大数据驱动的复杂系统协同创新，是数据科学在人类解决重大科学与工程问题中发挥核心驱动力的重要体现。随着大数据资源在复杂系统协同创新过程中发挥越来越重要的应用价值，一些新的应用与技术问题也越来越引起关注。首先是大数据的价值稀疏性特征，导致群智协同过程所需决策数据的获取，在时效性与一致性方面的问题越来越突出。而协同创新的动态演化，使得传统的数据挖掘算法，经常面临参数、条件和目标的变化。完全随机的一个意外情况，都有可能导致系统全局规则的巨大调整。针对上述应用挑战，本课题着眼于复杂系统在大数据环境下协同运行所面临的新的“生态环境”，按照问题驱动的研究思路，重点围绕决策依据挖掘、群智协同推进、创新可信保障等应用环节，有针对性地开展研究工作，并提出相应的理论与技术解决方案。在此基础上，结合典型的复杂系统协同应用，对课题的研究成果进行理论与方法验证。

Collaborative innovation in big data driven complex systems significantly reflects the fact that data science is the core driving force when addressing major scientific and engineering problems. With big data resource playing increasing important role in collaborative innovation of complex systems, some emergent application and technology problems have attracted extensive attention. First of all, the value sparsity of big data creates serious timeliness and consistency problems when obtaining collaborative data, which is helpful to domain knowledge integration and collaborative assistant decision-making. Furthermore, the dynamic evolution of collaborative innovation makes parameters, conditions and goals of traditional data mining algorithms always change. Any unexpected accidents possibly lead to huge adjustment to the global discipline of the system. In view of these challenges, the proposal focuses on the new ecosystem faced by the collaborative running of complex systems in the big data environment. According to the problem driven research methodology, we mainly investigate the topic of decision-making basis mining, swarm intelligence collaborative advancing and dependable guarantee of innovation. The corresponding theory and application solution are then proposed based on the research motivation. We also theoretically and technically validate our scientific research achievements through a typical collaborative application of complex systems.