Deductive Optimization of Relational Data Storage

Optimizing the physical data storage and retrieval of data are two key database management problems. In this paper, we propose a language that can express a wide range of physical database layouts, going well beyond the row- and column-based methods that are widely used in database management systems. We use deductive synthesis to turn a high-level relational representation of a database query into a highly optimized low-level implementation which operates on a specialized layout of the dataset. We build a compiler for this language and conduct experiments using a popular database benchmark, which shows that the performance of these specialized queries is competitive with a state-of-the-art in memory compiled database system

Scoop: An Adaptive Indexing Scheme for Stored Data in Sensor Networks

In this paper, we present the design of Scoop, a system for indexing and querying stored data in sensor networks. Scoop works by collecting statistics about the rate of queries and distribution of sensor readings over a sensor network, and uses those statistics to build an index that tells nodes where in the network to store their readings. Using this index, a users queries over that stored data can be answered efficiently, without flooding those queries throughout the network. This approach offers a substantial advantage over other solutions that either store all data externally on a basestation (requiring every reading to be collected from all nodes), or that store all data locally on the node that produced it (requiring queries to be flooded throughout the network). Our results, in fact, show that Scoop offers a factor of four improvement over existing techniques in a real implementation on a 64-node mote-based sensor network. These results also show that Scoop is able to efficciently adapt to changes in the distribution and rates of data and queries