In this paper, we describe a scheme for tolerating and recovering from mid-query faults in a distributed shared nothing database. Rather than aborting and restarting queries, our system, Osprey, divides running queries into subqueries, and replicates data such that each subquery can be rerun on a different node if the node initially responsible fails or returns too slowly. Our approach is inspired by the fault tolerance properties of Map Reduce, in which map or reduce jobs are greedily assigned to workers, and failed jobs are rerun on other workers. Osprey is implemented using a middleware approach, with only a small amount of custom code to handle cluster coordination. Each node in the system is a discrete database system running on a separate machine. Data, in the form of tables, is partitioned amongst database nodes and each partition is replicated on several nodes, using a technique called chained declustering [1]. A coordinator machine acts as a standard SQL interface to users; it transforms an input SQL query into a set of subqueries that are then executed on the nodes. Each subquery represents only a small fraction of the total execution of the query; worker nodes are assigned a new subquery as they finish their current one. In this greedy-approach, the amount of work lost due to node failure is small (at most one subquery's work), and the system is automatically load balanced, because slow nodes will be assigned fewer subqueries. We demonstrate Osprey's viability as a distributed system for a small data warehouse data set and workload. Our experiments show that the overhead introduced by the middleware is small compared to the workload, and that the system shows promising load balancing and fault tolerance properties.

We present a new approach to deformation invariant image matching. Our matcher (a) aligns templates to targets over a broad range of nonlinear deformations, (b) factors the total deformation into spectral categories, where low wavenumber deformations are smooth and global and high wavenumbers are turbulent and local, and (c) weighs the reduction in template-target misfit within each category to differentiate between relevant and irrelevant deformations. It accomplishes this by aligning images in a scale-cascaded fashion, with more complex, local deformations following simpler, more global ones. Each step of the cascade involves finding an iterative solution to a nonlinear optimization problem using a Gabor deformation basis. Cascaded alignment makes deformation invariant matching feasible and efficient. Our approach is applied to recognize the flexible bodies of salamanders from a large database; results indicate that the method is very promising.