D6.2 – Summary, Analysis, Road-mapping and Production of Training materials:RAGE – WP6 – D6.2

Deliverable 6.2 describes and summarizes the final results of the population of the RAGE Ecosystem portal (EP) with information, knowledge and training material. The core version of the EP and its services are tested and validated. The next step will be to make the EP a long-lasting, self-sustaining Portal. For this purpose, the functionalities for selling and buying, the shop system, were set up and are ready to be launched as part of the foreseen commercial exploitation of the EP as core element of the RAGE Ecosystem. Library, Media Archive, Software Repository are prepared to be systematically expanded by incoming external resources, and the Social network interoperability support is in place. Tutorials explain how to use the portal and how to make components or how to create specific aspects of applied games. The content of this deliverable is part of the operational documentation prepared for the teams involved in the roll-out of the RAGE Ecosystem, thus representing its first priority audience

Integrating Scientific Publication into an Applied Gaming Ecosystem

The European (EU)-based industry for non-leisuregames (so called Applied Games, AGs) is an emerging business. Assuch it is still fragmented and needs to achieve critical mass tocompete globally. Nevertheless, its growth potential is widelyrecognized and even suggested to exceed the growth potential ofthe leisure games market. The European project Realizing anApplied Gaming Ecosystem (RAGE) is aiming at supporting thischallenge. RAGE will help to seize these opportunities by makingavailable an interoperable set of advanced Applied Game (AG)technology assets, as well as proven practices of using such AGassets in various real-world contexts. As described in [1], RAGEwill finally provide a centralized access to a wide range of appliedgaming software modules, relevant information, knowledge andcommunity services, and related scientific documents, taxonomies,media, and educational resources within an online communityportal called the RAGE Ecosystem. Besides this, an integrationbetween the RAGE Ecosystem and relevant social networkinteraction spaces that arranges and facilitates collaboration thatunderlie Research and Development (R&D), as well as marketorientedinnovation and exploitation will be created in order tosupport community building, as well as collaborative assetexploitation of User Generated Contents (UGCs) of the RAGEEcosystem. In this paper, we will describe the integration of theScientific Publication Platform (SPP) Mendeley [2] into the RAGEEcosystem. This will allow for automating repetitive tasks,reducing errors, and speeding up time consuming tasks. On theother hand it will support information, UGC, and knowledgesharing, as well as persistency of social interaction threads withinSocial Networking Sites (SNSs) and Groupware Systems (GWSs)that are connected to the RAGE Ecosystem. The paper reviewsrelevant use cases and scenarios, as well as related authentication,access, and information integration challenges. In this way, on theone hand a qualitative evaluation regarding an optimal technicalintegration is facilitated while on the other hand designapproaches for supporting features of resulting user interfaces areinitiated

Towards Support for Long-Term Digital Preservation in Product Life Cycle Management

Important legal and economic motivations exist for the design and engineering industry to address and integrate digital long-term preservation into product life cycle management (PLM). Investigations revealed that it is not sufficient to archive only the product design data which is created in early PLM phases, but preservation is needed for data that is produced during the entire product lifecycle including early and late phases. Data that is relevant for preservation consists of requirements analysis documents, design rationale, data that reflects experiences during product operation and also metadata like social collaboration context. In addition, also the engineering environment itself that contains specific versions of all tools and services is a candidate for preservation. This paper takes a closer look at engineering preservation use case scenarios as well as PLM characteristics and workflows that are relevant for long-term preservation. Resulting requirements for a long-term preservation system lead to an OAIS (Open Archival Information System) based system architecture and a proposed preservation service interface that respects the needs of the engineering industry

Distributed Leader Election in P2P Systems for Dynamic Sets

The collection of and search for location information is a core component in many pervasive and mobile computing applications. In distributed collaboration scenarios this location data is collected by different entities, e.g., users with GPS enabled mobile phones. Instead of using a centralized service for managing this distributed dynamic location data, we use a peer-to-peer data structure, the so-called distributed space partitioning tree (DSPT). A DSPT is a general use peer-to-peer data structure, similar to distributed hash tables (DHTs), that allows publishing, updating of, and searching for dynamic sets. In this paper we present an efficient distributed leader election algorithm that can be used in DSPTs to eliminate redundant network traffic. 1

Distributed Hybrid Genetic Programming for Learning Boolean Functions

When genetic programming (GP) is used to find programs with Boolean inputs and outputs, ordered binary decision diagrams (OBDDs) are often used successfully. In all known OBDD-based GP-systems the variable ordering, a crucial factor for the size of OBDDs, is preset to an optimal ordering of the known test function. Certainly this cannot be done in practical applications, where the function to learn and hence its optimal variable ordering are unknown. Here, the first GP-system is presented that evolves the variable ordering of the OBDDs and the OBDDs itself by using a distributed hybrid approach. For the experiments presented the unavoidable size increase compared to the optimal variable ordering is quite small. Hence, this approach is a big step towards learning well-generalizing Boolean functions