Many analytics tasks are based on information that is initially incomplete, inconsistent, or simply used incorrectly. Existing strategies to help people cope with these sources of uncertainty often require heavyweight upfront organizational tasks (i.e., tagging, data-cleaning, or modeling). The Mimir project aims to streamline this process, making it more on-demand and intuitive.
One of the greatest threats to a the security of a database system comes from within: Users who have been granted access to data using it in a malicious or illegitimate way. Our goal is to develop new types of statistical signatures for a user or role's behavior as they access a database. Using these signatures, we can identify non-standard behvaior that could be evidence of malicious activity.
ASTral is a database that uses a combination of programming language, program optimization, and data structure techniques to create and maintain self-adapting physical layouts that rapidly react to changing workloads.
The PocketData project explores how smartphones make use of embedded databases in the interest of designing new energy-efficient, low-latency, developer-friendly data management tools for pocket-scale data.
DBToaster is an SQL-to-native-code compiler. It generates lightweight, specialized, embeddable query engines for applications that require real-time, low-latency data processing and monitoring capabilities. The DBToaster compiler generates code that can be easily incorporated into any C++ or JVM-based (Java, Scala, ...) project.
Since 2009, DBToaster has spearheaded the currently ongoing database compilers revolution. If you are looking for the fastest possible execution of continuous analytical queries, DBToaster is the answer. DBToaster code is 3-6 orders of magnitude faster than all other systems known to us.
DBToaster was started at Cornell by the research group of Christoph Koch (now at EPFL). Development on DBToaster continues at the DATA lab at EPFL.
The MayBMS system (note: MayBMS is read as “maybe-MS”, like DBMS) is a complete probabilistic database management system that leverages robust relational database technology: MayBMS is an extension of the Postgres server backend. MayBMS is open source and the source code is available under the BSD license.
MayBMS stands alone as a complete probabilistic database management system that supports a powerful, compositional query language for which nevertheless worst-case efficiency and result quality guarantees can be made. The MayBMS backend is accessible through several APIs, with efficient internal operators for computing and managing probabilistic data.
MayBMS was started at Saarland University by the research group of Christoph Koch (now at EPFL). MayBMS has turned into Sprout, and is being developed at Oxford.