| Popis: |
Spatial distance join is one of the most common operations for spatial data analysis,which has various application scenarios.Existing distributed methods face the problems of too large space,high data skew,and slow self-join.To this end,this paper proposes a novel distributed distance join algorithm,i.e.,JUST-Join,for massive spatial data.First,JUST-Join regards only the necessary space as the global domain,which can filter invalid data out,reducing the overhead of unnecessary data transmission and computation.Second,we consider both the spatial distributions of the two datasets,which relieves the data skew issue.Third,for the spatial self-join,we adopt plane sweep method to further improve the efficiency.We implement JUST-Join algorithm based on Spark,and conduct extensive experiments using real datasets.The experimental results show that JUST-Join is superior to the state-of-the-art distributed spatial analysis systems in terms both of efficiency and scalability. |
