Voronoi classfied and clustered constellation data structure for three-dimensional urban buildings

In the past few years, the growth of urban area has been increasing and has resulted immense number of urban datasets. This situation contributes to the difficulties in handling and managing issues related to urban area. Huge and massive datasets can degrade the performance of data retrieval and information analysis. In addition, urban environments are very difficult to manage because they involved with various types of data, such as multiple types of zoning themes in urban mixeduse development. Thus, a special technique for efficient data handling and management is necessary. In this study, a new three-dimensional (3D) spatial access method, the Voronoi Classified and Clustered Data Constellation (VOR-CCDC) is introduced. The VOR-CCDC data structure operates on the basis of two filters, classification and clustering. To boost up the performance of data retrieval, VORCCDC offers a minimal percentage of overlap among nodes and a minimal coverage area in order to avoid repetitive data entry and multi-path queries. Besides that, VOR-CCDC data structure is supplemented with an extra element of nearest neighbour information. Encoded neighbouring information in the Voronoi diagram allows VOR-CCDC to optimally explore the data. There are three types of nearest neighbour queries that are presented in this study to verify the VOR-CCDC’s ability in finding the nearest neighbour information. The queries are Single Search Nearest Neighbour query, k Nearest Neighbour (kNN) query and Reverse k Nearest Neighbour (RkNN) query. Each query is tested with two types of 3D datasets; single layer and multi-layer. The test demonstrated that VOR-CCDC performs the least amount of input/output than their best competitor, the 3D R-Tree. Besides that, VOR-CCDC is also tested for performance evaluation. The results indicate that VOR-CCDC outperforms its competitor by responding 60 to 80 percent faster to the query operation. In the future, VOR-CCDC structure is expected to be expanded for temporal and dynamic objects. Besides that, VOR-CCDC structure can also be used in other applications such as brain cell database for analysing the spatial arrangement of neurons or analysing the protein chain reaction in bioinformatics applications

From data acquisition to data fusion : a comprehensive review and a roadmap for the identification of activities of daily living using mobile devices

This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user’s daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs)

Service Abstractions for Scalable Deep Learning Inference at the Edge

Deep learning driven intelligent edge has already become a reality, where millions of mobile, wearable, and IoT devices analyze real-time data and transform those into actionable insights on-device. Typical approaches for optimizing deep learning inference mostly focus on accelerating the execution of individual inference tasks, without considering the contextual correlation unique to edge environments and the statistical nature of learning-based computation. Specifically, they treat inference workloads as individual black boxes and apply canonical system optimization techniques, developed over the last few decades, to handle them as yet another type of computation-intensive applications. As a result, deep learning inference on edge devices still face the ever increasing challenges of customization to edge device heterogeneity, fuzzy computation redundancy between inference tasks, and end-to-end deployment at scale. In this thesis, we propose the first framework that automates and scales the end-to-end process of deploying efficient deep learning inference from the cloud to heterogeneous edge devices. The framework consists of a series of service abstractions that handle DNN model tailoring, model indexing and query, and computation reuse for runtime inference respectively. Together, these services bridge the gap between deep learning training and inference, eliminate computation redundancy during inference execution, and further lower the barrier for deep learning algorithm and system co-optimization. To build efficient and scalable services, we take a unique algorithmic approach of harnessing the semantic correlation between the learning-based computation. Rather than viewing individual tasks as isolated black boxes, we optimize them collectively in a white box approach, proposing primitives to formulate the semantics of the deep learning workloads, algorithms to assess their hidden correlation (in terms of the input data, the neural network models, and the deployment trials) and merge common processing steps to minimize redundancy

The Design and Use of a Smartphone Data Collection Tool and Accompanying Configuration Language

Understanding human behaviour is key to understanding the spread of epidemics, habit dispersion, and the efficacy of health interventions. Investigation into the patterns of and drivers for human behaviour has often been facilitated by paper tools such as surveys, journals, and diaries. These tools have drawbacks in that they can be forgotten, go unfilled, and depend on often unreliable human memories. Researcher-driven data collection mechanisms, such as interviews and direct observation, alleviate some of these problems while introducing others, such as bias and observer effects. In response to this, technological means such as special-purpose data collection hardware, wireless sensor networks, and apps for smart devices have been built to collect behavioural data. These technologies further reduce the problems experienced by more traditional behavioural research tools, but often experience problems of reliability, generality, extensibility, and ease of configuration. This document details the construction of a smartphone-based app designed to collect data on human behaviour such that the difficulties of traditional tools are alleviated while still addressing the problems faced by modern supplemental technology. I describe the app's main data collection engine and its construction, architecture, reliability, generality, and extensibility, as well as the programming language developed to configure it and its feature set. To demonstrate the utility of the tool and its configuration language, I describe how they have been used to collect data in the field. Specifically, eleven case studies are presented in which the tool's architecture, flexibility, generality, extensibility, modularity, and ease of configuration have been exploited to facilitate a variety of behavioural monitoring endeavours. I further explain how the engine performs data collection, the major abstractions it employs, how its design and the development techniques used ensure ongoing reliability, and how the engine and its configuration language could be extended in the future to facilitate a greater range of experiments that require behavioural data to be collected. Finally, features and modules of the engine's encompassing system, iEpi, are presented that have not otherwise been documented to give the reader an understanding of where the work fits into the larger data collection and processing endeavour that spawned it

Knowledge-Based Decision Support for Integrated Water Resources Management with an application for Wadi Shueib, Jordan

This book takes a two-staged approach to contribute to the contemporary Integrated Water Resources Management (IWRM) research. First it investigates sub-basin-scale IWRM modelling and scenario planning. The Jordanian Wadi Shueib is used as exemplary case study. Then, it develops a framework to collaboratively manage planning and decision making knowledge on the basis of semantic web technologies. Future IWRM initiatives can benefit from the valuable insights achieved in the presented study

Efficient Distance Join Query Processing in Distributed Spatial Data Management Systems

Due to the ubiquitous use of spatial data applications and the large amounts of such data these applications use, the processing of large-scale distance joins in distributed systems is becoming increasingly popular. Distance Join Queries (DJQs) are important and frequently used operations in numerous applications, including data mining, multimedia and spatial databases. DJQs (e.g., k Nearest Neighbor Join Query, k Closest Pair Query, ε Distance Join Query, etc.) are costly operations, since they involve both the join and distance-based search, and performing DJQs efficiently is a challenging task. Recent Big Data developments have motivated the emergence of novel technologies for distributed processing of large-scale spatial data in clusters of computers, leading to Distributed Spatial Data Management Systems (DSDMSs). Distributed cluster-based computing systems can be classified as Hadoop-based or Spark-based systems. Based on this classification, in this paper, we compare two of the most recent and leading DSDMSs, SpatialHadoop and LocationSpark, by evaluating the performance of several existing and newly proposed parallel and distributed DJQ algorithms under various settings with large spatial real-world datasets. A general conclusion arising from the execution of the distributed DJQ algorithms studied is that, while SpatialHadoop is a robust and efficient system when large spatial datasets are joined (since it is built on top of the mature Hadoop platform), LocationSpark is the clear winner in total execution time efficiency when medium spatial datasets are combined (due to in-memory processing provided by Spark). However, LocationSpark requires higher memory allocation when large spatial datasets are involved in DJQs (even more so when k and ε are large). Finally, this detailed performance study has demonstrated that the new distributed DJQ algorithms we have proposed are efficient, robust and scalable with respect to different parameters, such as dataset sizes, k, ε and number of computing nodes

What'sup: a mobile application for searching ongoing cultural events

Trabalho de projecto de mestrado em Engenharia Informática (Sistemas de Informação), apresentado à Universidade de Lisboa, através da Faculdade de Ciências, 2012Hoje em dia, não existem muitas aplicações móveis de turismo em Portugal, orientadas para a organização e calendarização de eventos culturais. Para além disso, a área do turismo doméstico (turismo feito dentro do próprio país por residentes desse país) em Portugal tem muito potencial e, face à actual situação do país, pode representar uma alternativa fiável e financeiramente mais agradável. De qualquer forma, a aplicação que se pretende desenvolver não tem como alvo o turismo tradicional (visita de museus, monumentos e espaços verdes) mais orientado ao local, em que se assiste a eventos que são previsíveis em termos de calendarização e que acontecem regularmente ao longo do tempo, sempre nesses locais. Em vez disso, apostamos na descoberta de eventos dentro do âmbito doo turismo urbano, de curta duração e num contexto mais ad-hoc (sem um planeamento prévio muito aprofundado ou sem um conhecimento prévio do local onde nos encontramos), tendo maioritariamente como objectivo o entretenimento, como por exemplo a participação em festivais, concertos, eventos de cinema, festas, exposições, entre outros. O nosso foco é, portanto, um turismo mais orientado aos eventos, mais dinâmico. O nosso utilizador-alvo procura idealmente eventos que estejam a decorrer nesse momento ou que decorram no próprio dia. Também é importante salientar que decidimos utilizar ao máximo as tecnologias daWeb Semântica que têm emergido nos últimos anos. Este factor mostra que a nossa aplicação é baseada numa abordagem inovadora e que tem grandes potencialidades dentro da área. Deste modo, este projecto, chamado What’sUp tem como objectivo o desenvolvimento de uma aplicação móvel, destinada a funcionar em dispositivos Android, que possa indicar ao utilizador eventos culturais existentes no local onde este se encontra (e nas redondezas), que decorram num dado momento ou intervalo de tempo. Estes eventos são apresentados ao utilizador através da análise da linguagem natural que é introduzida pelo mesmo, quando este procura por eventos na sua área circundante. O utilizador pode colocar perguntas à aplicação (através de introdução de texto) do tipo "O que há de teatro agora?”, ”Que concertos vai haver hoje à noite?”ou mesmo ”Que exposições posso visitar às 10 horas”. A aplicação analisa a pergunta do utilizador, através de um sistema de palavras-chave e transforma-a numa query na linguagem de pesquisa SPARQL, que é executada sobre uma ontologia, que serve como base de dados da nossa aplicação. Esta ontologia de eventos culturais, escrita na linguagem OWL 2 (baseada em RDF), permite nos representar a informação dos eventos com um detalhe muito elevado. O resultado da query é a lista de eventos culturais, correspondente à pergunta do utilizador. No entanto, para que este processo se concretize, há uma série de tarefas que são executadas antes disso. Quando a informação sobre os eventos ´e armazenada na ontologia, já sofreu um conjunto de alterações e passou por várias fases: primeiramente, os eventos são extraídos de um conjunto predefinido de sítios Web apropriados (com informação sobre eventos culturais em Lisboa e arredores) existentes na Internet, utilizando web scrapers para tal. Estes web scrapers possuem uma grande flexibilidade, já que os web sites de onde a informação é extraída estão estruturados de formas diferentes e mostram informações diferentes sobre os eventos culturais. Por isso mesmo, é necessário adaptar os web scrapers para que se enquadrem com a estrutura de navegação de cada site, com o objectivo de extrair a informação correctamente em cada caso. ´E extraído conteúdo digital (em forma de texto) relevante sobre cada evento publicado nos documentos HTML das agendas on-line, previamente seleccionadas. Essa informação é guardada num ficheiro do tipo CSV (Comma-Separated Values). O ficheiro é lido por uma aplicação Java, que faz ligeiras alterações às expressões (sejam elas sobre a data, categoria ou preço do evento) e executa sobre cada expressão extraída, a função apropriada da gramática construída em Prolog, baseada no conceito de gramática livre de contexto, também chamada de gramática de cláusulas definidas. Esta gramática é constituída por um conjunto de regras que permite avaliar expressões de linguagem natural com certas características. Assim, são extraídas as características relevantes das expressões. As propriedades do evento são definidas através do tipo de retorno que estas funções devolvem. De seguida são criadas instancias desses eventos na ontologia, com as suas respectivas características, como o nome do evento, a sua categoria, data, preço e local. Isto ´e concretizado através do uso da framework para Java, Jena API, que nos permite editar a ontologia directamente (sem aceder a uma ferramenta de edição de ontologias) e ter controlo sobre o seu conteúdo. Todos estes elementos formam uma base de dados de eventos devidamente catalogados e organizados, que pode ser pesquisada, devolvendo os resultados esperados, depois do utilizador fazer a sua pesquisa, através de um input de texto na aplicação. A localização do utilizador é sempre tida em conta, através dos dados da sua geolocalização, retirados do dispositivo móvel (por exemplo, do sistema de Global Positioning System, conhecido por GPS). Com estes dados, a aplicação consegue calcular quais os eventos que decorrem em locais que se encontram mais perto do utilizador e apresentar essa informação. Assim, a aplicação devolve um resultado ou conjunto de resultados que correspondam à pesquisa do utilizador e permite ao utilizador aceder a toda a informação sobre cada evento, incluindo a sua geolocalização no mapa da aplicação e a distância a cada evento. O utilizador faz as suas pesquisas através de linguagem natural, o que é vantajoso para o próprio. Esta abordagem permite ao utilizador evitar uma pesquisa por parâmetros e demasiado complicada, que exigiria da sua parte uma maior carga cognitiva, em termos de utilização da aplicação e também em termos de conhecimento dos principais locais e atractivos turísticos da zona onde se encontra. Deste modo, o utilizador não necessita de saber o nome do evento nem o local onde este acontece para o encontrar. Apenas tem que fazer uma pergunta que o leve a obter os resultados para descobrir que eventos estão a decorrer no momento. Para que a informação sobre eventos seja renovada, os web scrapers são executados periodicamente para actualizar a base de dados de eventos. Este processo é, portanto, automático e invisível para o utilizador. A arquitectura da aplicação está dividida em vários módulos, tendo cada um a sua função e sendo fundamental para o funcionamento de todo o processo. Procurámos ter uma arquitectura modular, em que cada módulo é relativamente independente e, consequentemente, pode ser estendido individualmente quando for necessário. A dissecação da arquitectura é feita na secção do trabalho realizado. A interface final da aplicação tem um estilo simples e minimalista, onde, num primeiro nível, o utilizador faz um input de texto e depois explora os resultados da sua pesquisa. O utilizador pode ainda alterar algumas definições na sua pesquisa, a partir da aplicação, como por exemplo, o raio geográfico da procura (500 metros, 2000 metros, etc). Desta forma, este projecto visa oferecer alternativas para os turistas ocasionais, que queiram descobrir a cultura da cidade. Para isso, esta aplicação fornece toda a informação de que estes necessitam para se orientarem nas suas aventuras turísticas e culturais.What’sUp is a project which focuses on the development of a tourism and culture oriented context-based mobile application that helps the user to explore local cultural offers. The goal is to inform and to lead the user about the ongoing and upcoming cultural events in the user geographical area, without the need to specifically state his geographic location or the exact date/time of the intended event. We built a Web ontology in OWL 2, which contains four types of information about the cultural events: When, What, Where and How Much. This event data is scattered across on-line agendas and e-magazines on the Web. We periodically extract the most important content about the events, to keep our event information updated. This information is then refined and inserted in the web ontology, using web scraping methods and a Definite Clause Grammar. Each user question is made in natural language (through text input) and is after transformed in a SPARQL query that runs in the OWL 2 database. The result of that query is a list of cultural events, that is then presented to the user. What’sUp can greatly improve the access to context-based information in digital cities, through the use of natural language interaction and Semantic Web technologies

Data Management for Dynamic Multimedia Analytics and Retrieval

Multimedia data in its various manifestations poses a unique challenge from a data storage and data management perspective, especially if search, analysis and analytics in large data corpora is considered. The inherently unstructured nature of the data itself and the curse of dimensionality that afflicts the representations we typically work with in its stead are cause for a broad range of issues that require sophisticated solutions at different levels. This has given rise to a huge corpus of research that puts focus on techniques that allow for effective and efficient multimedia search and exploration. Many of these contributions have led to an array of purpose-built, multimedia search systems. However, recent progress in multimedia analytics and interactive multimedia retrieval, has demonstrated that several of the assumptions usually made for such multimedia search workloads do not hold once a session has a human user in the loop. Firstly, many of the required query operations cannot be expressed by mere similarity search and since the concrete requirement cannot always be anticipated, one needs a flexible and adaptable data management and query framework. Secondly, the widespread notion of staticity of data collections does not hold if one considers analytics workloads, whose purpose is to produce and store new insights and information. And finally, it is impossible even for an expert user to specify exactly how a data management system should produce and arrive at the desired outcomes of the potentially many different queries. Guided by these shortcomings and motivated by the fact that similar questions have once been answered for structured data in classical database research, this Thesis presents three contributions that seek to mitigate the aforementioned issues. We present a query model that generalises the notion of proximity-based query operations and formalises the connection between those queries and high-dimensional indexing. We complement this by a cost-model that makes the often implicit trade-off between query execution speed and results quality transparent to the system and the user. And we describe a model for the transactional and durable maintenance of high-dimensional index structures. All contributions are implemented in the open-source multimedia database system Cottontail DB, on top of which we present an evaluation that demonstrates the effectiveness of the proposed models. We conclude by discussing avenues for future research in the quest for converging the fields of databases on the one hand and (interactive) multimedia retrieval and analytics on the other

Multimodal Content Delivery for Geo-services

This thesis describes a body of work carried out over several research projects in the area of multimodal interaction for location-based services. Research in this area has progressed from using simulated mobile environments to demonstrate the visual modality, to the ubiquitous delivery of rich media using multimodal interfaces (geo- services). To effectively deliver these services, research focused on innovative solutions to real-world problems in a number of disciplines including geo-location, mobile spatial interaction, location-based services, rich media interfaces and auditory user interfaces. My original contributions to knowledge are made in the areas of multimodal interaction underpinned by advances in geo-location technology and supported by the proliferation of mobile device technology into modern life. Accurate positioning is a known problem for location-based services, contributions in the area of mobile positioning demonstrate a hybrid positioning technology for mobile devices that uses terrestrial beacons to trilaterate position. Information overload is an active concern for location-based applications that struggle to manage large amounts of data, contributions in the area of egocentric visibility that filter data based on field-of-view demonstrate novel forms of multimodal input. One of the more pertinent characteristics of these applications is the delivery or output modality employed (auditory, visual or tactile). Further contributions in the area of multimodal content delivery are made, where multiple modalities are used to deliver information using graphical user interfaces, tactile interfaces and more notably auditory user interfaces. It is demonstrated how a combination of these interfaces can be used to synergistically deliver context sensitive rich media to users - in a responsive way - based on usage scenarios that consider the affordance of the device, the geographical position and bearing of the device and also the location of the device