Distributed Spatial k-dominant Skyline Maintenance Using Computational Object Preservation

Abstract

The existing k-dominant skyline solutions are restricted to centralized query processors, limiting scalability, and imposing a single point of failure. To overcome those problems in this paper, we propose the computation and maintenance algorithms for spatial k-dominant skyline query processing in large-scale distributed environment. Where the underlying dataset is partitioned into geographically distant computing core (personal computer) that are connected to the coordinator (server). Our proposed techniques preserve the spatial k-dominant computation object itself into a serialized form. This preservation is done in client’s core after completing a computational job successfully. When the issue of maintenance comes in action, preserve data object retrieves and use for computation. This procedure eliminates the necessity of intermediate re-send and re-computation of k-dominant skyline for the maintenance issue. Thus, we quantify the gain of data transferring consecutively into different cores to maximize the overall gain as well as the query or balancing the load on different cores fairly. Extensive performance study shows that proposed algorithms are efficient and robust to different data distributions.