Online-to-Offline (O2O)融合是推动电子商务发生深层次变革的新兴模式，对线上企业突破商业模式瓶颈及传统零售业转型发展具有重大意义。目前，O2O商务领域的研究仍然停留在模式和策略的探讨上，如何释放O2O融合所带来的智能性尚未得到充分的探索。大数据理念的渗透普及、及大数据计算技术的瞬息千里，为实现O2O智能商务决策提供了卓越支撑。然而，已有的面向智能商务的大数据挖掘研究存在建模方法不够完善、碎片化知识难以有效融合、存储和计算架构不够明晰等问题。为此，项目组联合来自多个交叉领域的学者，围绕“面向O2O智能商务的大数据融合与应用”展开全面研究。项目将构建O2O商务大数据融合框架，突破轨迹大数据挖掘方法及碎片化知识融合方法，并构建O2O商务大数据分析平台及示范应用。项目有望在O2O商务大数据挖掘方法上产生一批国际水平的研究成果，对大数据研究及应用提供重要的补充和推动作用。

Online-to-Offline (O2O) Fusion is a new-emerging paradigm that will promote the further transformation of e-business. It is also meaningful for both online enterprise and retail enterprise to break through the bottlenecks of traditional business models that they have suffered. Currently, O2O research mainly focuses on discussing paradigm or strategies itself. Nevertheless, how to release the intelligence of O2O fusion remains a pretty much open issue. Although the penetration of big data computing technologies can provide significant supports to O2O intelligent business decision, there are still a multitude of challenges that are worth exploring, including models are not perfect enough to support mining heterogeneous data, fragmented knowledge is difficult to be effectively fused, and the storage and computing infrastructure is not clear enough for business big data. In light of this, we organize a multidisciplinary team from the fields of data mining, database, behavior analysis, and business intelligence to explore several crucial issues within O2O big data fusion and its applications. In particular, we will first build a big data mining framework for O2O intelligence, meeting the challenges rising from data decentralization and heterogeneity. We then focus on designing novel mining and fusion methods for big trajectory data and fragmented knowledge. Last but not least, we will design and develop a real platform for O2O big data mining and construct several demonstration applications. We expect to publish a series of high-quality research papers during the project execution. As a result, this declared project would be expected to replenish and promote both theoretical research and practical applications of big data.