Mixed spatial and nonspatial problems in location based services

With hundreds of millions of users reporting locations and embracing mobile technologies, Location Based Services (LBSs) are raising new challenges. In this dissertation, we address three emerging problems in location services, where geolocation data plays a central role. First, to handle the unprecedented growth of generated geolocation data, existing location services rely on geospatial database systems. However, their inability to leverage combined geographical and textual information in analytical queries (e.g. spatial similarity joins) remains an open problem. To address this, we introduce SpsJoin, a framework for computing spatial set-similarity joins. SpsJoin handles combined similarity queries that involve textual and spatial constraints simultaneously. LBSs use this system to tackle different types of problems, such as deduplication, geolocation enhancement and record linkage. We define the spatial set-similarity join problem in a general case and propose an algorithm for its efficient computation. Our solution utilizes parallel computing with MapReduce to handle scalability issues in large geospatial databases. Second, applications that use geolocation data are seldom concerned with ensuring the privacy of participating users. To motivate participation and address privacy concerns, we propose iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. iSafe combines geolocation data extracted from crime datasets and geosocial networks such as Yelp. In order to enhance iSafe's ability to compute safety recommendations, even when crime information is incomplete or sparse, we need to identify relationships between Yelp venues and crime indices at their locations. To achieve this, we use SpsJoin on two datasets (Yelp venues and geolocated businesses) to find venues that have not been reviewed and to further compute the crime indices of their locations. Our results show a statistically significant dependence between location crime indices and Yelp features. Third, review centered LBSs (e.g., Yelp) are increasingly becoming targets of malicious campaigns that aim to bias the public image of represented businesses. Although Yelp actively attempts to detect and filter fraudulent reviews, our experiments showed that Yelp is still vulnerable. Fraudulent LBS information also impacts the ability of iSafe to provide correct safety values. We take steps toward addressing this problem by proposing SpiDeR, an algorithm that takes advantage of the richness of information available in Yelp to detect abnormal review patterns. We propose a fake venue detection solution that applies SpsJoin on Yelp and U.S. housing datasets. We validate the proposed solutions using ground truth data extracted by our experiments and reviews filtered by Yelp.

A Fuzzy Preprocessing Module for Optimizing the Access Network Selection in Wireless Networks

A heterogeneous wireless network is characterized by the presence of different wireless access technologies that coexist in an overlay fashion. These wireless access technologies usually differ in terms of their operating parameters. On the other hand, Mobile Stations (MSs) in a heterogeneous wireless network are equipped with multiple interfaces to access different types of services from these wireless access technologies. The ultimate goal of these heterogeneous wireless networks is to provide global connectivity with efficient ubiquitous computing to these MSs based on the Always Best Connected (ABC) principle. This is where the need for intelligent and efficient Vertical Handoffs (VHOs) between wireless technologies in a heterogeneous environment becomes apparent. This paper presents the design and implementation of a fuzzy multicriteria based Vertical Handoff Necessity Estimation (VHONE) scheme that determines the proper time for VHO, while considering the continuity and quality of the currently utilized service, and the end-users' satisfaction.

Mining the online social network data: Influence, summarization, and organization

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.

Simulation and analysis of network traffic for efficient and reliable information transfer

With the growing commercial importance of the Internet and the development of new real-time, connection-oriented services like IP-telephony and electronic commerce resilience is becoming a key issue in the design of TP-based networks. Two emerging technologies, which can accomplish the task of efficient information transfer, are Multiprotocol Label Switching (MPLS) and Differentiated Services. A main benefit of MPLS is the ability to introduce traffic-engineering concepts due to its connection-oriented characteristic. With MPLS it is possible to assign different paths for packets through the network. Differentiated services divides traffic into different classes and treat them differently, especially when there is a shortage of network resources. In this thesis, a framework was proposed to integrate the above two technologies and its performance in providing load balancing and improving QoS was evaluated. Simulation and analysis of this framework demonstrated that the combination of MPLS and Differentiated services is a powerful tool for QoS provisioning in IP networks.

30 years of upper air soundings on board of R/V POLARSTERN (1982-12-29 to 2012-11-25)

The research vessel and supply icebreaker POLARSTERN is the flagship of the Alfred-Wegener-Institut in Bremerhaven (Germany) and one of the infrastructural pillars of German Antarctic research. Since its commissioning in 1982, POLARSTERN has conducted 30 campaigns to Antarctica (157 legs, mostly austral summer), and 29 to the Arctic (94 legs, northern summer). Usually, POLARSTERN is more than 300 days per year in operation and crosses the Atlantic Ocean in a meridional section twice a year. The first radiosonde on POLARSTERN was released on the 29th of December 1982, two days after POLARSTERN started on its maiden voyage to the Antarctic. And these daily soundings have continued up to the present. Due to the fact that POLARSTERN has reliably and regularly been providing upper air observations from data sparse regions (oceans and polar regions), the radiosonde data are of special value for researchers and weather forecast services alike. In the course of 30 years (1982-12-29 to 2012-11-25) a total of 12378 radiosonde balloons were started on POLARSTERN. All radiosonde data can now be found here. Each dataset contains the directly measured parameters air temperature, relative humidity and air pressure, and the derived altitude, wind direction and wind speed. 432 datasets additionally contain ozone measurements.

Important factors in predicting mortality outcome from stroke : findings from the Anglia Stroke Clinical Network Evaluation Study

The Anglia Stroke Clinical Network Evaluation Study (ASCNES) is funded by the National Institute for Health Research (NIHR) Research for Patient Benefit Programme (PB-PG-1208-18240). This paper presents independent research funded by the NIHR under its Research for Patient Benefit (RfPB) programme (Grant Reference Number PB-PG-1208-18240). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. EAW receives funding support from the NIHR Biomedical Research Centre award to Cambridge

« Utilisation des services de santé chez les personnes âgées : association avec la maladie chronique et l’incapacité »

Diverses publications soulignent l'augmentation de l'espérance de vie et avec elle, le vieillissement mondial de la population. Ce processus se poursuivra à l'avenir, ainsi que son influence sur l’incidence et la prévalence de l'incapacité. Chez les personnes âgées, l’incapacité, les maladies chroniques et leur association constituent un sujet important dans le domaine de la santé publique en raison de l'effet qu'ils ont sur la demande des services de santé. Le but de ce mémoire est d’examiner quelle est la contribution respective des maladies chroniques et de l'incapacité dans l'utilisation des services de santé chez les personnes âgées et de leur interaction. Il s’agit de savoir si l'association entre la maladie chronique et l'utilisation des services de santé est modifiée par l’incapacité prenant en compte les caractéristiques de l'individu et son environnement. Ce travail est basé sur le modèle comportemental proposé par Andersen et Newman et le modèle du processus d’incapacité de Verbrugge et Jette. Pour répondre à l’objectif, nous utilisons les données du projet de recherche “ FRéLE ” (Fragilité, une étude longitudinale de ses expressions), réalisé durant la période 2010 -2013 auprès d’un échantillon de 1643 personnes âgées vivant dans la communauté au Québec. L’incapacité est évaluée à l’aide de deux indicateurs : les AVQ et les AVD. Les maladies chroniques sont mesurées par l’indice fonctionnel de comorbidité (IFC). La dépression est évaluée selon les critères de l’échelle de dépression gériatrique (EDG). L’état cognitif est mesuré par l'évaluation cognitive de Montréal (MoCA). Les facteurs de prédisposition comportent l’âge, le sexe, l’ethnicité et le niveau scolaire. Les facteurs facilitateurs incluent le revenu et le réseau social, ce dernier étant mesure par la présence ou non d’une personne de soutien et son lien avec la personne âgée. Divers modèles de régression sont adoptés pour identifier les facteurs statistiquement significatifs du modèle comportemental d’Andersen et Newman et du modèle du processus d’incapacité de Verbrugge et Jette. Nos résultats ont montré que, si le rôle des prédicteurs de l’utilisation varie en fonction du type de services de santé utilisé, l’utilisation s’accroît principalement avec le nombre de maladies chroniques. En ce qui concerne l’interaction entre la maladie chronique et l’incapacité, nos résultats ont révélé que l’interaction n’est statistiquement significative pour aucun des services analysés. Compte tenu de la diversité et les besoins de la population âgée, caractérisée par une prévalence élevée de maladies chroniques et d'incapacités, l’étude des facteurs impliqués dans l'utilisation des services de santé sera utile pour la mise en œuvre d’une offre de services, plus conforme aux besoins de cette population

Artificial neural network application in short-term prediction in an oscillating water column

Oscillating Water Column (OWC) is one type of promising wave energy devices due to its obvious advantage over many other wave energy converters: no moving component in sea water. Two types of OWCs (bottom-fixed and floating) have been widely investigated, and the bottom-fixed OWCs have been very successful in several practical applications. Recently, the proposal of massive wave energy production and the availability of wave energy have pushed OWC applications from near-shore to deeper water regions where floating OWCs are a better choice. For an OWC under sea waves, the air flow driving air turbine to generate electricity is a random process. In such a working condition, single design/operation point is nonexistent. To improve energy extraction, and to optimise the performance of the device, a system capable of controlling the air turbine rotation speed is desirable. To achieve that, this paper presents a short-term prediction of the random, process by an artificial neural network (ANN), which can provide near-future information for the control system. In this research, ANN is explored and tuned for a better prediction of the airflow (as well as the device motions for a wide application). It is found that, by carefully constructing ANN platform and optimizing the relevant parameters, ANN is capable of predicting the random process a few steps ahead of the real, time with a good accuracy. More importantly, the tuned ANN works for a large range of different types of random, process.

Structural and agentic analysis of supply-chains: a social network analysis approach

This research adds to a body of work exploring the role of Social Network Analysis (SNA) in the study of both relational and structural characteristics of supply chain networks. Two contrasting network cases (food enterprises and digital-based enterprises) are chosen in order to elicit structural differences in business networks subject to divergences in local embeddedness and the relative materiality of the goods and services produced. Our analysis and findings draw out differences in network structure as evidenced by metrics of network centralization and cohesion, the presence of components and other sub-groupings, and the position of central actors. We relate these structural features both to the nature of the networks and to the (qualitative) experiences of the actors themselves. We find, in particular, the role of customers as co-creators of knowledge (for the Food network), the central role of infrastructure and services (for the Digital network), the importance of ICT as a source of codified knowledge inputs, along with the continuing importance of geographical proximity for the development and transfer of tacit knowledge and for incremental learning.

Forces of agglomeration and dispersal in rural business networks: a social network analysis

Two concepts in rural economic development policy have been the focus of much research and policy action: the identification and support of clusters or networks of firms and the availability and adoption by rural businesses of Information and Communication Technologies (ICT). From a theoretical viewpoint these policies are based on two contrasting models, with clustering seen as a process of economic agglomeration, and ICT-mediated communication as a means of facilitating economic dispersion. The study’s conceptual framework is based on four interrelated elements: location, interaction, knowledge, and advantage, together with the concept of networks which is employed as an operationally and theoretically unifying concept. The research questions are developed in four successive categories: Policy, Theory, Networks, and Method. The questions are approached using a study of two contrasting groups of rural small businesses in West Cork, Ireland: (a) Speciality Foods, and (b) firms in Digital Products and Services. The study combines Social Network Analysis (SNA) with Qualitative Thematic Analysis, using data collected from semi-structured interviews with 58 owners or managers of these businesses. Data comprise relational network data on the firms’ connections to suppliers, customers, allies and competitors, together with linked qualitative data on how the firms established connections, and how tacit and codified knowledge was sourced and utilised. The research finds that the key characteristics identified in the cluster literature are evident in the sample of Speciality Food businesses, in relation to flows of tacit knowledge, social embedding, and the development of forms of social capital. In particular the research identified the presence of two distinct forms of collective social capital in this network, termed “community” and “reputation”. By contrast the sample of Digital Products and Services businesses does not have the form of a cluster, but matches more closely to dispersive models, or “chain” structures. Much of the economic and social structure of this set of firms is best explained in terms of “project organisation”, and by the operation of an individual rather than collective form of “reputation”. The rural setting in which these firms are located has resulted in their being service-centric, and consequently they rely on ICT-mediated communication in order to exchange tacit knowledge “at a distance”. It is this factor, rather than inputs of codified knowledge, that most strongly influences their operation and their need for availability and adoption of high quality communication technologies. Thus the findings have applicability in relation to theory in Economic Geography and to policy and practice in Rural Development. In addition the research contributes to methodological questions in SNA, and to methodological questions about the combination or mixing of quantitative and qualitative methods.

SDN enabled dynamically reconfigurable high capacity optical access architecture for converged services

Dynamically reconfigurable time-division multiplexing (TDM) dense wavelength division multiplexing (DWDM) long-reach passive optical networks (PONs) can support the reduction of nodes and network interfaces by enabling a fully meshed flat optical core. In this paper we demonstrate the flexibility of the TDM-DWDM PON architecture, which can enable the convergence of multiple service types on a single physical layer. Heterogeneous services and modulation formats, i.e. residential 10G PON channels, business 100G dedicated channel and wireless fronthaul, are demonstrated co-existing on the same long reach TDM-DWDM PON system, with up to 100km reach, 512 users and emulated system load of 40 channels, employing amplifier nodes with either erbium doped fiber amplifiers (EDFAs) or semiconductor optical amplifiers (SOAs). For the first time end-to-end software defined networking (SDN) management of the access and core network elements is also implemented and integrated with the PON physical layer in order to demonstrate two service use cases: a fast protection mechanism with end-to-end service restoration in the case of a primary link failure; and dynamic wavelength allocation (DWA) in response to an increased traffic demand.

« Utilisation des services de santé chez les personnes âgées : association avec la maladie chronique et l’incapacité »

Diverses publications soulignent l'augmentation de l'espérance de vie et avec elle, le vieillissement mondial de la population. Ce processus se poursuivra à l'avenir, ainsi que son influence sur l’incidence et la prévalence de l'incapacité. Chez les personnes âgées, l’incapacité, les maladies chroniques et leur association constituent un sujet important dans le domaine de la santé publique en raison de l'effet qu'ils ont sur la demande des services de santé. Le but de ce mémoire est d’examiner quelle est la contribution respective des maladies chroniques et de l'incapacité dans l'utilisation des services de santé chez les personnes âgées et de leur interaction. Il s’agit de savoir si l'association entre la maladie chronique et l'utilisation des services de santé est modifiée par l’incapacité prenant en compte les caractéristiques de l'individu et son environnement. Ce travail est basé sur le modèle comportemental proposé par Andersen et Newman et le modèle du processus d’incapacité de Verbrugge et Jette. Pour répondre à l’objectif, nous utilisons les données du projet de recherche “ FRéLE ” (Fragilité, une étude longitudinale de ses expressions), réalisé durant la période 2010 -2013 auprès d’un échantillon de 1643 personnes âgées vivant dans la communauté au Québec. L’incapacité est évaluée à l’aide de deux indicateurs : les AVQ et les AVD. Les maladies chroniques sont mesurées par l’indice fonctionnel de comorbidité (IFC). La dépression est évaluée selon les critères de l’échelle de dépression gériatrique (EDG). L’état cognitif est mesuré par l'évaluation cognitive de Montréal (MoCA). Les facteurs de prédisposition comportent l’âge, le sexe, l’ethnicité et le niveau scolaire. Les facteurs facilitateurs incluent le revenu et le réseau social, ce dernier étant mesure par la présence ou non d’une personne de soutien et son lien avec la personne âgée. Divers modèles de régression sont adoptés pour identifier les facteurs statistiquement significatifs du modèle comportemental d’Andersen et Newman et du modèle du processus d’incapacité de Verbrugge et Jette. Nos résultats ont montré que, si le rôle des prédicteurs de l’utilisation varie en fonction du type de services de santé utilisé, l’utilisation s’accroît principalement avec le nombre de maladies chroniques. En ce qui concerne l’interaction entre la maladie chronique et l’incapacité, nos résultats ont révélé que l’interaction n’est statistiquement significative pour aucun des services analysés. Compte tenu de la diversité et les besoins de la population âgée, caractérisée par une prévalence élevée de maladies chroniques et d'incapacités, l’étude des facteurs impliqués dans l'utilisation des services de santé sera utile pour la mise en œuvre d’une offre de services, plus conforme aux besoins de cette population

Mining the Online Social Network Data: Influence, Summarization, and Organization

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.

THE DESIGN OF A POWER SYSTEM FOR A WIRELESS SENSOR NETWORK FOR LONG-TERM OPERATION

Wireless sensor networks (WSNs) have shown wide applicability to many fields including monitoring of environmental, civil, and industrial settings. WSNs however are resource constrained by many competing factors that span their hardware, software, and networking. One of the central resource constrains is the charge consumption of WSN nodes. With finite energy supplies, low charge consumption is needed to ensure long lifetimes and success of WSNs. This thesis details the design of a power system to support long-term operation of WSNs. The power system’s development occurs in parallel with a custom WSN from the Queen’s MEMS Lab (QML-WSN), with the goal of supporting a 1+ year lifetime without sacrificing functionality. The final power system design utilizes a TPS62740 DC-DC converter with AA alkaline batteries to efficiently supply the nodes while providing battery monitoring functionality and an expansion slot for future development. Testing tools for measuring current draw and charge consumption were created along with analysis and processing software. Through their use charge consumption of the power system was drastically lowered and issues in QML-WSN were identified and resolved including the proper shutdown of accelerometers, and incorrect microcontroller unit (MCU) power pin connection. Controlled current profiling revealed unexpected behaviour of nodes and detailed current-voltage relationships. These relationships were utilized with a lifetime projection model to estimate a lifetime between 521-551 days, depending on the mode of operation. The power system and QML-WSN were tested over a long term trial lasting 272+ days in an industrial testbed to monitor an air compressor pump. Environmental factors were found to influence the behaviour of nodes leading to increased charge consumption, while a node in an office setting was still operating at the conclusion of the trail. This agrees with the lifetime projection and gives a strong indication that a 1+ year lifetime is achievable. Additionally, a light-weight charge consumption model was developed which allows charge consumption information of nodes in a distributed WSN to be monitored. This model was tested in a laboratory setting demonstrating +95% accuracy for high packet reception rate WSNs across varying data rates, battery supply capacities, and runtimes up to full battery depletion.

Applying complexity science to air traffic management

Complexity science is the multidisciplinary study of complex systems. Its marked network orientation lends itself well to transport contexts. Key features of complexity science are introduced and defined, with a specific focus on the application to air traffic management. An overview of complex network theory is presented, with examples of its corresponding metrics and multiple scales. Complexity science is starting to make important contributions to performance assessment and system design: selected, applied air traffic management case studies are explored. The important contexts of uncertainty, resilience and emergent behaviour are discussed, with future research priorities summarised.