VIDI-Video project takes on the challenge of creating a substantially enhanced semantic access to video, implemented in a search engine. The engine will boost the performance of video search by forming a 1000 element thesaurus detecting instances of audio, visual or mixed-media content. This project's approach is to let the system learn many, possibly weaker, detectors instead of modelling a few of them carefully. Concrete outputs will be a fully implemented audio-visual search engine, consisting of two main parts, viz. a learning system and a runtime system, where the former will feed its results into the latter after each round of training-and-thesaurus-update. The learning system will consist of software to be developed for overall video processing; visual analysis; audio analysis; integrated feature detector; and multimedia query and user interface.
The purpose of Cloud9 is the evolution of current Cloud technology in Internet scale, so as to support the creation of Hyper-Clouds either from existing Clouds spread all over the word or from physically remote computers through the Internet. The desired characteristics of the resulting Hyper-Clouds are constant availability, reliability, transparency and efficiency, while the offered services range from simple data storage and retrieval operations to data- and computationally- intensive tasks. Our goal is the development of a self-managing and elastic Hyper-Cloud, i.e., a platform that (i) can dynamically tune the resources that are allocated to the running applications depending on user needs and current conditions of the underlying execution environment and (ii) can provide mechanisms for the detection and healing of system failures and anomalies in real time.
Data Engineering Laboratory