中文摘要：

如何在互联网上大量的带有地理位置标签和时间标签的信息中查找满足用户需求的信息十分重要．文中针对带有地理位置和时间标签的文本信息，提出多样性感知的时空文本信息的k近邻查询处理方法．首先，归一化处理数据对象的时空变量，并建立三维Rtree索引，有效融合数据对象的时间变量和空间变量．然后，提出多样性感知的近邻查询算法（DST—KNN）和改进的DST-KNN（IDST—KNN）．最后，通过基于大量数据集的实验验证文中查询处理方法的高效性和准确性．

英文摘要：

It is very important to find textual contents satisfying user＇s demand among a mount of textual contents with location and time tags generated on web. Firstly, location variables and time variables of data objects are normalized, and a three-dimensional Rtree index combining location variables and time variables is designed. Then, a DST-KNN query algorithm and an improved diversity-aware KNN query algorithm called IDST-KNN query algorithm are proposed. Finally, experiments on massive datasets illustrate that the query processing approaches are efficient and accurate.