DR@Y

The goal of the Semantic Web vision is to free Web data from the applications that control them, so that data can be easily described and exchanged. This is accomplished by supplementing natural language and other data found on the Web with machine readable metadata in statement form (e.g., X is-a person, X has-name ``Joe'', X has-age ``35'') and enabling descriptions of data ontologies so that data from different applications can be integrated through ontology mapping. One ultimate goal is to turn the Web into a giant database, against which one could issue structured queries and receive structured answers in response.

SW-Store is a recently launched project whose goal is to manage and query Semantic Web data. We are starting from a clean-slate and designing a DBMS specifically for this type of data and the prevalent Semantic Web data model, the Resource Description Framework, or RDF. We explore how common SW queries and applications such as reasoning and biological data integration can be built into the database. This work builds on a recent publication that won "Best Paper" at VLDB in September.

Current OLTP database designs, which date largely from the 1970s, are based on several assumptions about the architecture of database applications and hardware that are less true today than they were 30 years ago. For example, all but the very largest OLTP applications can fit in main memory of a modern shared-nothing cluster of server machines. On a single node with a memory resident database, OLTP transactions take only a few microseconds to execute. Additionally, many applications carefully construct database transactions so they have no user stalls. Taken together, both of these points mean there is a large class of OLTP applications for which a single-threaded execution engine with no concurrency control performs very well, avoiding the need for high overhead, locking-based pessimistic concurrency control protocols designed to keep CPUs busy during disk and user stalls. Further, the cost of computers has dropped so dramatically in the past thirty years that paying for a dedicated database administrator has become one of the dominant costs in running a database system, such that tools that automate design and tuning have great value. Finally, the architecture of a server node has also shifted -- the number of cores available to process data is proliferating. The goal of the H-Store project is to investigate how these architectural and application shifts affect the performance of OLTP databases, and to study what performance benefits would be possible with a complete redesign of OLTP systems in light of these trends. Our early results show that a simple prototype built from scratch using modern assumptions can outperform current commercial DBMS offerings by around a factor of 80 on OLTP workloads. We are currently working to build a full-featured system that demonstrates these performance wins in a more robust prototype.

As shown in the C-Store project, storing data in columns seems to be the best performing solution at the storage layer level in analytical applications. Meanwhile, storing data in rows performs better for transactional applications. Further, scientific database applications prefer to store data in multidimensional arrays. Chunky-Store looks at how multiple different storage layer options can be integrated into a single database system so that data can be stored in a way that is best-suited for the way it is expected to be used. Various hybrid storage layer designs are also being explored. This work is collaboration between MIT and Yale.