Storm System Database: A Big Data Approach to Moving Object Databases

Rainfall data is often collected by measuring the amount of precipitation collected in a physical container at a site. Such methods provide precise data for those sites, but are limited in granularity to the number and placement of collection devices. We use radar images of storm systems that are publicly available and provide rainfall estimates for large regions of the globe, but at the cost of loss of precision. We present a moving object database called Storm DB that stores decibel measurements of rain clouds as moving regions, i.e., we store a single rain cloud as a region that changes shape and position over time. Storm DB is a prototype system that answers rain amount queries over a user defined time duration for any point in the continental United States. In other words, a user can ask the database for the amount of rainfall that fell at any point in the US over a specified time window. Although this single query seems straightforward, it is complicated due to the expected size of the dataset: storm clouds are numerous, radar images are available in high resolution, and our system will collect data over a large timeframe, thus, we expect the number and size of moving regions representing storm clouds to be large. To implement our proposed query, we bring together the following concepts: (i) image processing to retrieve storm clouds from radar images, (ii) interpolation mechanisms to construct moving regions with infinite temporal resolution from region snapshots, (iii) transformations to compute exact point in moving polygon queries using 2-dimensional rather than 3-dimensional algorithms, (iv) GPU algorithms for massively parallel computation of the duration that a point lies inside a moving polygon, and (v) map/reduce algorithms to provide scalability. The resulting prototype lays the groundwork for building big data solutions for moving object databases.