重点实验室客座研究员、中国石油大学（北京）商烁教授的论文Discovery of Path Nearby Clusters in Spatial Networks被国际期刊（CCF A类）IEEE Transactions on Knowledge and Data Engineering  (TKDE) 接收，文继荣教授是论文合作者之一。该项研究首次提出了基于空间网络的路径近邻热点区域挖掘问题，使用基于位置的社会媒体信息来挖掘城市中的热点区域。作者在空间距离和社会媒体信息密度两个不同的维度上设计并实现了高性能搜索算法，并给出了具有理论依据的局部最优解。此项研究成果可以广泛应用在智能交通系统、城市计算、基于位置的社会媒体分析等领域。

论文摘要：

The discovery of regions of interest in large cities is an important challenge. We propose and investigate a novel query called the Path Nearby Cluster (PNC) query that finds regions of potential interest (e.g., sightseeing places and commercial districts) with respect to a user-specified travel route. Given a set of spatial objects O (e.g., POIs, geo-tagged photos, or geo-tagged tweets) and a query route q, if a cluster c has high spatial-object density and is spatially close to q, it is returned by the query (a cluster is a circular region defined by a center and a radius). This query aims to bring important benefits to users in popular applications such as trip planning and location recommendation. Efficient computation of the PNC query faces two challenges: how to prune the search space during query processing, and how to identify clusters with high density effectively. To address these challenges, a novel collective search algorithm is developed. Conceptually, the search process is conducted in the spatial and density domains concurrently. In the spatial domain, network expansion is adopted, and a set of vertices are selected from the query route as expansion centers. In the density domain, clusters are sorted according to their density distributions and they are scanned from the maximum to the minimum. A pair of upper and lower bounds are defined to prune the search space in the two domains globally. The performance of the PNC query is studied in extensive experiments based on real and synthetic spatial data.

论文链接：http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6990621