Исследование и выбор криптографических стандартов на основе интеллектуального анализа документов

This paper discusses the problems of application and choice of cryptographic standards taking into account user requirements and preferences. User profiles are created by means of the ontology apparatus. On the basis of user profiles and document features an appropriate set of documents is formed, the elements of which are then arranged according to the degree of compliance to user requirements. Various filtration methods, such as collaborative filtering, content analysis and filtering, as well as hybrid methods combining both approaches, are used. Thus, a recommender system for choosing cryptographic standards and algorithms is built. If there are several user selection criteria, it is reasonable to apply an integral index of object’s relevance to user preferences. This index is defined as the weighed sum of the particular indices.В данной статье исследуются проблемы применимости и выбора криптографических стандартов с учетом предпочтений и требований потенциального пользователя. Профили пользователя формируются с помощью онтологических методов. На основе профилей пользователей и характеристик документов формируется набор документов, которые могут подойти конкретному пользователю, и элементы этого набора ранжируются по вероятности соответствия его требованиям. При формировании набора документов используются различные методы фильтрации: коллаборативная фильтрация, анализ и фильтрация контента, а также гибридные методы, совмещающие оба подхода. Таким образом, создается рекомендующая система выбора криптографических стандартов и алгоритмов. При наличии нескольких пользовательских критериев выбора объекта целесообразно использовать интегральный показатель соответствия объекта, который вычисляется в виде взвешенной суммы показателей

From the Client-Server Architecture to the Information Service Architecture

This paper aims to justify the need for refining the concept of the traditional three-tier client-server architecture to address the drastic changes we have encountered in the form of information processing needs demanded by the public and the information processing services supplied to the public. The paper suggests much needed revisions to the traditional approach and demonstrates how the Information Service Architecture fits into the realm of future systems development by using a fairly complex example of an information system implementation

Moving To The Cloud: Transitioning From Client-Server To Service Architecture

This paper makes the case that the traditional three-tier client-server architecture requires a major overhaul to address the changing and rapidly increasing information processing and services needs of consumers. Revisions to the conventional architecture model are suggested and two examples of information systems applications are discussed to illustrate how the new information service architecture fits into the realm of future systems development

diffeRS: A Mobile Recommender Service

Thanks to advances in mobile technology, modern mobile devices have become essential companions, assisting their users in attaining their daily tasks. It will not be long before these devices will become recommending companions, advising users about what data (e.g., restaurants) and what services (e.g., podcast channels) they may enjoy in the local area at the present time. Because of the very nature of the items (both data and services) being suggested (i.e., location dependent and mobile with respect to the consuming user), recommendations cannot be computed on central servers and then pushed to the users. Rather, a novel decentralised mobile recommender service will have to be developed and deployed; instead of relying on global knowledge about users' profiles, such service will have to exploit the wisdom of local communities to compute recommendations. Moreover, because of resource limitations of mobile devices, the algorithms it will employ will have to be computationally light. In this paper, we propose diffeRS, a totally decentralised mobile recommender service specifically designed for pervasive environments. diffeRS crafts a virtual view of the local community's preferences, by exchanging users' profiles via radio technology (e.g., Bluetooth) during periods of colocation. Profiles are stored locally and recommendations are computed using a lightweight algorithm. As our experimental evaluations demonstrate, diffeRS achieves an accuracy and coverage that are comparable to those of centralized recommender systems in use today. © 2010 IEEE

Distributed Collaborative Filtering for Peer-to-Peer File Sharing Systems

Peer-to-peer networks are becoming more and more popular to share information such as, for example, multimedia files. Since this information is stored locally at the different peers, it is necessary to facilitate the search in an intelligent way. Collaborative filtering is such a search technique that enables to incorporate the preferences of a user that can be learned from the download activities of the users. To be effective collaborative filtering requires a large database that captures these activities. Within a peerto-peer network this is, however, not readily available. Here, we propose a collaborative filtering approach that is self-organizing and operates in a distributed way. Information about the similarity between multimedia files (items) is stored locally at these items in so called item-based buddy tables. We propose to use the language model (popular within information retrieval) to build recommendations for the different users based on the buddy tables of those items a user has downloaded previously (indicating the preference of the user). We have tested and compared our distributed collaborative filtering approach to centralized collaborative filtering and showed that it has similar performance. It is therefore a promising technique to facilitate the search for information in peer-to-peer networks.

Distributed Collaborative Filtering for Peer-to-Peer File Sharing Systems

Collaborative filtering requires a centralized rating database. However, within a peer-to-peer network such a centralized database is not readily available. In this paper, we propose a fully distributed collaborative filtering method that is self-organizing and operates in a distributed way. Similarity ranks between multimedia files (items) are calculated by log-based user profiles and are stored locally at these items in so-called buddy tables. This intuitively creates a semantic overlay to organize multimedia files. Based on this semantic overlay and the items that a user has downloaded previously (indicating the profile of the user), recommendations can be performed and the recommended items can be easily located. We have tested our distributed collaborative filtering approach and compared it to centralized collaborative filtering, showing that it has similar performance. It is therefore a promising technique to facilitate filtering for relevant multimedia data in P2P networks

Modèle multi-agents pour le filtrage collaboratif de l'information

Les systèmes de recommandation sont nés de la volonté de pallier le problème de surcharge d'information du web. Combinant des techniques de filtrage d'information, personnalisation, intelligence artificielle, réseaux sociaux et interaction personne-machine, les systèmes de recommandation fournissent à des utilisateurs des suggestions qui répondent à leurs besoins et préférences informationnelles. En effet, les systèmes de recommandation sont particulièrement sollicités dans les applications de commerce électronique. Cependant, ce type de système a été en grande partie confiné à une architecture centralisée. Récemment, l'architecture distribuée a connu une popularité croissante, comme en témoigne par exemple, les réseaux pair-à-pair (« peer-to-peer »), le calcul distribué (« Grid computing »), le web sémantique, etc., et s'impose peu à peu comme une alternative à l'approche client/serveur classique. L'hypothèse des chercheurs est que les systèmes de recommandation peuvent tirer profit d'une architecture distribuée. Dans cette thèse, nous étudions les défis que posent les systèmes de recommandation distribués et nous proposons une nouvelle architecture pair-à-pair, de filtrage collaboratif, basée sur la discrimination du voisinage. Nous étudions l'évolution de la performance, de la couverture et de la qualité des prédictions pour différentes techniques de recommandation. En outre, nous identifions la méthode de recommandation la plus efficace pour cette nouvelle architecture pair-à-pair. Bien que cette thèse se concentre essentiellement sur le domaine décentralisé de système de recommandation, nos contributions ne se limitent pas strictement à ce domaine de recherche. En effet, ces contributions touchent des problèmes de recherche dans plusieurs autres domaines de recherche (système multi-agents, gestions profils utilisateurs, réduction de la complexité computationnelle, collecte des préférences utilisateurs, PageRank, etc.). ______________________________________________________________________________ MOTS-CLÉS DE L’AUTEUR : Filtrage de l'information, Filtrage collaboratif, Système de recommandation, Système distribué, Agent social

An Ontology Centric Architecture For Mediating Interactions In Semantic Web-Based E-Commerce Environments

Information freely generated, widely distributed and openly interpreted is a rich source of creative energy in the digital age that we live in. As we move further into this irrevocable relationship with self-growing and actively proliferating information spaces, we are also finding ourselves overwhelmed, disheartened and powerless in the presence of so much information. We are at a point where, without domain familiarity or expert guidance, sifting through the copious volumes of information to find relevance quickly turns into a mundane task often requiring enormous patience. The realization of accomplishment soon turns into a matter of extensive cognitive load, serendipity or just plain luck. This dissertation describes a theoretical framework to analyze user interactions based on mental representations in a medium where the nature of the problem-solving task emphasizes the interaction between internal task representation and the external problem domain. The framework is established by relating to work in behavioral science, sociology, cognitive science and knowledge engineering, particularly Herbert Simon’s (1957; 1989) notion of satisficing on bounded rationality and Schön’s (1983) reflective model. Mental representations mediate situated actions in our constrained digital environment and provide the opportunity for completing a task. Since assistive aids to guide situated actions reduce complexity in the task environment (Vessey 1991; Pirolli et al. 1999), the framework is used as the foundation for developing mediating structures to express the internal, external and mental representations. Interaction aids superimposed on mediating structures that model thought and action will help to guide the “perpetual novice” (Borgman 1996) through the vast digital information spaces by orchestrating better cognitive fit between the task environment and the task solution. This dissertation presents an ontology centric architecture for mediating interactions is presented in a semantic web based e-commerce environment. The Design Science approach is applied for this purpose. The potential of the framework is illustrated as a functional model by using it to model the hierarchy of tasks in a consumer decision-making process as it applies in an e-commerce setting. Ontologies are used to express the perceptual operations on the external task environment, the intuitive operations on the internal task representation, and the constraint satisfaction and situated actions conforming to reasoning from the cognitive fit. It is maintained that actions themselves cannot be enforced, but when the meaning from mental imagery and the task environment are brought into coordination, it leads to situated actions that change the present situation into one closer to what is desired. To test the usability of the ontologies we use the Web Ontology Language (OWL) to express the semantics of the three representations. We also use OWL to validate the knowledge representations and to make rule-based logical inferences on the ontological semantics. An e-commerce application was also developed to show how effective guidance can be provided by constructing semantically rich target pages from the knowledge manifested in the ontologies