708 research outputs found
Delivery of Personalized and Adaptive Content to Mobile Devices:A Framework and Enabling Technology
Many innovative wireless applications that aim to provide mobile information access are emerging. Since people have different information needs and preferences, one of the challenges for mobile information systems is to take advantage of the convenience of handheld devices and provide personalized information to the right person in a preferred format. However, the unique features of wireless networks and mobile devices pose challenges to personalized mobile content delivery. This paper proposes a generic framework for delivering personalized and adaptive content to mobile users. It introduces a variety of enabling technologies and highlights important issues in this area. The framework can be applied to many applications such as mobile commerce and context-aware mobile services
How Question Answering Technology Helps to Locate Malevolent Online Content
The inherent lack of control over the Internet content resulted in proliferation of online material that can be potentially detrimental. For example, the infamous âAnarchist Cookbookâ teaching how to make weapons, home made bombs, and poisons, keeps re-appearing in various places. Some websites teach how to break into computer networks to steal passwords and credit card information. Law enforcement, security experts, and public watchdogs started to locate, monitor, and act when such malevolent content surfaces on the Internet. Since the resources of law enforcement are limited, it may take some time before potentially malevolent content is located, enough for it to disseminate and cause harm. Currently applied approach for searching the content of the Internet, available for law enforcement and public watchdogs is by using a search engine, such as Google, AOL, MSN, etc. We have suggested and empirically evaluated an alternative technology (called automated question answering or QA) capable of locating potentially malevolent online content. We have implemented a proof-of-concept prototype that is capable of finding web pages that may potentially contain the answers to specified questions (e.g. âHow to steal a password?â). Using students as subjects in a controlled experiment, we have empirically established that our QA prototype finds web pages that are more likely to provide answers to given questions than simple keyword search using Google. This suggests that QA technology can be a good replacement or an addition to the traditional keyword searching for the task of locating malevolent online content and, possibly, for a more general task of interactive online information exploration
Interface Design for Mobile Applications
Interface design is arguably one of the most important issues in the development of mobile applications. Mobile users often suffer from the poor interface design that seriously hinders the usability of those mobile applications. The major challenge in the interface design of mobile applications is caused by the unique features of mobile devices, such as small screen size, low resolution, and inefficient data entry methods. Therefore, there is a pressing need of theoretical frameworks or guidelines for designing effective and user-friendly interfaces for mobile applications. Based on a comprehensive literature review, this paper proposes a novel framework for the design of effective mobile interfaces. This framework consists of four major components, namely information presentation, data entry methods, mobile users, and context. We also provide a set of practical interface design guidelines and some insights into what factors should be taken into consideration while designing interfaces for mobile applications
Personalized web search using clickthrough data and web page rating
Personalization of Web search is to carry out retrieval for each user incorporating his/her interests. We propose a novel technique to construct personalized information retrieval model from the users' clickthrough data and Web page ratings. This model builds on the userbased collaborative filtering technology and the top-N resource recommending algorithm, which consists of three parts: user profile, user-based collaborative filtering, and the personalized search model. Firstly, we conduct user's preference score to construct the user profile from clicked sequence score and Web page rating. Then it attains similar users with a given user by user-based collaborative filtering algorithm and calculates the recommendable Web page scoring value. Finally, personalized informaion retrieval be modeled by three case applies (rating information for the user himself; at least rating information by similar users; not make use of any rating information). Experimental results indicate that our technique significantly improves the search performance. © 2012 ACADEMY PUBLISHER
Exploiting conceptual spaces for ontology integration
The widespread use of ontologies raises the need to integrate distinct conceptualisations. Whereas the symbolic approach of established representation standards â based on first-order logic (FOL) and syllogistic reasoning â does not implicitly represent semantic similarities, ontology mapping addresses this problem by aiming at establishing formal relations between a set of knowledge entities which represent the same or a similar meaning in distinct ontologies. However, manually or semi-automatically identifying similarity relationships is costly. Hence, we argue, that representational facilities are required which enable to implicitly represent similarities. Whereas Conceptual Spaces (CS) address similarity computation through the representation of concepts as vector spaces, CS rovide neither an implicit representational mechanism nor a means to represent arbitrary relations between concepts or instances. In order to overcome these issues, we propose a hybrid knowledge representation approach which extends FOL-based ontologies with a conceptual grounding through a set of CS-based representations. Consequently, semantic similarity between instances â represented as members in CS â is indicated by means of distance metrics. Hence, automatic similarity detection across distinct ontologies is supported in order to facilitate ontology integration
Algorithmes passant aÌ lâeÌchelle pour la gestion de donneÌes du Web seÌmantique sur les platformes cloud
In order to build smart systems, where machines are able to reason exactly like humans, data with semantics is a major requirement. This need led to the advent of the Semantic Web, proposing standard ways for representing and querying data with semantics. RDF is the prevalent data model used to describe web resources, and SPARQL is the query language that allows expressing queries over RDF data. Being able to store and query data with semantics triggered the development of many RDF data management systems. The rapid evolution of the Semantic Web provoked the shift from centralized data management systems to distributed ones. The first systems to appear relied on P2P and client-server architectures, while recently the focus moved to cloud computing.Cloud computing environments have strongly impacted research and development in distributed software platforms. Cloud providers offer distributed, shared-nothing infrastructures that may be used for data storage and processing. The main features of cloud computing involve scalability, fault-tolerance, and elastic allocation of computing and storage resources following the needs of the users.This thesis investigates the design and implementation of scalable algorithms and systems for cloud-based Semantic Web data management. In particular, we study the performance and cost of exploiting commercial cloud infrastructures to build Semantic Web data repositories, and the optimization of SPARQL queries for massively parallel frameworks.First, we introduce the basic concepts around Semantic Web and the main components and frameworks interacting in massively parallel cloud-based systems. In addition, we provide an extended overview of existing RDF data management systems in the centralized and distributed settings, emphasizing on the critical concepts of storage, indexing, query optimization, and infrastructure. Second, we present AMADA, an architecture for RDF data management using public cloud infrastructures. We follow the Software as a Service (SaaS) model, where the complete platform is running in the cloud and appropriate APIs are provided to the end-users for storing and retrieving RDF data. We explore various storage and querying strategies revealing pros and cons with respect to performance and also to monetary cost, which is a important new dimension to consider in public cloud services. Finally, we present CliqueSquare, a distributed RDF data management system built on top of Hadoop, incorporating a novel optimization algorithm that is able to produce massively parallel plans for SPARQL queries. We present a family of optimization algorithms, relying on n-ary (star) equality joins to build flat plans, and compare their ability to find the flattest possibles. Inspired by existing partitioning and indexing techniques we present a generic storage strategy suitable for storing RDF data in HDFS (Hadoopâs Distributed File System). Our experimental results validate the efficiency and effectiveness of the optimization algorithm demonstrating also the overall performance of the system.Afin de construire des systĂšmes intelligents, ouÌ les machines sont capables de raisonner exactement comme les humains, les donnĂ©es avec sĂ©mantique sont une exigence majeure. Ce besoin a conduit aÌ lâapparition du Web sĂ©mantique, qui propose des technologies standards pour reprĂ©senter et interroger les donnĂ©es avec sĂ©mantique. RDF est le modĂšle rĂ©pandu destineÌ aÌ dĂ©crire de façon formelle les ressources Web, et SPARQL est le langage de requĂȘte qui permet de rechercher, dâajouter, de modifier ou de supprimer des donnĂ©es RDF. Ătre capable de stocker et de rechercher des donnĂ©es avec sĂ©mantique a engendreÌ le dĂ©veloppement des nombreux systĂšmes de gestion des donnĂ©es RDF.LâĂ©volution rapide du Web sĂ©mantique a provoqueÌ le passage de systĂšmes de gestion des donnĂ©es centralisĂ©es aÌ ceux distribuĂ©s. Les premiers systĂšmes Ă©taient fondĂ©s sur les architectures pair-aÌ-pair et client-serveur, alors que rĂ©cemment lâattention se porte sur le cloud computing.Les environnements de cloud computing ont fortement impacteÌ la recherche et dĂ©veloppement dans les systĂšmes distribuĂ©s. Les fournisseurs de cloud offrent des infrastructures distribuĂ©es autonomes pouvant ĂȘtre utilisĂ©es pour le stockage et le traitement des donnĂ©es. Les principales caractĂ©ristiques du cloud computing impliquent lâĂ©volutivitĂ©Ì, la tolĂ©rance aux pannes et lâallocation Ă©lastique des ressources informatiques et de stockage en fonction des besoins des utilisateurs.Cette thĂšse Ă©tudie la conception et la mise en Ćuvre dâalgorithmes et de systĂšmes passant aÌ lâĂ©chelle pour la gestion des donnĂ©es du Web sĂ©mantique sur des platformes cloud. Plus particuliĂšrement, nous Ă©tudions la performance et le coĂ»t dâexploitation des services de cloud computing pour construire des entrepĂŽts de donneÌes du Web sĂ©mantique, ainsi que lâoptimisation de requĂȘtes SPARQL pour les cadres massivement parallĂšles.Tout dâabord, nous introduisons les concepts de base concernant le Web seÌmantique et les principaux composants des systeÌmes fondeÌs sur le cloud. En outre, nous preÌsentons un aperçu des systeÌmes de gestion des donneÌes RDF (centraliseÌs et distribueÌs), en mettant lâaccent sur les concepts critiques de stockage, dâindexation, dâoptimisation des requeÌtes et dâinfrastructure.Ensuite, nous preÌsentons AMADA, une architecture de gestion de donneÌes RDF utilisant les infrastructures de cloud public. Nous adoptons le modeÌle de logiciel en tant que service (software as a service - SaaS), ouÌ la plateforme reÌside dans le cloud et des APIs approprieÌes sont mises aÌ disposition des utilisateurs, afin quâils soient capables de stocker et de reÌcupeÌrer des donneÌes RDF. Nous explorons diverses strateÌgies de stockage et dâinterrogation, et nous eÌtudions leurs avantages et inconveÌnients au regard de la performance et du couÌt moneÌtaire, qui est une nouvelle dimension importante aÌ consideÌrer dans les services de cloud public.Enfin, nous preÌsentons CliqueSquare, un systeÌme distribueÌ de gestion des donneÌes RDF baseÌ sur Hadoop. CliqueSquare inteÌgre un nouvel algorithme dâoptimisation qui est capable de produire des plans massivement paralleÌles pour des requeÌtes SPARQL. Nous preÌsentons une famille dâalgorithmes dâoptimisation, sâappuyant sur les eÌquijointures n- aires pour geÌneÌrer des plans plats, et nous comparons leur capaciteÌ aÌ trouver les plans les plus plats possibles. InspireÌs par des techniques de partitionnement et dâindexation existantes, nous preÌsentons une strateÌgie de stockage geÌneÌrique approprieÌe au stockage de donneÌes RDF dans HDFS (Hadoop Distributed File System). Nos reÌsultats expeÌrimentaux valident lâeffectiviteÌ et lâefficaciteÌ de lâalgorithme dâoptimisation deÌmontrant eÌgalement la performance globale du systeÌme
- âŠ