DICODE: Mastering Data-Intensive Collaboration and Decision Making
CP, FP7-ICT, SO 4.3: Intelligent Information Management

The goal of the Dicode project is to facilitate and augment collaboration and decision making in data-intensive and cognitively-complex settings. To do so, it will exploit and build on the most prominent high-performance computing paradigms and large data processing technologies – such as cloud computing, MapReduce, Hadoop, Mahout, and column databases – to meaningfully search, analyze and aggregate data existing in diverse, extremely large, and rapidly evolving sources. Building on current advancements, the solution foreseen in the Dicode project will bring together the reasoning capabilities of both the machine and the humans. It can be viewed as an innovative workbench incorporating and orchestrating a set of interoperable services that reduce the data-intensiveness and complexity overload at critical decision points to a manageable level, thus permitting stakeholders to be more productive and concentrate on creative activities. Services to be developed are: (i) scalable data mining services (including services for text mining and opinion mining), (ii) collaboration support services, and (iii) decision making support services. The achievement of the Dicode project’s goal will be validated through three use cases addressing clearly established problems. These cases were chosen to test the transferability of Dicode solution in different collaboration and decision making settings, associated with diverse types of data and data sources, thus covering the full range of the foreseen solution’s features and functionalities. They concern: (i) scientific collaboration supported by integrated large-scale knowledge discovery in clinico-genomic research, (ii) delivering pertinent information from heterogeneous data to communities of doctors and patients in medical treatment decision making, and (iii) capturing tractable, commercially valuable high-level information from unstructured Web 2.0 data for opinion mining.

DIVEST: Dismantling of Vessels with Enhanced Safety and Technology
 CP, FP7-SST-2007-RTD-1, Theme 7: Transport

This research project aims to provide a hlistic understanding of ship dismantling through the combination of requirements and impacts (with associated procedures and processes) from pertinent social, technical, economic and environmental drivers into a single, integrated and validated decision support tool (database). To achieve the above, the consortium brings together experts from different disciplines (law, environment, engineering and information and communication technology) in an attempt to analyze the challenges of ship dismantling with respect to factors related to their interests and background. The challenge is to assimilate and process information and the ultimate goal is the provision of a body of knowledge that can assist decision makers from industries, institutions and governments during their everyday activities.

​PALETTE: Pedagogically Sustained Adaptive Learning through the Exploitation of Tacit and Explicit Knowledge
IP, IST FP6-2004, Priority 2.4.10: Technology-Enhanced Learning

The project aims at facilitating and augmenting individual and organisational learning in Communities of Practice (CoPs). Towards this aim, an interoperable and extensible set of innovative services as well as a set of specific scenarios of use will be designed, implemented and thoroughly validated in CoPs of diverse contexts. The above services and scenarios will support: (i) incremental convergence towards a comprehensive representation of practices; (ii) argumentative debates about practices; (iii) enhancement of practices through knowledge exploration, inside and outside of the CoPs; (iv) provision of procedures for the reification and creation of new practices. To realise the above goals, PALETTE’s R&D process adopts a participative design approach, establishing a good balance between technological and pedagogical experts. Evaluation is integrated in the same process, in order to provide direct, frequent and detailed feedback. It is expected that the adoption of the developed services and scenarios will result to the: facilitation of tasks performed by learning CoPs by removing barriers imposed by current approaches; exploitation of diverse mental models, knowledge resources and competences of each CoP’s member through the social interaction of codified and tacit specialist knowledge; uncomplicated creation of new knowledge, which can lead to the evolution of the associated learning resources; easy access and reuse of knowledge built by CoPs; increase of active participation of individuals in CoPs; emergence of new CoPs, inside and outside organisations; increase of the overall quality of learning in CoPs.

ShipDismantl: Cost Effective And Environmentally Sound Dismantling Of Obsolete Vessels
STREP, IST FP6-2003, Transport-3 Call, TST4-CT-2005​

​​This research project aims to: (i) Develop innovative dismantling and recycling procedures consisting of optimal design of a prototype ship dismantling site, and optimization of ship breaking facilities and dismantling processes with respect to environmental and energy issues, cost, as well as occupationally hazards concerns. The above will also be applied to cases concerning basic improvement of already operational ship breaking yards, which may be active in full or partial dismantling. (ii) Develop a Decision Support System (DSS) for the ship breaking industry, which will be released free of charge to all stakeholders worldwide. The DSS will take into consideration the existing capacity and dismantling methodology of a given ship breaker, the type and the particular characteristics of the dismantling ship, and diverse third parties reports (including the inventory report of hazardous material on board), and it will outline the dismantling process in an environmentally sound, cost and energy effective way, taking into consideration the health and safety of the workers. (iii) Support the decision of acceptance of a given obsolete vessel for dismantling at a given site based on the comparison of the available against the required infrastructure. (iv) Validate the proposed tools and methodologies through a real case study.

INNO-MED: Development of an Innovative Evidence-Based Medical Information System for the Improvement of Effectiveness and Quality of Medical Care
Interreg IIIC – East Zone – RFO INNOREF – INSP09

​This project concerns the development of an innovative Evidence–Based Medical Information System, which will support a series of integrated services related to the effective management and exploitation of Health Information, Clinical Knowledge and Practice. The need for this project is further validated from the assumption that health care is largely information and communication dependent, while ICT solutions can provide easily accessible and cost effective ways of meeting this increasing dependency. The proposed system will provide a wide range of users (medical doctors, health care professionals, health policy makers, patients and citizens) with the opportunity to access, process and produce evidence-based medical information. More specifically, it will provide: (i) Flexible mechanisms for capturing, retrieving and analyzing clinical practice and information, which may be stored either in the proposed system’s Knowledge Base or in remote (national or international) medical databases (these mechanisms concern all types of users); (ii) A user-friendly framework for e-collaboration and evidence-based medical decision making, through which medical doctors will be able to exploit already registered knowledge and conduct argumentative discourses in an effort to determine diagnosis and optimal treatment under a specific set of circumstances; (iii) Mechanisms for the exploitation of registered clinical knowledge towards the development of clinical guidelines for medical doctors and their associated staff; (iv) Mechanisms for the exploitation of registered clinical knowledge towards the identification of critical Preventive Medicine and Health Promotion issues.