随着智能移动终端的广泛使用，基于位置的社交网络（LBSN）逐渐兴起，产生了爆炸式增长的海量规模社交空间数据，使得云环境下的社交空间数据管理成为研究热点。相比传统的空间数据，社交空间数据包含社交关系、空间位置信息、文本关键字三种特征属性，使得其数据管理具有新的特点和挑战，并衍生出了需要同时满足三种属性约束的新的复杂查询需求，即社交空间关键字查询。直接应用已有的分布式LBSN数据管理或空间数据管理技术无法高效地、高可扩展性地支持社交空间关键字查询。因此，本课题围绕LBSN中分布式社交空间关键字查询所面临的问题展开研究，设计云环境下的社交空间数据模型的存储机制，研究融合社交-空间-文本三种属性的分布式索引结构及其构建与维护技术，进而研究社交空间关键字标准查询、变体查询和交互式查询的分布式查询处理与优化技术。该研究成果将对云环境下社交空间关键字问题产生重要的理论意义和实际应用价值。

Due to the emerging of Location Based Social Network (LBSN), the traditional spatial keyword queries incorporate social relationships. The query results have to satisfy all the constraints of location, keywords and social networks. Therefore, new demands of queries are derived from socialized spatial keyword queries, which we name social spatial keyword queries. Based on spatial indices and text indices, we study mixed indices of social spatial data, along with its efficient query processing and optimization techniques. Besides, with the explosive scale growth of social spatial data, the efficiency of query processing algorithms in the centralized environment suffers due to the poor scalability. Distrbibuted data management techniques of LBSN and spatial data cannot be directly impelemented to process social spatial keyword query. In this project, we focus on the problem of social spatial keyword queries, analyze the motivation of the variants of social spatial keyword queries, and study the representation model of massive social spatial data, the distributed index with its construction and maintenance, the distributed query processing and optimization in the cloud environment. The research achievement has important theoretical and practical significance to the applications of social spatial keyword queries in the cloud.