2018/10/21 by Sanket Vaibhav Mehta, Shagun Sodhani, Mehta, Sanket Vaibhav +3
Computer Science · #Data Management and Algorithms #Data Mining Algorithms and Applications #Databases (cs.DB) #Distributed #FOS: Computer and information sciences #Parallel #Recommender Systems and Techniques #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.1810.09007
openalex publication_date 2018/10/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Spatial co-location pattern mining refers to the task of discovering the group of objects or events that co-occur at many places. Extracting these patterns from spatial data is very difficult due to the complexity of spatial data types, spatial relationships, and spatial auto-correlation. These patterns have applications in domains including public safety, geo-marketing, crime prediction and ecology. Prior work focused on using the spatial join. While these approaches provide state-of-the-art results, they are very expensive to compute due to the multiway spatial join and scaling them to real-world datasets is an open problem. We address these limitations by formulating the co-location pattern discovery as a clique enumeration problem over a neighborhood graph (which is materialized using a distributed graph database). We propose three new traversal based algorithms, namely CliqueEnumG, CliqueEnumK and CliqueExtend. We provide the empirical evidence for the effectiveness of our proposed algorithms by evaluating them for a large real-life dataset. The three algorithms allow for a trade-off between time and memory requirements and support interactive data analysis without having to recompute all the intermediate results. These attributes make our algorithms applicable to a wide range of use cases for different data sizes.