The Dicode project -- Data intensiveness and cognitive complexity in contemporary collaboration and decision making settings -- Requirements for Big Data Analytics Supporting Decision Making: A Sensemaking Perspective -- Making Sense of Linked Data: A Semantic Exploration Approach -- The Dicode Data Mining Services -- The Dicode Collaboration and Decision Making Support Services -- Integrating Dicode Services: The Dicode Workbench -- Clinico-Genomic Research Assimilator: A Dicode Use Case -- Opinion Mining from Unstructured Web 2.0 Data: A Dicode Use Case -- Data Mining in Data-Intensive and Cognitively-Complex Settings: Lessons Learned from the Dicode Project -- Collaboration and Decision Making in Data-Intensive and Cognitively-Complex Settings: Lessons Learned from the Dicode project.

This book reports on cutting-edge research carried out within the context of the EU-funded Dicode project, which aims at facilitating and augmenting collaboration and decision making in data-intensive and cognitively complex settings. Whenever appropriate, Dicode builds on prominent high-performance computing paradigms and large data processing technologies to meaningfully search, analyze, and aggregate data from diverse, extremely large, and rapidly evolving sources. The Dicode approach and services are fully explained, and particular emphasis is placed on deepening insights regarding the exploitation of big data, as well as on collaboration and issues relating to sense-making support. Building on current advances, the solution developed in the Dicode project brings together the reasoning capabilities of both the machine and 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 effective in their work practices.