The popularity of location empowered devices such as GPS enabled smart-phones has immensely amplified the use of location-based services in social networks. This happened by allowing users to share Geo-tagged contents such as current locations/check-ins with their social network friends. These location-aware social networks are called Location-based Social Networks (LBSN), and examples include Foursquare and Gowalla. The data of LBSNs are being used for providing different kinds of services such as the recommendation of locations, friends, activities, and media contents, and the prediction of user's locations. To provide such services, different queries are utilized that exploit activity/check-in data of users. Usually, LBSN data is divided into two parts, a social graph that encapsulates the friendships of users and an activity graph that maintains the visit history of users at locations. Such a data separation is scalable enough for processing queries that directly utilize friendship information and visit history of users. These queries are called user and activity analytic queries. The visits of users at locations create relationships between those locations. Such relationships can be built on different features such as common visitors, geographical distance, and mutual location categories between them. The process of analysing such relationships for optimizing location-based services is termed Location Analytics. In location analytics, we expose the subjective nature of locations that can further be used for applications in the domain of prediction of visitors, traffic management, route planning, and targeted marketing.In this thesis, we provide a general LBSN data model which can support storage and processing of queries required for different applications, called location analytics queries. The LBSN data model we introduce, segregates the LBSN data into three graphs: the social graph, the activity graph, and the location graph. The location graph maintains the interactions of locations among each other. We define primitive queries for each of these graphs. In order to process an advanced query, we express it as a combination of these primitive queries and process them on corresponding graphs in parallel. We further provide a distributed data processing framework called GeoSocial-GraphX (GSG). GSG implements the aforementioned LBSN data model for efficient and scalable processing of the queries. We further exploit the location graph for providing novel location analytics queries in the domain of influence maximization and visitor prediction. We introduce a notion of location influence. Such influence can capture the interactions of locations based on their visitors and can be used for propagation of information between them. The applications of such a query lie in the domain of outdoor marketing, and simulation of virus and news propagation. We also provide a unified system IMaxer that can evaluate and compare different information propagation mechanisms. We further exploit the subjective nature of locations by analysing the mobility behaviour of their visitors. We use such information to predict the individual visitors as well as the groups of visitors (cohorts) in future for those locations. The prediction of visitors can be used for better event planning, traffic management, targeted marketing, and ride-sharing services.In order to evaluate the proposed frameworks and approaches, we utilize data from four real-life LBSNs: Foursquare, Brightkite, Gowalla, and Wee Places. The detailed LBSN data mining and statistically significant experimental evaluation results show the effectiveness, efficiency, and scalability of our proposed methods. Our proposed approaches can be employed in real systems for providing life-care services. / Doctorat en Sciences de l'ingénieur et technologie / The portal is not showing my complete name. The name (my complete name), I want to have on the diploma is "Muhammad Aamir Saleem". Please correct this issue. / info:eu-repo/semantics/nonPublished

University of Macau / Faculty of Science and Technology. / Department of Computer and Information Science

abstract: The rapid urban expansion has greatly extended the physical boundary of our living area, along with a large number of POIs (points of interest) being developed. A POI is a specific location (e.g., hotel, restaurant, theater, mall) that a user may find useful or interesting. When exploring the city and neighborhood, the increasing number of POIs could enrich people's daily life, providing them with more choices of life experience than before, while at the same time also brings the problem of "curse of choices", resulting in the difficulty for a user to make a satisfied decision on "where to go" in an efficient way. Personalized POI recommendation is a task proposed on purpose of helping users filter out uninteresting POIs and reduce time in decision making, which could also benefit virtual marketing. Developing POI recommender systems requires observation of human mobility w.r.t. real-world POIs, which is infeasible with traditional mobile data. However, the recent development of location-based social networks (LBSNs) provides such observation. Typical location-based social networking sites allow users to "check in" at POIs with smartphones, leave tips and share that experience with their online friends. The increasing number of LBSN users has generated large amounts of LBSN data, providing an unprecedented opportunity to study human mobility for personalized POI recommendation in spatial, temporal, social, and content aspects. Different from recommender systems in other categories, e.g., movie recommendation in NetFlix, friend recommendation in dating websites, item recommendation in online shopping sites, personalized POI recommendation on LBSNs has its unique challenges due to the stochastic property of human mobility and the mobile behavior indications provided by LBSN information layout. The strong correlations between geographical POI information and other LBSN information result in three major human mobile properties, i.e., geo-social correlations, geo-temporal patterns, and geo-content indications, which are neither observed in other recommender systems, nor exploited in current POI recommendation. In this dissertation, we investigate these properties on LBSNs, and propose personalized POI recommendation models accordingly. The performance evaluated on real-world LBSN datasets validates the power of these properties in capturing user mobility, and demonstrates the ability of our models for personalized POI recommendation. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2014

O cada vez maior número de tecnologias que fornecem serviços de geolocalização tem possibilitado gerar uma grande quantidade de dados de geolocalização. Estes dados, são armazenados principalmente como pontos de localização com informação temporal. Uma trajetória é definida como uma sequência discreta e finita destes pontos de localização. Nos últimos anos, a recente área de mineração de trajetórias visa aproveitar esta abundância de dados. Nesta área, existem várias técnicas de mineração desenvolvidas, mas todas elas dependem diretamente da qualidade das trajetórias. Assim, o preprocessamento tem um papel primordial na mineração de trajetórias. Entre as tarefas de preprocessamento, um problema relevante é a reconstrução ou inferência de trajetórias. Devido ao alto consumo de energia de dispositivos de localização como o GPS e ao crescente uso de geo-marcações nas redes sociais, que possibilita a construção de trajetórias ordenando temporalmente estas marcações, muitas das trajetórias existentes apresentam taxas de amostragem muito baixas. A maioria das pesquisas nesse problema utilizam, no caso de áreas urbanizadas, informações do grafo formado por ruas e cruzamentos. Porém, elas levam em conta apenas trajetórias de veículos principalmente pelo fato que muitos dos percursos dos pedestres ficam fora das ruas. Atualmente, graças às plataformas livres de mapas colaborativos, é possível incluir estes trajetos como parte das informações de ruas. Assim, este projeto tem o objetivo de investigar o uso das informações das ruas na reconstrução de trajetórias, principalmente de pedestres. O escopo da proposta compreende o desenvolvimento de uma rede social geo-localizada com o intuito de capturar dados de localização. Posteriormente, estes dados serão anonimizados, utilizados na reconstrução de trajetórias de pedestres e disponibilizados para uso em pesquisas futuras. / The ever-greater number of technologies providing location-based services has allowed the generation of big amounts of geolocation data. This data is mainly stored as location points in conjunction with temporal information. A trajectory is defined as a discrete and finite sequence of this kind of points. In recent years, the relatively new field of trajectory data mining aims to leverage this abundance of data. On this field, there are several data mining techniques developed, but all of these depend on trajectory quality. Hence, preprocessing becomes relevant to this field. Among trajectory data mining tasks, one important problem is trajectory reconstruction. Due to the high energy consumption of geolocation devices like GPS and the growing usage of geo-tags in social networks, which can represent trajectories by being sorted chronologically, most of these trajectories are collected at low sampling rates. A majority of research on this problem is focused on using road network information in urbanized areas to reconstruct trajectories. However, these approaches take into account vehicle trajectories only due to fact that most pedestrian paths do not always follow the same road network routes than vehicles. Currently, thanks to open collaborative maps, it is possible to add pedestrian paths to the road network structure. Thereby, this project aims to research the usage of road network information in pedestrian trajectories reconstruction. This projects scope comprises the development of a location-based social network to collect geolocation data. Subsequently, this data will be anonymized, used for pedestrian trajectory reconstruction and, finally, made available for research purposes.

O cada vez maior número de tecnologias que fornecem serviços de geolocalização tem possibilitado gerar uma grande quantidade de dados de geolocalização. Estes dados, são armazenados principalmente como pontos de localização com informação temporal. Uma trajetória é definida como uma sequência discreta e finita destes pontos de localização. Nos últimos anos, a recente área de mineração de trajetórias visa aproveitar esta abundância de dados. Nesta área, existem várias técnicas de mineração desenvolvidas, mas todas elas dependem diretamente da qualidade das trajetórias. Assim, o preprocessamento tem um papel primordial na mineração de trajetórias. Entre as tarefas de preprocessamento, um problema relevante é a reconstrução ou inferência de trajetórias. Devido ao alto consumo de energia de dispositivos de localização como o GPS e ao crescente uso de geo-marcações nas redes sociais, que possibilita a construção de trajetórias ordenando temporalmente estas marcações, muitas das trajetórias existentes apresentam taxas de amostragem muito baixas. A maioria das pesquisas nesse problema utilizam, no caso de áreas urbanizadas, informações do grafo formado por ruas e cruzamentos. Porém, elas levam em conta apenas trajetórias de veículos principalmente pelo fato que muitos dos percursos dos pedestres ficam fora das ruas. Atualmente, graças às plataformas livres de mapas colaborativos, é possível incluir estes trajetos como parte das informações de ruas. Assim, este projeto tem o objetivo de investigar o uso das informações das ruas na reconstrução de trajetórias, principalmente de pedestres. O escopo da proposta compreende o desenvolvimento de uma rede social geo-localizada com o intuito de capturar dados de localização. Posteriormente, estes dados serão anonimizados, utilizados na reconstrução de trajetórias de pedestres e disponibilizados para uso em pesquisas futuras. / The ever-greater number of technologies providing location-based services has allowed the generation of big amounts of geolocation data. This data is mainly stored as location points in conjunction with temporal information. A trajectory is defined as a discrete and finite sequence of this kind of points. In recent years, the relatively new field of trajectory data mining aims to leverage this abundance of data. On this field, there are several data mining techniques developed, but all of these depend on trajectory quality. Hence, preprocessing becomes relevant to this field. Among trajectory data mining tasks, one important problem is trajectory reconstruction. Due to the high energy consumption of geolocation devices like GPS and the growing usage of geo-tags in social networks, which can represent trajectories by being sorted chronologically, most of these trajectories are collected at low sampling rates. A majority of research on this problem is focused on using road network information in urbanized areas to reconstruct trajectories. However, these approaches take into account vehicle trajectories only due to fact that most pedestrian paths do not always follow the same road network routes than vehicles. Currently, thanks to open collaborative maps, it is possible to add pedestrian paths to the road network structure. Thereby, this project aims to research the usage of road network information in pedestrian trajectories reconstruction. This projects scope comprises the development of a location-based social network to collect geolocation data. Subsequently, this data will be anonymized, used for pedestrian trajectory reconstruction and, finally, made available for research purposes.

The explosive adoption rate of cell phones over the past few years has increased the desire for advertising agencies to explore mobile as an advertising channel. Over 90% of Americans own a cell phone, which opens a new channel for advertising agencies to reach consumers. The traditional advertising channels include print, television, radio, and most recently, the Internet. This professional report explores the importance and utilization of mobile as an advertising channel in advertising agencies media plans for their clients. / text

The aim of this thesis is to define human urban mobility patterns using modern tools and find out what influence human migration. First part deals with history of research and introduces different methods of modeling. Second part consists of own research based on data from application Foursquare and project Rekola. This data is processed using gravitation model and theory of intervening opportunities.

The aim of this thesis is to find methodology or theory to define human urban mobility patterns using modern tools and to find out what influences human mobility. The first part deals with history of research and introduces different methods of modeling. The second part consists of own research based on data from Foursquare application and project called Rekola. These data are processed using gravitation model and theory of intervening opportunities.

The location-based social network (LBSN) Snapchat allows millions of users to share their locations to others through Snap Map: a digital map that updates their position each time they open the app. While social science studies have explored sentiments, behaviors and norms among Snap Map users, there is limited research on this type of location-based social network in the field of human-computer interaction (HCI), indicating a need for expanding the understanding of the roles that this technology and its design play in shaping the experiences and interactions among users. To address this need, this dialogical study applied a mixed-methods approach consisting of autoethnography and semi-structured interviews with two co-participants over the course of five weeks. The outcomes were analyzed using concepts from postphenomenology, introducing the main stabilities being seen and seeing others that were leveraged to nuance how Snap Map impacts e.g. communication, agency and what it means to act. The postphenomenological findings where then discussed from the lens of the spatial self to unpack how Snap Map mediates identity and performance. This combination of methods and lenses applied to a social media platform is a novel and fruitful approach in HCI, that led to discussions on how the design of Snap Map leads to concepts such as spatiotemporal ambiguity and the nexus of selves, shaping users’ relations to each other, themselves and their identity. / Miljontals användare på det platsbaserade sociala nätverket Snapchat delar sin plats med varandra via Snapkartan: en digital karta som automatiskt uppdaterar användarens position varje gång de öppnar appen. Studier inom samhällsvetenskapen har kartlagt känslor, beteenden och normer bland Snapkartans användare, men inom fältet människa-datainteraktion (MDI) finns det idag lite forskning tillägnad denna typ av teknologi, vilket indikerar ett behov av att utvidga förståelsen för de roller teknologin och dess design spelar i påverkan av dagliga upplevelser och interaktioner bland användarna. För att adressera detta använde denna dialogiska studie en blandad metod som bestod av autoetnografi och semi-strukturerade intervjuer med två deltagare under fem veckor. Resultaten analyserades med hjälp av begrepp från postfenomenologi, där de huvudsakliga stabiliteterna "att bli sedd" och "att se andra" introducerades och användes för att nyansera hur Snapkartan påverkar exempelvis kommunikation, agens och vad det innebär att agera. De postfenomenologiska resultaten diskuterades sedan utifrån perspektivet av den rumsmedvetna aspekten av ens identitet (the spatial self) för att bryta ned hur Snapkartan förmedlar identitet och performativa handlingar. Denna kombination av metoder och perspektiv som tillämpas på en social medieplattform är ett nytt och fruktbart tillvägagångssätt inom MDI, som ledde till en diskussion om hur Snapkartans design ger upphov till begrepp som rumslig och tidsmässig tvetydighet och sammankopplingen av flera självuppfattningar som formar användarnas relationer till varandra, sig själva och deras identitet.

Online social networks (OSNs) are Web platforms providing different services to facilitate social interaction among their users. A particular kind of OSNs is the location-based social network (LBSN), which adds services based on location. One of the most important challenges in LBSNs is the link prediction problem. Link prediction problem aims to estimate the likelihood of the existence of future friendships among user pairs. Most of the existing studies in link prediction focus on the use of a single information source to perform predictions, i.e. only social information (e.g. social neighborhood) or only location information (e.g. common visited places). However, some researches have shown that the combination of different information sources can lead to more accurate predictions. In this sense, in this thesis we propose different link prediction methods based on the use of different information sources naturally existing in these networks. Thus, we propose seven new link prediction methods using the information related to user membership in social overlapping groups: common neighbors within and outside of common groups (WOCG), common neighbors of groups (CNG), common neighbors with total and partial overlapping of groups (TPOG), group naïve Bayes (GNB), group naïve Bayes of common neighbors (GNB-CN), group naïve Bayes of Adamic-Adar (GNB-AA) and group naïve Bayes of Resource Allocation (GNB-RA). Due to that social groups exist naturally in networks, our proposals can be used in any type of OSN.We also propose new eight link prediction methods combining location and social information: Check-in Observation (ChO), Check-in Allocation (ChA), Within and Outside of Common Places (WOCP), Common Neighbors of Places (CNP), Total and Partial Overlapping of Places (TPOP), Friend Allocation Within Common Places (FAW), Common Neighbors of Nearby Places (CNNP) and Nearby Distance Allocation (NDA). These eight methods are exclusively for work in LBSNs. Obtained results indicate that our proposals are as competitive as state-of-the-art methods, or better than they in certain scenarios. Moreover, since our proposals tend to be computationally more efficient, they are more suitable for real-world applications. / Redes sociais online (OSNs) são plataformas Web que oferecem serviços para promoção da interação social entre usuários. OSNs que adicionam serviços relacionados à geolocalização são chamadas redes sociais baseadas em localização (LBSNs). Um dos maiores desafios na análise de LBSNs é a predição de links. A predição de links refere-se ao problema de estimar a probabilidade de conexão futura entre pares de usuários que não se conhecem. Grande parte das pesquisas que focam nesse problema exploram o uso, de maneira isolada, de informações sociais (e.g. amigos em comum) ou de localização (e.g. locais comuns visitados). Porém, algumas pesquisas mostraram que a combinação de diferentes fontes de informação pode influenciar o incremento da acurácia da predição. Motivado por essa lacuna, neste trabalho foram desenvolvidos diferentes métodos para predição de links combinando diferentes fontes de informação. Assim, propomos sete métodos que usam a informação relacionada à participação simultânea de usuários en múltiples grupos sociais: common neighbors within and outside of common groups (WOCG), common neighbors of groups (CNG), common neighbors with total and partial overlapping of groups (TPOG), group naïve Bayes (GNB), group naïve Bayes of common neighbors (GNB-CN), group naïve Bayes of Adamic-Adar (GNB-AA), e group naïve Bayes of Resource Allocation (GNB-RA). Devido ao fato que a presença de grupos sociais não está restrita a alguns tipo de redes, essas propostas podem ser usadas nas diversas OSNs existentes, incluindo LBSNs. Também, propomos oito métodos que combinam o uso de informações sociais e de localização: Check-in Observation (ChO), Check-in Allocation (ChA), Within and Outside of Common Places (WOCP), Common Neighbors of Places (CNP), Total and Partial Overlapping of Places (TPOP), Friend Allocation Within Common Places (FAW), Common Neighbors of Nearby Places (CNNP), e Nearby Distance Allocation (NDA). Tais propostas são para uso exclusivo em LBSNs. Os resultados obtidos indicam que nossas propostas são tão competitivas quanto métodos do estado da arte, podendo até superá-los em determinados cenários. Ainda mais, devido a que na maioria dos casos nossas propostas são computacionalmente mais eficientes, seu uso resulta mais adequado em aplicações do mundo real.