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The goal of the Dicode project was to facilitate and augment collaboration and decision making in data-intensive and cognitively-complex settings. To do so, whenever appropriate, the project exploited and built on prominent high-performance computing paradigms and large data processing technologies to meaningfully search, analyze and aggregate data existing in diverse, extremely large, and rapidly evolving sources. Moreover, particular emphasis was given to the deepening of our insights about the proper exploitation of big data, as well as to collaboration and sense making support issues. Building on current advancements, the solution proposed by the Dicode project brings together the reasoning capabilities of both the machine and the humans. This innovative solution incorporates and orchestrates 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 effective in their work practices. The achievement of the Dicode project’s goal was validated through three use cases addressing clearly established problems. These cases concern: (i) scientific collaboration supported by integrated large-scale knowledge discovery in clinico-genomic research, (ii) management of pertinent information from heterogeneous data to facilitate the process of making clinical decisions in drug trials, and (iii) capturing tractable, commercially valuable high-level information from unstructured Web 2.0 data for opinion mining. The project’s results are presented in detail in a recently published Springer book (http://link.springer.com/book/10.1007/978-3-319-02612-1).
http://dicode-project.eu/
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