• Stream Mill. The complete data stream management system must support relational streams, XML streams, and languages more powerful than SQL and XQuery--as required, e.g., for mining queries and queries for finding patterns in data streams.

• ArchIS. Poweful Archival Information Systems can be built by combining XML and relational DBs. XML supports a temporally-grouped view of the transaction-time history of the underlying DB, whereby powerful temporal queries are expressed in XQuery (with no extension required). Internally, RDBMSs support these temporal views and queries efficiently via SQL/XML.

• ATLaS. ATLaS allows users to define new aggregate and table functions in SQL itself. This provides a native extensibility mechanism for DBMS to overcome their current limitations with advanced applications. ATLaS is Turing-complete, and supports efficiently data mining and complex data-intensive applications.

• DEIS. Support for Design of Evolving Information Systems. An NSF-IIS SGER, Science of Design, Collaborative Research project. The objective is to develop the enabling DB technology for information systems to gracefully adapt to changes.

• ICAP. Incorporating Change Management into Archival Processes.We propose a uniform approach to archive and retrieve the history of web documents using XML. The evolution history and past versions of these documents can be queried using XML query languages
  

• TBALL. Technologically Based Assessment of Language and Literacy: an interdisciplinary project involving EE, CS, Linguistics, Education, and Neuroscience departments from UCLA, UCB, USC and partnerships with local elementary schools.

Previous Projects

• TENORS. SQL^ST uses a point-based temporal model to minimize the new constructs needed in SQL and is implemented in the TENORS prototype.

• SQL-TS. This simple SQL extension searches for pattern in sequences and time series. SQL-TS is supported by powerful query optimization techniques based on a generalization of the Knuth, Morris and Pratt text search algorithm.

• TREPL. EPL and TREPL extend active databases with ability of triggering on complex event patterns.

• LDL⁺⁺. This second-generation deductive database system overcomes the semantic and computational problems of nonderministic and nonmonotonic reasoning to achieve new levels of of expressive power for declarative database languages. Active rules, planning problems, monotonic aggregates, and optimal graph search algorithms can then be expressed via efficiently computable logic programs.