Location-Based services - A crescente importância da localização

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

Wireless privacy and personalized location-based services: the challenge of translating the legal framework into business practices

L'avancement des communications sans-fil permet l'obtention de nouveaux services bases sur l'habileté des fournisseurs de services sans-fil à déterminer avec précision, et avec l'utilisation de technologies de pistage, la localisation et position géographiquement d'appareils sans-fil Cette habileté permet d'offrir aux utilisateurs de sans-fil de nouveaux services bases sur la localisation et la position géographique de leur appareil. Le développement des services basés sur la localisation des utilisateurs de sans-fil soulevé certains problèmes relatifs à la protection de la vie privée qui doivent être considérés. En effet, l'appareil sans-fil qui suit et enregistre les mouvements de I 'utilisateur permet un système qui enregistre et entrepose tous les mouvements et activités d'un tel utilisateur ou encore qui permet l'envoi de messages non anticipes à ce dernier. Pour ce motif et afin de protéger la vie privée des utilisateurs de sans-fil, une compagnie désirant développer ou déployer une technologie permettant d'offrir ce genre de services personnalisés devra analyser l'encadrement légal touchant la protection des données personnelles--lequel est dans certains cas vague et non approprié à ce nouveau contexte--ainsi que la position de l'industrie dans ce domaine, et ce, afin d'être en mesure de traduire cet encadrement en pratiques commerciales. Cette analyse permettra d'éclairer le fournisseur de ces services sur la façon d'établir son modèle d'affaires et sur le type de technologie à développer afin d'être en mesure de remédier aux nouveaux problèmes touchant la vie privée tout en offrant ces nouveaux services aux utilisateurs de sans-fil.

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.

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.

Multi-agent system for personalization of location-based services

The current ubiquitous network access and increase in network bandwidth are driving the sales of mobile location-aware user devices and, consequently, the development of context-aware applications, namely location-based services. The goal of this project is to provide consumers of location-based services with a richer end-user experience by means of service composition, personalization, device adaptation and continuity of service. Our approach relies on a multi-agent system composed of proxy agents that act as mediators and providers of personalization meta-services, device adaptation and continuity of service for consumers of pre-existing location-based services. These proxy agents, which have Web services interfaces to ensure a high level of interoperability, perform service composition and take in consideration the preferences of the users, the limitations of the user devices, making the usage of different types of devices seamless for the end-user. To validate and evaluate the performance of this approach, use cases were defined, tests were conducted and results gathered which demonstrated that the initial goals were successfully fulfilled.

Location-based Marketing: the academic framework

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Scenario Analysis of the Location Based Services Industry

Tutkimuksen tavoitteena oli luoda erilaisia skenaariota paikka riippuvaisten palveluiden toimialan tulevaisuudesta. Tunnistamalla nykyisiä sekä tulevia alaa edistäviä ja rajoittavia tekijöitä kolme skenaariota luotiin, jotka mahdollisesti kuvaisivat paikka riippuvaisten palveluiden toimialaa viiden vuoden päästä: "Massa spämmaus", "Raju operaattori kilpailu - nousevia yksityisyyden huolia" sekä "Nokian ajama kolmannen sukupolven verkot tulevat ennen odotettua" Skenaarioiden luomiseksi ensimmäinen osa tutkimuksesta keskittyi erilaisiin skenaarioiden kirjoitus prosesseihin ja niissä huomioitaviin asioihin. Tutkimuksen tarkoituksiin sopiva skenaarion kirjoitusprosessi esiteltiin, minkä jälkeen| paikkariippuvaisten palveluiden toimialaa käsiteltiin. Lopuksi itse skenaariot esiteltiin ja nimettiin skenaarioiden teemojen mukaan. Tutkimuksen johtopäätös on, ettei toimialan tulevaisuutta voida ennustaa riittävällä varmuudella. Tutkimuksen arvo kuitenkin piilee sen antamassa ymmärryksessä liittyen tekijöihin, jotka tulevat päättämään alan tulevaisuuden sekä skenaariossa, joita nämä tekijät voivat muodostaa.

Optimizing Mobile Java Location-Based Services in Symbian OS

This thesis evaluates methods for obtaining high performance in applications running on the mobile Java platform. Based on the evaluated methods, an optimization was done to a Java extension API running on top the Symbian operating system. The API provides location-based services for mobile Java applications. As a part of this thesis, the JNI implementation in Symbian OS was also benchmarked. A benchmarking tool was implemented in the analysis phase in order to implement extensive performance test set. Based on the benchmark results, it was noted that the landmarks implementation of the API was performing very slowly with large amounts of data. The existing implementation proved to be very inconvenient for optimization because the early implementers did not take performance and design issues into consideration. A completely new architecture was implemented for the API in order to provide scalable landmark initialization and data extraction by using lazy initialization methods. Additionally, runtime memory consumption was also an important part of the optimization. The improvement proved to be very efficient based on the measurements after the optimization. Most of the common API use cases performed extremely well compared to the old implementation. Performance optimization is an important quality attribute of any piece of software especially in embedded mobile devices. Typically, projects get into trouble with performance because there are no clear performance targets and knowledge how to achieve them. Well-known guidelines and performance models help to achieve good overall performance in Java applications and programming interfaces.

Privacy-friendly Business Models for Location-Based Mobile Services

This paper presents a theoretical model to analyze the privacy issues around location based mobile business models. We report the results of an exploratory field experiment in Switzerland that assessed the factors driving user payoff in mobile business. We found that (1) the personal data disclosed has a negative effect on user payoff; (2) the amount of personalization available has a direct and positive effect, as well as a moderating effect on user payoff; (3) the amount of control over user's personal data has a direct and positive effect, as well as a moderating effect on user payoff. The results suggest that privacy protection could be the main value proposition in the B2C mobile market. From our theoretical model we derive a set of guidelines to design a privacy-friendly business model pattern for third-party services. We discuss four examples to show the mobile platform can play a key role in the implementation of these new business models.

Positional Accuracy in Smart-Phones and Its Effect on LBS Applications.

Location-based services (LBS) highly rely on the location of the mobile user in order to provide the service tailored to that location. This location is calculated differently depending on the technology available in the used mobile device. No matter which technology is used, the location will never be calculated 100% correctly; instead there will always be a margin of error generated during the calculation, which is referred to as positional accuracy. This research has reviewed the eight most common positioning technologies available in the major current smart-phones and assessed their positional accuracy with respect to its usage by LBS applications. Given the vast majority of these applications, this research classified them into thirteen categories, and these categories were also classified depending on their level criticality as low, medium, or high critical, and whether they function indoor or outdoor. The accuracies of different positioning technologies are compared to these two criteria. Low critical outdoor and high critical indoor applications were found technologically covered; high and medium critical outdoor ones weren?t fully resolved. Finally three potential solutions are suggested to be implemented in future smartphones to resolve this technological gap: Real-Time Kinematics Global Positioning System (RTK GPS), terrestrial transmitters, and combination of Wireless Sensors Network and Radio Frequency Identification (WSN-RFID).

gvSIG Mini y Phone Cache

gvSIG Mini es una aplicación open-source de usuario final cliente móvil de Infraestructura de Datos Espaciales IDEs con licencia GNU/ GPL, diseñada para teléfonos móviles Java y Android que permite la visualización y navegación sobre cartografía digital estructurada en tiles procedente de servicios web OGC como WMS(-C) y de servicios como OpenStreetMap (OSM), Yahoo Maps, Maps Bing, así como el almacenamiento en caché para reducir al mínimo el ancho de banda. gvSIG Mini puede acceder a servicios geoespaciales como NameFinder, para la búsqueda de puntos de interés y YOURS (Yet Another OpenStreetMap Routing Service) para el cálculo de rutas y la renderización de la información vectorial el lado del cliente. Por otra parte, gvSIG Mini también ofrece servicio de localización GPS. La versión de gvSIG Mini para Android, posee algunas características adicionales como son el soporte de localización Android o el uso del lacelerómetro para centrado. Esta versión también hace uso de servicios como son la predicción del tiempo o TweetMe que permite compartir una localización utilizando el popular servicio social Twitter. gvSIG Mini es una aplicación que puede ser descargada y usada libremente, convirtiéndose en una plataforma para el desarrollo de nuevas soluciones y aplicaciones en el campo de Location Based Services (LBS). gvSIG Mini ha sido desarrollado por Prodevelop, S.L. No es un proyecto oficial de gvSIG, pero se une a la familia a través del catálogo de extensiones no oficiales de gvSIG. Phone Cache es una extensión que funciona sobre gvSIG 1.1.2 que permite generar una caché, para poder utilizar gvSIG Mini para Java en modo desconectado

Strategie di elaborazione di tracciati GPS per reti veicolari

Il proliferare di dispositivi di elaborazione e comunicazione mobili (telefoni cellulari, computer portatili, PDA, wearable devices, personal digital assistant) sta guidando un cambiamento rivoluzionario nella nostra società dell'informazione. Si sta migrando dall'era dei Personal Computer all'era dell'Ubiquitous Computing, in cui un utente utilizza, parallelamente, svariati dispositivi elettronici attraverso cui può accedere a tutte le informazioni, ovunque e quantunque queste gli si rivelino necessarie. In questo scenario, anche le mappe digitali stanno diventando sempre più parte delle nostre attività quotidiane; esse trasmettono informazioni vitali per una pletora di applicazioni che acquistano maggior valore grazie alla localizzazione, come Yelp, Flickr, Facebook, Google Maps o semplicemente le ricerche web geo-localizzate. Gli utenti di PDA e Smartphone dipendono sempre più dai GPS e dai Location Based Services (LBS) per la navigazione, sia automobilistica che a piedi. Gli stessi servizi di mappe stanno inoltre evolvendo la loro natura da uni-direzionale a bi-direzionale; la topologia stradale è arricchita da informazioni dinamiche, come traffico in tempo reale e contenuti creati dagli utenti. Le mappe digitali aggiornabili dinamicamente sono sul punto di diventare un saldo trampolino di lancio per i sistemi mobili ad alta dinamicità ed interattività, che poggiando su poche informazioni fornite dagli utenti, porteranno una moltitudine di applicazioni innovative ad un'enorme base di consumatori. I futuri sistemi di navigazione per esempio, potranno utilizzare informazioni estese su semafori, presenza di stop ed informazioni sul traffico per effettuare una ottimizzazione del percorso che valuti simultaneamente fattori come l'impronta al carbonio rilasciata, il tempo di viaggio effettivamente necessario e l'impatto della scelta sul traffico locale. In questo progetto si mostra come i dati GPS raccolti da dispositivi fissi e mobili possano essere usati per estendere le mappe digitali con la locazione dei segnali di stop, dei semafori e delle relative temporizzazioni. Queste informazioni sono infatti oggi rare e locali ad ogni singola municipalità, il che ne rende praticamente impossibile il pieno reperimento. Si presenta quindi un algoritmo che estrae utili informazioni topologiche da agglomerati di tracciati gps, mostrando inoltre che anche un esiguo numero di veicoli equipaggiati con la strumentazione necessaria sono sufficienti per abilitare l'estensione delle mappe digitali con nuovi attributi. Infine, si mostrerà come l'algoritmo sia in grado di lavorare anche con dati mancanti, ottenendo ottimi risultati e mostrandosi flessibile ed adatto all'integrazione in sistemi reali.

Implementación de un sistema informático para el procesamiento de datos de tráfico vial

El presente Trabajo de Fin de Grado se enmarca dentro de un sistema de control y desarrollo de sistemas inteligentes de transporte (ITS). Este Trabajo consta de varias líneas de desarrollo, que se engloban dentro de dicho marco y surgen de la necesidad de aumentar la seguridad, flujo, estructura y mantenimiento de las carreteras incorporando las tecnologías más recientes. En primer lugar, el presente Trabajo se centra en el desarrollo de un nuevo sistema de procesamiento de datos de tráfico en tiempo real que aprovecha las tecnologías de Big Data, Cloud Computing y Map-Reduce que han surgido estos últimos años. Para ello se realiza un estudio previo de los datos de tráfico vial que originan los vehículos que viajan por carreteras. Centrándose en el sistema empleado por la Dirección General de Tráfico de España y comparándolos con el de las Empresas basadas en servicios de localización (LBS). Se expone el modelo Hadoop utilizado así como el proceso Map-Reduce implementado en este sistema analizador. Por último los datos de salida son preparados y enviados a un módulo web básico que actúa como Sistema de Información Geográfica (GIS).---ABSTRACT---This Final Degree Project is part of a control system and development of intelligent transport systems (ITS). This work is part of a several lines of development, which are included within this framework and arise from the need to increase security, flow, structure and maintenance of roads incorporating the latest technologies. First, this paper focuses on the development of a new data processing system of real-time traffic that takes advantage of Big Data, Cloud Computing and Map-Reduce technologies emerged in our recent years. It is made a preliminary study of road traffic data originated by vehicles traveling by road. Focusing on the system used by the Dirección General de Tráfico of Spain and compared with that of the companies offering location based services (LBS). It is exposed the used Hadoop model and the Map-Reduce process implemented on this analyzer system. Finally, the output data is prepared and sent to a basic web module that acts as Geographic Information System (GIS).