In dieser Arbeit werden die Mšglichkeiten sicherer Ausgliederung von Datenbanken und inhaltsbasiertem Routing an einen nicht voll vertrauenswŸrdigen Dienstanbieter betrachtet. Wir untersuchen die Grenzen der Sicherheit, die in diesem Szenario erreicht werden kšnnen. Sicherheit wird dabei unter Zuhilfenahme aktueller komplexitŠtstheoretischer Arbeiten definiert. Dies ermšglicht die Verwendung formaler Methoden zur Untersuchung der Bedingungen, unter denen verschiedene Grade von Sicherheit mšglich sind. Die BeitrŠge dieser Dissertation sind im Einzelnen: - Wir untersuchen die Eignung sog. Privacy-Homomorphismen, welche die AusfŸhrung von Operationen auf verschlŸsselten Daten erlauben. Dies dient der Entwicklung von Protokollen zur sicheren Datenbankausgliederung. Weiter beschreiben wir ein allgemeines Framework fŸr sichere Datenbankausgliederung, das auf sog. Volltextsuch-VerschlŸsselungsverfahren basiert. Ferner stellen wir einen Beweis fŸr die Sicherheit und Korrektheit vor. - Wir beschreiben ein neues Volltextsuch-VerschlŸsselungsverfahren, das im Vergleich zu bekannten Arbeiten eine grš§ere Anzahl verschiedener Operationen fŸr das Datenbank-Outsourcing-Problem ermšglicht und signifikant niedrigere Fehlerraten hat. - Wir schlagen einen Ansatz vor, um im Kontext der sicheren Datenbank-Auslagerung Blanko-Zugriffe auf die verschlŸsselten Daten zu verwalten. Verglichen mit existierenden Techniken ist unser Ansatz anwendbar auf generellere Szenarien, ist einfacher und hat Šhnliche Effizienzeigenschaften. - Wir untersuchen die Mšglichkeit des sicheren inhaltsbasierten Routings, in dem wir ein formales Sicherheitsmodell konstruieren, existierende AnsŠtze in diesem Modell bewerten und eine formale Analyse der Mšglichkeit von Vertraulichkeit durchfŸhren. Unser Sicherheitsmodell deckt die UnzulŠnglichkeiten der bestehenden AnsŠtze auf. Schlie§lich beschreiben wir ein inhaltsbasiertes Routingverfahren, welches das Modell erfŸllt. / This thesis considers the possibilities of secure outsourcing of databases and of content-based routing operations to an untrusted service provider. We explore the limits of the security that is achievable in these scenarios. When discussing security, we refer to the state of the art definitions from cryptography and complexity theory. The key contributions of the thesis are the following: - We explore the applicability of cryptographic constructs that allow performing operations over encrypted data, also known as privacy homomorphisms, for creating protocols that could enable secure database outsourcing. We also describe a framework for secure database outsourcing that is based on searchable encryption schemes, and prove its correctness and security. - We describe a new searchable encryption scheme that exceeds existing analogues with regard to certain parameters: compared to the existing works, the proposed scheme allows for performing a larger number of operations over a securely outsourced database and has significantly lower chances of returning erroneous results of a search. - We propose an approach for managing discretionary access to securely outsourced and encrypted databases. Compared to existing techniques, our approach is applicable to more general scenarios, is simpler and has similar performance characteristics. - We examine possibilities of performing a secure content-based routing by building a formal security model that describes a secure content-based routing system, evaluate existing approaches against this model, and provide an analysis of the possibilities for achieving confidentiality when performing the routing. Compared to the existing works, which fail in providing complete confidentiality, our security model considers shortcomings of these solutions. We also describe a content-based routing system that satisfies this model and to the best of our knowledge is the first of its kind to provide a complete confidentiality.

<p>We have 3G on the doorstep but nothing seems to attract ordinary people to this technology. To attract the mass market the telecom industry must show something beyond high bit rates. They must show how ordinary people can take advantage of this new technology. This is done by showing the possibilities of the new technology and by demonstrating applications that it will handle. The telecom industry must convince the telecom operators to invest in this technology and the only thing that matters to them is how much revenue they can make by adopting the upcoming technology. </p><p>To convince the operators industry must show how the operators can charge for the new types of applications that will be introduced soon. This is the main reason why this Master's Thesis has been conducted. The purpose of this thesis is to provide a demonstration to Ericsson's 3G lab in Katrineholm in the form of an IP application with a billing solution. This thesis describes the migration from 1G to 3G and examines existing and future billing strategies as well. </p><p>The IP application is an application that uses progressive streaming in order to stream multimedia content to a PDA connected to a 3G phone. This application is platform independent because it is placed on leading Web servers, Apache and IIS. </p><p>The billing application consists of a number of steps. The first step is logging, which is performed by the Web server on which the streaming application is placed. The second step, processing and billing, is performed in the BGw, which is Ericsson's mediation tool, and the SQL server.The third step is displaying the bill, which is done by using ASP to create an active HTML page.</p>

As information continues to grow at a very fast pace, our ability to access this information effectively does not, and we are often realize how harder is getting to locate an object quickly and easily. The so-called personalization technology is one of the best solutions to this information overload problem: by automatically learning the user profile, personalized information services have the potential to offer users a more proactive and intelligent form of information access that is designed to assist us in finding interesting objects. Recommender systems, which have emerged as a solution to minimize the problem of information overload, provide us with recommendations of content suited to our needs. In order to provide recommendations as close as possible to a user&rsquo / s taste, personalized recommender systems require accurate user models of characteristics, preferences and needs. Collaborative filtering is a widely accepted technique to provide recommendations based on ratings of similar users, But it suffers from several issues like data sparsity and cold start. In one-class collaborative filtering, a special type of collaborative filtering methods that aims to deal with datasets that lack counter-examples, the challenge is even greater, since these datasets are even sparser. In this thesis, we present a series of experiments conducted on a real-life customer purchase database from a major Turkish E-Commerce site. The sparsity problem is handled by the use of content-based technique combined with TFIDF weights, memory based collaborative filtering combined with different similarity measures and also hybrids approaches, and also model based collaborative filtering with the use of Singular Value Decomposition (SVD). Our study showed that the binary similarity measure and SVD outperform conventional measures in this OCCF dataset.

We have 3G on the doorstep but nothing seems to attract ordinary people to this technology. To attract the mass market the telecom industry must show something beyond high bit rates. They must show how ordinary people can take advantage of this new technology. This is done by showing the possibilities of the new technology and by demonstrating applications that it will handle. The telecom industry must convince the telecom operators to invest in this technology and the only thing that matters to them is how much revenue they can make by adopting the upcoming technology. To convince the operators industry must show how the operators can charge for the new types of applications that will be introduced soon. This is the main reason why this Master's Thesis has been conducted. The purpose of this thesis is to provide a demonstration to Ericsson's 3G lab in Katrineholm in the form of an IP application with a billing solution. This thesis describes the migration from 1G to 3G and examines existing and future billing strategies as well. The IP application is an application that uses progressive streaming in order to stream multimedia content to a PDA connected to a 3G phone. This application is platform independent because it is placed on leading Web servers, Apache and IIS. The billing application consists of a number of steps. The first step is logging, which is performed by the Web server on which the streaming application is placed. The second step, processing and billing, is performed in the BGw, which is Ericsson's mediation tool, and the SQL server.The third step is displaying the bill, which is done by using ASP to create an active HTML page.

Content-based retrieval of multimedia data has been still an active research area. The efficient retrieval of video data is proven a difficult task for content-based video retrieval systems. In this thesis study, a Content-Based Video Retrieval (CBVR) system that adapts two different index structures, namely Slim-Tree and BitMatrix, for efficiently retrieving videos based on low-level features such as color, texture, shape and motion is presented. The system represents low-level features of video data with MPEG-7 Descriptors extracted from video shots by using MPEG-7 reference software and stored in a native XML database. The low-level descriptors used in the study are Color Layout (CL), Dominant Color (DC), Edge Histogram (EH), Region Shape (RS) and Motion Activity (MA). Ordered Weighted Averaging (OWA) operator in Slim-Tree and BitMatrix aggregates these features to find final similarity between any two objects. The system supports three different types of queries: exact match queries, k-NN queries and range queries. The experiments included in this study are in terms of index construction, index update, query response time and retrieval efficiency using ANMRR performance metric and precision/recall scores. The experimental results show that using BitMatrix along with Ordered Weighted Averaging method is superior in content-based video retrieval systems.

The tremendous growth of Web has made information overload problem increasingly serious. Users are often confused by huge amount of information available on the internet and they are faced with the problem of finding the most relevant information that meets their needs. Recommender systems have proven to be an important solution approach to this problem. This thesis will present OPENMORE, a movie recommendation system, which is primarily based on content-based filtering technique. The distinctive point of this study lies in the methodology used to construct and update user and item profiles and the optimizations used to fine-tune the constructed user models. The proposed system arranges movie content data as features of a set of dimension slots, where each feature is assigned a stable feature weight regardless of individual movies. These feature weights and the explicit feedbacks provided by the user are then used to construct the user profile, which is fine-tuned through a set of optimization mechanisms. Users are enabled to view their profile, update them and create multiple contexts where they can provide negative and positive feedback for the movies on the feature level.

Recently, it has become more and more difficult for the existing web based systems to locate or retrieve any kind of relevant information, due to the rapid growth of the World Wide Web (WWW) in terms of the information space and the amount of the users in that space. However, in today&#039 / s world, many systems and approaches make it possible for the users to be guided by the recommendations that they provide about new items such as articles, news, books, music, and movies. However, a lot of traditional recommender systems result in failure when the data to be used throughout the recommendation process is sparse. In another sense, when there exists an inadequate number of items or users in the system, unsuccessful recommendations are produced. Within this thesis work, ReMovender, a web based movie recommendation system, which uses a content boosted collaborative filtering approach, will be presented. ReMovender combines the local/global similarity and missing data prediction v techniques in order to handle the previously mentioned sparseness problem effectively. Besides, by putting the content information of the movies into consideration during the item similarity calculations, the goal of making more successful and realistic predictions is achieved.

The evolution of the Internet has brought us into a world that represents a huge amount of information items such as music, movies, books, web pages, etc. with varying quality. As a result of this huge universe of items, people get confused and the question &ldquo / Which one should I choose?&rdquo / arises in their minds. Recommendation Systems address the problem of getting confused about items to choose, and filter a specific type of information with a specific information filtering technique that attempts to present information items that are likely of interest to the user. A variety of information filtering techniques have been proposed for performing recommendations, including content-based and collaborative techniques which are the most commonly used approaches in recommendation systems. This thesis work introduces ReMovender, a content-based movie recommendation system which is empowered by collaborative missing data prediction. The distinctive point of this study lies in the methodology used to correlate the users in the system with one another and the usage of the content information of movies. ReMovender makes it possible for the users to rate movies in a scale from one to five. By using these ratings, it finds similarities among the users in a collaborative manner to predict the missing ratings data. As for the content-based part, a set of movie features are used in order to correlate the movies and produce recommendations for the users.

In this thesis, a system, called Ontology-Driven Video Annotation and Retrieval System (OntoVARS) is developed in order to provide a video management system which is used for ontology-driven semantic content annotation and querying. The proposed system is based on MPEG-7 ontology which provides interoperability and common communication platform with other MPEG-7 ontology compatible systems. The Rhizomik MPEG-7 ontology is used as the core ontology and domain specific ontologies are integrated to the core ontology in order to provide ontology-based video content annotation and querying capabilities to the user. The proposed system supports content-based annotation and spatio-temporal data modeling in video databases by using the domain ontology concepts. Moreover, the system enables ontology-driven query formulation and processing according to the domain ontology instances and concepts. In the developed system, ontology-driven concept querying, spatio-temporal querying, region-based and time-based querying capabilities are performed as simple querying types. Besides these simple query types, compound queries are also generated by combining simple queries with &quot / (&quot / , &quot / )&quot / , &quot / AND&quot / and &quot / OR&quot / operators. For all these query types, the system supports both general and video specific query processing. By this means, the user is able to pose queries on all videos in the video databases as well as the details of a specific video of interest.

Diese Diplomarbeit führt kurz in das Thema Information Retrieval mit den Schwerpunkten Evaluation und Evaluationskampagnen ein. Im Anschluss wird anhand der Nachteile eines vorhandenen Retrieval Systems ein neues Retrieval Framework zur experimentellen Evaluation von Ansätzen aus dem Information Retrieval entworfen und umgesetzt. Die Komponenten des Frameworks sind dabei so abstrakt angelegt, dass verschiedene, bestehende Retrieval Systeme, wie zum Beispiel Apache Lucene oder Terrier, integriert werden können. Anhand einer Referenzimplementierung für den ImageCLEF Photographic Retrieval Task des ImageCLEF Tracks des Cross Language Evaluation Forums wird die Funktionsfähigkeit des Frameworks überprüft und bestätigt.