Efficient Storage of Big-Data for Real-Time GPS Applications

GPS applications need real-time responsiveness and are location-sensitive. GPS data is time-variant, dynamic and large. Current methods of centralized or distributed storage with static data impose constraints on addressing the real-time requirement of such applications. In this project we explore the need for real-timeliness of location based applications and evolve a methodology of storage mechanism for the GPS application's data. So far, the data is distributed based on zones and it also has limited redundancy leading to non-availability in case of failures. In our approach, data is partitioned into cells giving priority to Geo-spatial location. The geography of an area like a district, state, country or for that matter the whole world is divided into data cells. The size of the data cells is decided based on the previously observed location specific queries on the area. The cell size is so selected that a majority of the queries are addressed within the cell itself. This enables computation to happen closer to data location. As a result, data communication overheads are eliminated. We also build some data redundancy, which is used not only to enable failover mechanisms but also to target performance. This is done by nine-cell approach wherein each cell stores data of eight of its neighbours along with its own data. Cells that have an overload of queries, can easily pass-off some of their workload to their near neighbours and ensure timeliness in response. Further, effective load balancing of data ensures better utilization of resources. Experimental results show that our approach improves query response times, yields better throughput and reduces average query waiting time apart from enabling real-time updates on data.