The Mirror MMDBMS architecture

Handling large collections of digitized multimedia data, usually referred to as multimedia digital libraries, is a major challenge for information technology. The Mirror DBMS is a research database system that is developed to better understand the kind of data management that is required in the context of multimedia digital libraries (see also URL http://www.cs.utwente.nl/~arjen/mmdb.html). Its main features are an integrated approach to both content management and (traditional) structured data management, and the implementation of an extensible object-oriented logical data model on a binary relational physical data model. The focus of this work is aimed at design for scalability

Towards an All-Purpose Content-Based Multimedia Information Retrieval System

The growth of multimedia collections - in terms of size, heterogeneity, and variety of media types - necessitates systems that are able to conjointly deal with several forms of media, especially when it comes to searching for particular objects. However, existing retrieval systems are organized in silos and treat different media types separately. As a consequence, retrieval across media types is either not supported at all or subject to major limitations. In this paper, we present vitrivr, a content-based multimedia information retrieval stack. As opposed to the keyword search approach implemented by most media management systems, vitrivr makes direct use of the object's content to facilitate different types of similarity search, such as Query-by-Example or Query-by-Sketch, for and, most importantly, across different media types - namely, images, audio, videos, and 3D models. Furthermore, we introduce a new web-based user interface that enables easy-to-use, multimodal retrieval from and browsing in mixed media collections. The effectiveness of vitrivr is shown on the basis of a user study that involves different query and media types. To the best of our knowledge, the full vitrivr stack is unique in that it is the first multimedia retrieval system that seamlessly integrates support for four different types of media. As such, it paves the way towards an all-purpose, content-based multimedia information retrieval system

Semantics-based selection of everyday concepts in visual lifelogging

Concept-based indexing, based on identifying various semantic concepts appearing in multimedia, is an attractive option for multimedia retrieval and much research tries to bridge the semantic gap between the media’s low-level features and high-level semantics. Research into concept-based multimedia retrieval has generally focused on detecting concepts from high quality media such as broadcast TV or movies, but it is not well addressed in other domains like lifelogging where the original data is captured with poorer quality. We argue that in noisy domains such as lifelogging, the management of data needs to include semantic reasoning in order to deduce a set of concepts to represent lifelog content for applications like searching, browsing or summarisation. Using semantic concepts to manage lifelog data relies on the fusion of automatically-detected concepts to provide a better understanding of the lifelog data. In this paper, we investigate the selection of semantic concepts for lifelogging which includes reasoning on semantic networks using a density-based approach. In a series of experiments we compare different semantic reasoning approaches and the experimental evaluations we report on lifelog data show the efficacy of our approach

Learning roadmaps for Higher Education

An integrated platform for the support of teaching activities as been developed and deployed at the Aveiro Norte Polytechnic School of the University of Aveiro. In this paper we present an approach to Learning Roadmaps for Higher Education based on this platform. The aprend.e platform – Electronic Integrated System for Learning and Training - has at its core a Learning Management System with a number of plugins. It represents a new challenge for the University of Aveiro for higher education and is already being at its core is the concept of learning roadmaps that act upon two fundamental axes: education and learning. For the teachers, it aims at becoming a self-supporting tool that stimulates the organization and management of the course materials (lectures, presentations, multimedia content, and evaluation materials, amongst others). For the students, the learning roadmap aims at promoting self-study and supervised study, endowing the pupil with the capabilities to find the relevant information and to capture the concepts in the study materials. The outcome will be a stimulating learning process together with an organized management of those materials

Using the Internet to improve university education

Up to this point, university education has largely remained unaffected by the developments of novel approaches to web-based learning. The paper presents a principled approach to the design of problem-oriented, web-based learning at the university level. The principles include providing authentic contexts with multimedia, supporting collaborative knowledge construction, making thinking visible with dynamic visualisation, quick access to content resources via information and communication technologies, and flexible support by tele-tutoring. These principles are used in the MUNICS learning environment, which is designed to support students of computer science to apply their factual knowledge from the lectures to complex real-world problems. For example, students may model the knowledge management in an educational organisation with a graphical simulation tool. Some more general findings from a formative evaluation study with the MUNICS prototype are reported and discussed. For example, the students' ignorance of the additional content resources is discussed in the light of the well-known finding of insufficient use of help systems in software applications

Multimedia content delivery for emerging satellite networks

Multimedia content delivery over satellite systems is considered as a promising service in the emerging networks. The aim of this work is to design a novel radio resource management (RRM) algorithm for efficiently managing multicast multimedia content transmission over satellite network. The proposed approach performs the spectrum management on a per-group basis, by splitting multicast terminals into different subgroups according to the experienced channel qualities. We demonstrate that subgrouping policy defined by the authors as multicast subgrouping-maximum satisfaction index (MS-MSI), is based on a new metric (i.e., MSI), which overcomes the weakness of the previous techniques proposed in literature and provides the best trade-off between user throughput and fairness. As a further result, we demonstrate that MS-MSI is robust to the long propagation delay of satellite links. An extensive simulation campaign has been conducted by considering several satellite environments

Spatio-Temporal Motifs for Optimized Vehicle-to-Vehicle (V2V) Communications

Caching popular contents in vehicle-to-vehicle (V2V) communication networks is expected to play an important role in road traffic management, the realization of intelligent transportation systems (ITSs), and the delivery of multimedia content across vehicles. However, for effective caching, the network must dynamically choose the optimal set of cars that will cache popular content and disseminate it in the entire network. However, most of the existing prior art on V2V caching is restricted to cache placement that is solely based on location and user demands and does not account for the large-scale spatio-temporal variations in V2V communication networks. In contrast, in this paper, a novel spatio-temporal caching strategy is proposed based on the notion of temporal graph motifs that can capture spatio-temporal communication patterns in V2V networks. It is shown that, by identifying such V2V motifs, the network can find sub-optimal content placement strategies for effective content dissemination across a vehicular network. Simulation results using real traces from the city of Cologne show that the proposed approach can increase the average data rate by $45\%$ for different network scenarios

Design of an emulation framework for evaluating large-scale open content aware networks

The popularity of multimedia services has resulted in new revenue opportunities for network and service providers but has also introduced important new challenges. The large amount of resources and stringent quality requirements imposed by multimedia services has triggered the need for open content aware networks, where specific management algorithms that optimize the delivery of multimedia services can be dynamically plugged in when required. In the past, a plethora of algorithms have been proposed ranging from specific cache algorithms to video client heuristics that are optimized for a specific multimedia service type and its corresponding delivery. However, it remains difficult to accurately characterize the performance of these algorithms and investigate the impact of an actual deployment in multimedia services. In this paper, we present a framework that allows evaluating the performance of such algorithms for open content aware networks. The proposed evaluation framework has two important advantages. First, it performs an emulation of the novel algorithms instead of using a simulation approach, which is often carried out to characterize performance. Second, the emulation framework allows evaluating the impact of combining different multimedia algorithms with each other. We present the architecture of the emulation framework and discuss the main software components used. Furthermore, we present a performance evaluation of an illustrative use case, which identifies the need for emulation-based evaluation