Living Innovation Laboratory Model Design and Implementation

Living Innovation Laboratory (LIL) is an open and recyclable way for multidisciplinary researchers to remote control resources and co-develop user centered projects. In the past few years, there were several papers about LIL published and trying to discuss and define the model and architecture of LIL. People all acknowledge about the three characteristics of LIL: user centered, co-creation, and context aware, which make it distinguished from test platform and other innovation approaches. Its existing model consists of five phases: initialization, preparation, formation, development, and evaluation. Goal Net is a goal-oriented methodology to formularize a progress. In this thesis, Goal Net is adopted to subtract a detailed and systemic methodology for LIL. LIL Goal Net Model breaks the five phases of LIL into more detailed steps. Big data, crowd sourcing, crowd funding and crowd testing take place in suitable steps to realize UUI, MCC and PCA throughout the innovation process in LIL 2.0. It would become a guideline for any company or organization to develop a project in the form of an LIL 2.0 project. To prove the feasibility of LIL Goal Net Model, it was applied to two real cases. One project is a Kinect game and the other one is an Internet product. They were both transformed to LIL 2.0 successfully, based on LIL goal net based methodology. The two projects were evaluated by phenomenography, which was a qualitative research method to study human experiences and their relations in hope of finding the better way to improve human experiences. Through phenomenographic study, the positive evaluation results showed that the new generation of LIL had more advantages in terms of effectiveness and efficiency.Comment: This is a book draf

Living Knowledge

Diversity, especially manifested in language and knowledge, is a function of local goals, needs, competences, beliefs, culture, opinions and personal experience. The Living Knowledge project considers diversity as an asset rather than a problem. With the project, foundational ideas emerged from the synergic contribution of different disciplines, methodologies (with which many partners were previously unfamiliar) and technologies flowed in concrete diversity-aware applications such as the Future Predictor and the Media Content Analyser providing users with better structured information while coping with Web scale complexities. The key notions of diversity, fact, opinion and bias have been defined in relation to three methodologies: Media Content Analysis (MCA) which operates from a social sciences perspective; Multimodal Genre Analysis (MGA) which operates from a semiotic perspective and Facet Analysis (FA) which operates from a knowledge representation and organization perspective. A conceptual architecture that pulls all of them together has become the core of the tools for automatic extraction and the way they interact. In particular, the conceptual architecture has been implemented with the Media Content Analyser application. The scientific and technological results obtained are described in the following

Decentralized Task-aware Scheduling for Data Center Networks.

ABSTRACT Many data center applications perform rich and complex tasks (e.g., executing a search query or generating a user's news-feed). From a network perspective, these tasks typically comprise multiple flows, which traverse different parts of the network at potentially different times. Most network resource allocation schemes, however, treat all these flows in isolation -rather than as part of a task -and therefore only optimize flow-level metrics. In this paper, we show that task-aware network scheduling, which groups flows of a task and schedules them together, can reduce both the average as well as tail completion time for typical data center applications. To achieve these benefits in practice, we design and implement Baraat, a decentralized task-aware scheduling system. Baraat schedules tasks in a FIFO order but avoids head-of-line blocking by dynamically changing the level of multiplexing in the network. Through experiments with Memcached on a small testbed and large-scale simulations, we show that Baraat outperforms state-of-the-art decentralized schemes (e.g., pFabric) as well as centralized schedulers (e.g., Orchestra) for a wide range of workloads (e.g., search, analytics, etc)

CHORUS Deliverable 3.4: Vision Document

The goal of the CHORUS Vision Document is to create a high level vision on audio-visual search engines in order to give guidance to the future R&D work in this area and to highlight trends and challenges in this domain. The vision of CHORUS is strongly connected to the CHORUS Roadmap Document (D2.3). A concise document integrating the outcomes of the two deliverables will be prepared for the end of the project (NEM Summit)

Service-oriented coordination platform for technology-enhanced learning

It is currently difficult to coordinate learning processes, not only because multiple stakeholders are involved (such as students, teachers, administrative staff, technical staff), but also because these processes are driven by sophisticated rules (such as rules on how to provide learning material, rules on how to assess students’ progress, rules on how to share educational responsibilities). This is one of the reasons for the slow progress in technology-enhanced learning. Consequently, there is a clear demand for technological facilitation of the coordination of learning processes. In this work, we suggest some solution directions that are based on SOA (Service-Oriented Architecture). In particular, we propose a coordination service pattern consistent with SOA and based on requirements that follow from an analysis of both learning processes and potentially useful support technologies. We present the service pattern considering both functional and non-functional issues, and we address policy enforcement as well. Finally, we complement our proposed architecture-level solution directions with an example. The example illustrates our ideas and is also used to identify: (i) a short list of educational IT services; (ii) related non-functional concerns; they will be considered in future work