent that uses qualitative spatial reasoning has been obtained and an evaluation of its performance in an RTS game example monitored and analysed.</p><p>The study has shown that qualitative spatial reasoning affects AI agent’s behaviour significantly and indicates that it can be used to deduce a rule-base that increases the unpredictability and performance of the agent.</p>

Web technologies are becoming increasingly useful with new features and the gap to native apps is narrowing. Recently, IndexedDB was added to the web standard to provide large scale storage solutions directly in the browser. Is it performant enough to be used with a spatial database? In this study, such a system is developed for Foran Sverige AB and we learn that IndexedDB indeed can be used for this purpose. Even storage demanding geospatial applications can be developed as a multi- platform system with a single codebase, all while broadening the possible audience reach by avoiding an app installation process.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-07-01T11:58:46Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertacao_Jones_final.pdf: 3458864 bytes, checksum: 62e534b2f1fc2266364c4ce46ccf020a (MD5) / Made available in DSpace on 2016-07-01T11:58:46Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertacao_Jones_final.pdf: 3458864 bytes, checksum: 62e534b2f1fc2266364c4ce46ccf020a (MD5) Previous issue date: 2015-08-28 / FACEPE / Model-Driven Development (MDD) é um paradigma que usa modelos como o principal artefato no processo de desenvolvimento de sistemas. Isto é, neste paradigma, modelos não são apenas artefatos de documentação, pois devem corresponder a códigos executáveis. Em projetos de Banco de Dados Espaciais (BDE), existem várias linguagens de modelagens (e.g., OMT-G, MADS, GeoProfile e UML-GeoFrame), as quais permitem representar características espaciais (e.g., Múltiplas Representações e Relacionamento Espacial) por meio de uma ferramenta do tipo Computer-Aided Software Engineering (CASE). Embora essas linguagens sejam bem exploradas e difundidas na literatura, constatou-se que estas têm deficiências para modelar e implementar integridade espacial a partir de entidades com múltiplas representações. Assim, de modo a avançar o estado da arte sobre projeto de BDE, este trabalho faz uma análise dos principais trabalhos relacionados, e, de modo a contribuir para superar as deficiências encontradas, propõe uma extensão espacial para a linguagem de modelagem Enhanced Entity Relationship (EER). Essa extensão é implementada na ferramenta EERCASE e avalidada por meio de uma análise comparativa com os principais trabalhos relacionados, evidenciando seus pontos fortes e fracos. / Model-Driven Development (MDD) is a paradigm that uses models as the primary artifact in the systems development process. That is, in this paradigm, models are not only documentation artifacts, since these should be the executable code. In Spatial Databases projects (SDB), there are several modeling languages (e.g., OMT-G, MADS, GeoProfile and UML-GeoFrame), which allow to represent spatial characteristics (e.g., Multiple Representations and Spatial Relationship) by means of a tool type Computer-Aided Software Engineering (CASE). Although these languages are better exploited and widespread in the literature, it was found that they have deficiencies in modeling and implement spatial integrity from entities with multiple representations. Thus, in order to advance the state of the art in SDB project, this paper analyzes the main works related and, in order to contribute to overcome the deficiencies, proposes a spatial extension for modeling language Enhanced Entity Relationship (EER). This extension is implemented in tool EERCASE and evaluated through a comparison with the main work related, highlighting their strengths and weaknesses.

O termo de Banco de Dados Espacial tem sido usado frequentemente nas aplicações relativas a Sistemas de Informações Geográficas. Isto demonstra a aplicação de uma ferramenta computacional poderosa (Sistemas de Gerenciamento de Banco de Dados) na solução de problemas de Planejamento Urbano e Regional. Para que o Banco de Dados possa operar necessita uma Fonte de Dados ou Base de Dados que alimente a sua estrutura e garanta a qualidade e precisão dessas informações. Daí em diante se podeaplicar as suas próprias capacidades na solução dos problemas dos seus usuários. Reduzindo a escala podemos traçar um paralelo entre os Projetos de Planejamento Urbano e em Projeto de Edificações, logo as mesmas ferramentas podem ser utilizadas neste segundo tipo de projetos. Preparando caminho à utilização destas ferramentas, este trabalho relata o caminho escolhido, a implementação e a aplicação de uma abordagem de formação de Base de Dados Espaciais a partir do projeto de Edificações. / Spatial database is a term frequently used in Geographic Information Systems applications. This means the use of a powerful computing tool (Database Management System) in solving Regional and Urban Planning problems. A Database Management System needs a Data Source or Base of Data for the Data entry and quality and precise information guaranteening. Then we can use its own capacities in the solution of the problems of the users. Reducing the scale we can make a parallel between Urban Planning Design and Building Design and the same tools can be used for the second. Preparing the way of the utilization of these tools, this work speak about the choosing way, the implementation and an approach application of Base of Data formation from Building Design.

ABSTRACT A REAL TIME INDOOR NAVIGATION AND MONITORING SYSTEM FOR FIREFIGHTERS AND VISUALLY IMPAIRED MAY 2011 SIDDHESH RAJAN GANDHI M.S. E.C.E, UNIVERSITY OF MASSACHUSETTS AMHERST Directed by: Professor Aura Ganz There has been a widespread growth of technology in almost every facet of day to day life. But there are still important application areas in which technology advancements have not been implemented in a cost effective and user friendly manner. Such applications which we will address in this proposal include: 1) indoor localization and navigation of firefighters during rescue operations and 2) indoor localization and navigation for the blind and visually impaired population. Firefighting is a dangerous job to perform as there can be several unexpected hazards while rescuing victims. Since the firefighters do not have any knowledge about the internal structure of the fire ridden building, they will not be able to find the location of the EXIT door, a fact that can prove to be fatal. We introduce an indoor location tracking and navigation system (FIREGUIDE) using RFID technology integrated with augmented reality. FIREGUIDE assists the firefighters to find the nearest exit location by providing the navigation instructions to the exits as well as an Augmented Reality view of the location and direction of the exits. The system also presents the Incident Commander the current firefighter’s location superimposed on a map of the building floor. We envision that the FIREGUIDE system will save a significant number of firefighters and victims’ lives. Blind or visually impaired people find it difficult to navigate independently in both outdoor and indoor environments. The outdoor navigation problem can be solved by using systems that have GPS support. But indoor navigation systems for the blind or visually impaired are still a challenge to conquer, given the requirements of low cost and user friendly operation. In order to enhance the perception of indoor and unfamiliar environments for the blind and visually-impaired, as well as to aid in their navigation through such environments, we propose a novel approach that provides context–aware navigation services. INSIGHT uses RFID (Radio Frequency Identification), and tagged spaces (audio landmarks), enabling a ubiquitous computing system with contextual awareness of its users while providing them persistent and context-aware information. We present INSIGHT system that supports a number of unique features such as: a) Low deployment and maintenance cost; b) Scalability, i.e. we can deploy the system in very large buildings; c) An on-demand system that does not overwhelm the user, as it offers small amounts of information on demand; and d) Portability and ease-of-use, i.e., the custom handheld device carried by the user is compact and instructions are received audibly.

Satellite imagery research used to be an expensive research topic for companies and organizations due to the limited data and compute resources. As the computing power and storage capacity grows exponentially, a large amount of aerial and satellite images are generated and analyzed everyday for various applications. Current technological advancement and extensive data collection by numerous Internet of Things (IOT) devices and platforms have amplified labeled natural images. Such data availability catalyzed the development and performance of current state-of-the-art image classification and cross-modal models. Despite the abundance of publicly available remote sensing images, very few remote sensing (RS) images are labeled and even fewer are multi-captioned.These scarcities limit the scope of fine tuned state of the art models to at most 38 classes, based on the PatternNet data, one of the largest publicly available labeled RS data. Recent state-of-the art image-to-image retrieval and detection models in RS have shown great results. Because the text-to-image retrieval of RS images is still emerging, it still faces some challenges in the retrieval of those images.These challenges are based on the inaccurate retrieval of image categories that were not present in the training dataset and the retrieval of images from descriptive input. Motivated by those shortcomings in current cross-modal remote sensing image retrieval, we proposed CLIP-RS, a cross-modal remote sensing image retrieval platform. Our proposed framework CLIP-RS is a framework that combines a fine-tuned implementation of a recent state of the art cross-modal and text-based image retrieval model, Contrastive Language Image Pre-training (CLIP) and FAISS (Facebook AI similarity search), a library for efficient similarity search. Our implementation is deployed on a Web App for inference task on text-to-image and image-to-image retrieval of RS images collected via the Mapbox GL JS API. We used the free tier option of the Mapbox GL JS API and took advantage of its raster tiles option to locate the retrieved results on a local map, a combination of the downloaded raster tiles. Other options offered on our platform are: image similarity search, locating an image in the map, view images' geocoordinates and addresses.In this work we also proposed two remote sensing fine-tuned models and conducted a comparative analysis of our proposed models with a different fine-tuned model as well as the zeroshot CLIP model on remote sensing data. / Master of Science / Satellite imagery research used to be an expensive research topic for companies and organizations due to the limited data and compute resources. As the computing power and storage capacity grows exponentially, a large amount of aerial and satellite images are generated and analyzed everyday for various applications. Current technological advancement and extensive data collection by numerous Internet of Things (IOT) devices and platforms have amplified labeled natural images. Such data availability catalyzed the devel- opment and performance of current state-of-the-art image classification and cross-modal models. Despite the abundance of publicly available remote sens- ing images, very few remote sensing (RS) images are labeled and even fewer are multi-captioned.These scarcities limit the scope of fine tuned state of the art models to at most 38 classes, based on the PatternNet data,one of the largest publicly avail- able labeled RS data.Recent state-of-the art image-to-image retrieval and detection models in RS have shown great results. Because the text-to-image retrieval of RS images is still emerging, it still faces some challenges in the re- trieval of those images.These challenges are based on the inaccurate retrieval of image categories that were not present in the training dataset and the re- trieval of images from descriptive input. Motivated by those shortcomings in current cross-modal remote sensing image retrieval, we proposed CLIP-RS, a cross-modal remote sensing image retrieval platform.Cross-modal retrieval focuses on data retrieval across different modalities and in the context of this work, we focus on textual and imagery modalities.Our proposed frame- work CLIP-RS is a framework that combines a fine-tuned implementation of a recent state of the art cross-modal and text-based image retrieval model, Contrastive Language Image Pre-training (CLIP) and FAISS (Facebook AI similarity search), a library for efficient similarity search. In deep learning, the concept of fine tuning consists of using weights from a different model or algorithm into a similar model with different domain specific application. Our implementation is deployed on a Web Application for inference tasks on text-to-image and image-to-image retrieval of RS images collected via the Mapbox GL JS API. We used the free tier option of the Mapbox GL JS API and took advantage of its raster tiles option to locate the retrieved results on a local map, a combination of the downloaded raster tiles. Other options offered on our platform are: image similarity search, locating an image in the map, view images' geocoordinates and addresses.In this work we also pro- posed two remote sensing fine-tuned models and conducted a comparative analysis of our proposed models with a different fine-tuned model as well as the zeroshot CLIP model on remote sensing data. Detection models in RS have shown great results. Because the text-to-image retrieval of RS images is still emerging, it still faces some challenges in the re- trieval of those images.These challenges are based on the inaccurate retrieval of image categories that were not present in the training dataset and the re- trieval of images from descriptive input. Motivated by those shortcomings in current cross-modal remote sensing image retrieval, we proposed CLIP-RS, a cross-modal remote sensing image retrieval platform.Cross-modal retrieval focuses on data retrieval across different modalities and in the context of this work, we focus on textual and imagery modalities.Our proposed frame- work CLIP-RS is a framework that combines a fine-tuned implementation of a recent state of the art cross-modal and text-based image retrieval model, Contrastive Language Image Pre-training (CLIP) and FAISS (Facebook AI similarity search), a library for efficient similarity search. In deep learning, the concept of fine tuning consists of using weights from a different model or algorithm into a similar model with different domain specific application. Our implementation is deployed on a Web Application for inference tasks on text-to-image and image-to-image retrieval of RS images collected via the Mapbox GL JS API. We used the free tier option of the Mapbox GL JS API and took advantage of its raster tiles option to locate the retrieved results on a local map, a combination of the downloaded raster tiles. Other options offered on our platform are: image similarity search, locating an image in the map, view images' geocoordinates and addresses.In this work we also pro- posed two remote sensing fine-tuned models and conducted a comparative analysis of our proposed models with a different fine-tuned model as well as the zeroshot CLIP model on remote sensing data.