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 to meaningfully search, analyze and aggregate existing data in diverse, extremely large, and rapidly evolving sources.
More specifically, the project’s S&T objectives are to:
- fully understand the practices and needs of diverse communities and organizations as far as data-intensive and cognitively-complex collaboration and decision making is concerned
- provide a suite of innovative, adaptive and interoperable services that satisfies the full range of the associated requirements
- provide innovative work methodologies that exploit the abovementioned suite of services and advance the current practices in terms of efficicy, creativity, as well as time and cost effectiveness
- ensure usability and acceptability of the above services and work methodologies through their validation in real use cases
Clinico-Genomic Research Assimilator
This use case focuses on scientific collaboration in the clinico-genomic research community managing translational research processes so that relevant findings and results are timely delivered It helps to explore, evaluate, disseminate and diffuse relative scientific results. The Clinico-Genomic Research Assimilator links and mines disparate clinical and post-genomic data sources.
Opinion Mining from Unstructured Web 2.0 Data
This use case concerns capturing tractable, commercially valuable information to support marketing decisions and strategies. Through this use case, we aim to validate the Dicode suite of services for the automatic analyses of voluminous amounts of unstructured information from Web 2.0 platforms, blogging platforms (Twitter), and social network.
Trial of Rheumatoid Arthritis Treatment
Focusing on medical decision making, this use case helps doctors and patients in the domain of Rheumatoid Arthritis treatment Dicode services will enable more effective collaborative decision making to speed up the introduction of life saving treatments.
Prof. Nikos Karacapilidis (project coordinator)
Prof. Víctor Maojo (UPM contact)
Biomedical Informatics Group
Departamento de Inteligencia Artificial
Facultad de Informática – UPM
Campus de Montegancedo s/n
28660 Boadilla del Monte, Madrid, SPAIN
Tel: +34 91 336 68 97