Shoring up persistent applications
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Towards an effective calculus for object query languages
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
The object database standard: ODMG 2.0
The object database standard: ODMG 2.0
Query unnesting in object-oriented databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Incremental maintenance of materialized OQL views
Proceedings of the 3rd ACM international workshop on Data warehousing and OLAP
Optimizing object queries using an effective calculus
ACM Transactions on Database Systems (TODS)
Database research at UT Arlington
ACM SIGMOD Record
Query Engines for Web-Accessible XML Data
Proceedings of the 27th International Conference on Very Large Data Bases
Using a Metadata Software Layer in Information Systems Integration
CAiSE '01 Proceedings of the 13th International Conference on Advanced Information Systems Engineering
MOVIE: an incremental maintenance system for materialized object views
Data & Knowledge Engineering
Rapidly implementing languages to compile as C++ without crafting a compiler
Software—Practice & Experience
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The &lgr;-DB project at the University of Texas at Arlington aims at developing frameworks and prototype systems that address the new query optimization challenges for object-oriented and object-relational databases, such as query nesting, multiple collection types, methods, and arbitrary nesting of collections. We have already developed a theoretical framework for query optimization based on an effective calculus, called the monoid comprehension calculus [4]. The system reported here is a fully operational ODMG 2.0 [2] OODB management system, based on this framework. Our system can handle most ODL declarations and can process most OQL query forms. &lgr;-DB is not ODMG compliant. Instead it supports its own C++ binding that provides a seamless integration between OQL and C++ with low impedance mismatch. It allows C++ variables to be used in queries and results of queries to be passed back to C++ programs. Programs expressed in our C++ binding are compiled by a preprocessor that performs query optimization at compile time, rather than run-time, as it is proposed by ODMG. In addition to compiled queries, &lgr;-DB provides an interpreter that evaluates ad-hoc OQL queries at run-time. The &lgr;-DB system architecture is shown in Figure 1. The &lgr;-DB evaluation engine is written in SDL (the SHORE Data Language) of the SHORE object management system [1], developed at the University of Wisconsin. ODL schemas are translated into SDL schemas in a straightforward way and are stored in the system catalog. The &lgr;-DB OQL compiler is a C++ preprocessor that accepts a language called &lgr;-OQL, which is C++ code with embedded DML commands to perform transactions, queries, updates, etc. The preprocessor translates &lgr;-OQL programs into C++ code that contains calls to the &lgr;-DB evaluation engine. We also provide a visual query formulation interface, called VOODOO, and a translator from visual queries to OQL text, which can be sent to the &lgr;-DB OQL interpreter for evaluation.Even though a lot of effort has been made to make the implementation of our system simple enough for other database researchers to use and extend, our system is quite sophisticated since it employs current state-of-the-art query optimization technologies as well as new advanced experimental optimization techniques which we have developed through the years, such as query unnesting [3]. The &lgr;-DB OODBMS is available as an open source software through the web at http://lambda.uta.edu/lambda-DB.html