Model-driven dual caching For nomadic service-oriented architecture clients

Mobile devices have evolved over the years from resource constrained devices that supported only the most basic tasks to powerful handheld computing devices. However, the most significant step in the evolution of mobile devices was the introduction of wireless connectivity which enabled them to host applications that require internet connectivity such as email, web browsers and maybe most importantly smart/rich clients. Being able to host smart clients allows the users of mobile devices to seamlessly access the Information Technology (IT) resources of their organizations. One increasingly popular way of enabling access to IT resources is by using Web Services (WS). This trend has been aided by the rapid availability of WS packages/tools, most notably the efforts of the Apache group and Integrated Development Environment (IDE) vendors. But the widespread use of WS raises questions for users of mobile devices such as laptops or PDAs; how and if they can participate in WS. Unlike their “wired” counterparts (desktop computers and servers) they rely on a wireless network that is characterized by low bandwidth and unreliable connectivity.The aim of this thesis is to enable mobile devices to host Web Services consumers. It introduces a Model-Driven Dual Caching (MDDC) approach to overcome problems arising from temporarily loss of connectivity and fluctuations in bandwidth

Pervasive Data Access in Wireless and Mobile Computing Environments

The rapid advance of wireless and portable computing technology has brought a lot of research interests and momentum to the area of mobile computing. One of the research focus is on pervasive data access. with wireless connections, users can access information at any place at any time. However, various constraints such as limited client capability, limited bandwidth, weak connectivity, and client mobility impose many challenging technical issues. In the past years, tremendous research efforts have been put forth to address the issues related to pervasive data access. A number of interesting research results were reported in the literature. This survey paper reviews important works in two important dimensions of pervasive data access: data broadcast and client caching. In addition, data access techniques aiming at various application requirements (such as time, location, semantics and reliability) are covered

Management of spatial data for visualization on mobile devices

Vector-based mapping is emerging as a preferred format in Location-based Services(LBS), because it can deliver an up-to-date and interactive map visualization. The Progressive Transmission(PT) technique has been developed to enable the ecient transmission of vector data over the internet by delivering various incremental levels of detail(LoD). However, it is still challenging to apply this technique in a mobile context due to many inherent limitations of mobile devices, such as small screen size, slow processors and limited memory. Taking account of these limitations, PT has been extended by developing a framework of ecient data management for the visualization of spatial data on mobile devices. A data generalization framework is proposed and implemented in a software application. This application can signicantly reduce the volume of data for transmission and enable quick access to a simplied version of data while preserving appropriate visualization quality. Using volunteered geographic information as a case-study, the framework shows exibility in delivering up-to-date spatial information from dynamic data sources. Three models of PT are designed and implemented to transmit the additional LoD renements: a full scale PT as an inverse of generalisation, a viewdependent PT, and a heuristic optimised view-dependent PT. These models are evaluated with user trials and application examples. The heuristic optimised view-dependent PT has shown a signicant enhancement over the traditional PT in terms of bandwidth-saving and smoothness of transitions. A parallel data management strategy associated with three corresponding algorithms has been developed to handle LoD spatial data on mobile clients. This strategy enables the map rendering to be performed in parallel with a process which retrieves the data for the next map location the user will require. A viewdependent approach has been integrated to monitor the volume of each LoD for visible area. The demonstration of a exible rendering style shows its potential use in visualizing dynamic geoprocessed data. Future work may extend this to integrate topological constraints and semantic constraints for enhancing the vector map visualization

Transmission adaptative de modèles 3D massifs

Avec les progrès de l'édition de modèles 3D et des techniques de reconstruction 3D, de plus en plus de modèles 3D sont disponibles et leur qualité augmente. De plus, le support de la visualisation 3D sur le web s'est standardisé ces dernières années. Un défi majeur est donc de transmettre des modèles massifs à distance et de permettre aux utilisateurs de visualiser et de naviguer dans ces environnements virtuels. Cette thèse porte sur la transmission et l'interaction de contenus 3D et propose trois contributions majeures. Tout d'abord, nous développons une interface de navigation dans une scène 3D avec des signets -- de petits objets virtuels ajoutés à la scène sur lesquels l'utilisateur peut cliquer pour atteindre facilement un emplacement recommandé. Nous décrivons une étude d'utilisateurs où les participants naviguent dans des scènes 3D avec ou sans signets. Nous montrons que les utilisateurs naviguent (et accomplissent une tâche donnée) plus rapidement en utilisant des signets. Cependant, cette navigation plus rapide a un inconvénient sur les performances de la transmission : un utilisateur qui se déplace plus rapidement dans une scène a besoin de capacités de transmission plus élevées afin de bénéficier de la même qualité de service. Cet inconvénient peut être atténué par le fait que les positions des signets sont connues à l'avance : en ordonnant les faces du modèle 3D en fonction de leur visibilité depuis un signet, on optimise la transmission et donc, on diminue la latence lorsque les utilisateurs cliquent sur les signets. Deuxièmement, nous proposons une adaptation du standard de transmission DASH (Dynamic Adaptive Streaming over HTTP), très utilisé en vidéo, à la transmission de maillages texturés 3D. Pour ce faire, nous divisons la scène en un arbre k-d où chaque cellule correspond à un adaptation set DASH. Chaque cellule est en outre divisée en segments DASH d'un nombre fixe de faces, regroupant des faces de surfaces comparables. Chaque texture est indexée dans son propre adaptation set à différentes résolutions. Toutes les métadonnées (les cellules de l'arbre k-d, les résolutions des textures, etc.) sont référencées dans un fichier XML utilisé par DASH pour indexer le contenu: le MPD (Media Presentation Description). Ainsi, notre framework hérite de la scalabilité offerte par DASH. Nous proposons ensuite des algorithmes capables d'évaluer l'utilité de chaque segment de données en fonction du point de vue du client, et des politiques de transmission qui décident des segments à télécharger. Enfin, nous étudions la mise en place de la transmission et de la navigation 3D sur les appareils mobiles. Nous intégrons des signets dans notre version 3D de DASH et proposons une version améliorée de notre client DASH qui bénéficie des signets. Une étude sur les utilisateurs montre qu'avec notre politique de chargement adaptée aux signets, les signets sont plus susceptibles d'être cliqués, ce qui améliore à la fois la qualité de service et la qualité d'expérience des utilisateur

Data Management in Location-Dependent Information Services: Challenges and Issues

this article, we discuss location -dependent information access in a mobile-pervasive environment, in particular in a cellular mobile system, and present new research issues arising from on-demand access, broadcast, and data cachin

From Traditional Adaptive Data Caching to Adaptive Context Caching: A Survey

Context data is in demand more than ever with the rapid increase in the development of many context-aware Internet of Things applications. Research in context and context-awareness is being conducted to broaden its applicability in light of many practical and technical challenges. One of the challenges is improving performance when responding to large number of context queries. Context Management Platforms that infer and deliver context to applications measure this problem using Quality of Service (QoS) parameters. Although caching is a proven way to improve QoS, transiency of context and features such as variability, heterogeneity of context queries pose an additional real-time cost management problem. This paper presents a critical survey of state-of-the-art in adaptive data caching with the objective of developing a body of knowledge in cost- and performance-efficient adaptive caching strategies. We comprehensively survey a large number of research publications and evaluate, compare, and contrast different techniques, policies, approaches, and schemes in adaptive caching. Our critical analysis is motivated by the focus on adaptively caching context as a core research problem. A formal definition for adaptive context caching is then proposed, followed by identified features and requirements of a well-designed, objective optimal adaptive context caching strategy.Comment: This paper is currently under review with ACM Computing Surveys Journal at this time of publishing in arxiv.or

Multimedia Correlation Analysis in Unstructured Peer-to-Peer Network

Recent years saw the rapid development of peer-topeer (P2P) networks in a great variety of applications. However, similarity-based k-nearest-neighbor retrieval (k-NN) is still a challenging task in P2P networks due to the multiple constraints such as the dynamic topologies and the unpredictable data updates. Caching is an attractive solution that reduces network traffic and hence could remedy the technological constraints of P2P networks. However, traditional caching techniques have some major shortcomings that make them unsuitable for similarity search, such as the lack of semantic locality representation and the rigidness of exact matching on data objects. To facilitate the efficient similarity search, we propose semantic-aware caching scheme (SAC) in this paper. The proposed scheme is hierarchy-free, fully dynamic, non-flooding, and do not add much system overhead. By exploring the content distribution, SAC drastically reduces the cost of similarity-based k-NN retrieval in P2P networks. The performance of SAC is evaluated through simulation study and compared against several search schemes as advanced in the literature

Active caching for recommender systems

Web users are often overwhelmed by the amount of information available while carrying out browsing and searching tasks. Recommender systems substantially reduce the information overload by suggesting a list of similar documents that users might find interesting. However, generating these ranked lists requires an enormous amount of resources that often results in access latency. Caching frequently accessed data has been a useful technique for reducing stress on limited resources and improving response time. Traditional passive caching techniques, where the focus is on answering queries based on temporal locality or popularity, achieve a very limited performance gain. In this dissertation, we are proposing an ‘active caching’ technique for recommender systems as an extension of the caching model. In this approach estimation is used to generate an answer for queries whose results are not explicitly cached, where the estimation makes use of the partial order lists cached for related queries. By answering non-cached queries along with cached queries, the active caching system acts as a form of query processor and offers substantial improvement over traditional caching methodologies. Test results for several data sets and recommendation techniques show substantial improvement in the cache hit rate, byte hit rate and CPU costs, while achieving reasonable recall rates. To ameliorate the performance of proposed active caching solution, a shared neighbor similarity measure is introduced which improves the recall rates by eliminating the dependence on monotinicity in the partial order lists. Finally, a greedy balancing cache selection policy is also proposed to select most appropriate data objects for the cache that help to improve the cache hit rate and recall further

Multimedia

The nowadays ubiquitous and effortless digital data capture and processing capabilities offered by the majority of devices, lead to an unprecedented penetration of multimedia content in our everyday life. To make the most of this phenomenon, the rapidly increasing volume and usage of digitised content requires constant re-evaluation and adaptation of multimedia methodologies, in order to meet the relentless change of requirements from both the user and system perspectives. Advances in Multimedia provides readers with an overview of the ever-growing field of multimedia by bringing together various research studies and surveys from different subfields that point out such important aspects. Some of the main topics that this book deals with include: multimedia management in peer-to-peer structures & wireless networks, security characteristics in multimedia, semantic gap bridging for multimedia content and novel multimedia applications

Human Mobility and Application Usage Prediction Algorithms for Mobile Devices

Mobile devices such as smartphones and smart watches are ubiquitous companions of humans’ daily life. Since 2014, there are more mobile devices on Earth than humans. Mobile applications utilize sensors and actuators of these devices to support individuals in their daily life. In particular, 24% of the Android applications leverage users’ mobility data. For instance, this data allows applications to understand which places an individual typically visits. This allows providing her with transportation information, location-based advertisements, or to enable smart home heating systems. These and similar scenarios require the possibility to access the Internet from everywhere and at any time. To realize these scenarios 83% of the applications available in the Android Play Store require the Internet to operate properly and therefore access it from everywhere and at any time. Mobile applications such as Google Now or Apple Siri utilize human mobility data to anticipate where a user will go next or which information she is likely to access en route to her destination. However, predicting human mobility is a challenging task. Existing mobility prediction solutions are typically optimized a priori for a particular application scenario and mobility prediction task. There is no approach that allows for automatically composing a mobility prediction solution depending on the underlying prediction task and other parameters. This approach is required to allow mobile devices to support a plethora of mobile applications running on them, while each of the applications support its users by leveraging mobility predictions in a distinct application scenario. Mobile applications rely strongly on the availability of the Internet to work properly. However, mobile cellular network providers are struggling to provide necessary cellular resources. Mobile applications generate a monthly average mobile traffic volume that ranged between 1 GB in Asia and 3.7 GB in North America in 2015. The Ericsson Mobility Report Q1 2016 predicts that by the end of 2021 this mobile traffic volume will experience a 12-fold increase. The consequences are higher costs for both providers and consumers and a reduced quality of service due to congested mobile cellular networks. Several countermeasures can be applied to cope with these problems. For instance, mobile applications apply caching strategies to prefetch application content by predicting which applications will be used next. However, existing solutions suffer from two major shortcomings. They either (1) do not incorporate traffic volume information into their prefetching decisions and thus generate a substantial amount of cellular traffic or (2) require a modification of mobile application code. In this thesis, we present novel human mobility and application usage prediction algorithms for mobile devices. These two major contributions address the aforementioned problems of (1) selecting a human mobility prediction model and (2) prefetching of mobile application content to reduce cellular traffic. First, we address the selection of human mobility prediction models. We report on an extensive analysis of the influence of temporal, spatial, and phone context data on the performance of mobility prediction algorithms. Building upon our analysis results, we present (1) SELECTOR – a novel algorithm for selecting individual human mobility prediction models and (2) MAJOR – an ensemble learning approach for human mobility prediction. Furthermore, we introduce population mobility models and demonstrate their practical applicability. In particular, we analyze techniques that focus on detection of wrong human mobility predictions. Among these techniques, an ensemble learning algorithm, called LOTUS, is designed and evaluated. Second, we present EBC – a novel algorithm for prefetching mobile application content. EBC’s goal is to reduce cellular traffic consumption to improve application content freshness. With respect to existing solutions, EBC presents novel techniques (1) to incorporate different strategies for prefetching mobile applications depending on the available network type and (2) to incorporate application traffic volume predictions into the prefetching decisions. EBC also achieves a reduction in application launch time to the cost of a negligible increase in energy consumption. Developing human mobility and application usage prediction algorithms requires access to human mobility and application usage data. To this end, we leverage in this thesis three publicly available data set. Furthermore, we address the shortcomings of these data sets, namely, (1) the lack of ground-truth mobility data and (2) the lack of human mobility data at short-term events like conferences. We contribute with JK2013 and UbiComp Data Collection Campaign (UbiDCC) two human mobility data sets that address these shortcomings. We also develop and make publicly available a mobile application called LOCATOR, which was used to collect our data sets. In summary, the contributions of this thesis provide a step further towards supporting mobile applications and their users. With SELECTOR, we contribute an algorithm that allows optimizing the quality of human mobility predictions by appropriately selecting parameters. To reduce the cellular traffic footprint of mobile applications, we contribute with EBC a novel approach for prefetching of mobile application content by leveraging application usage predictions. Furthermore, we provide insights about how and to what extent wrong and uncertain human mobility predictions can be detected. Lastly, with our mobile application LOCATOR and two human mobility data sets, we contribute practical tools for researchers in the human mobility prediction domain