The goal of this research program is to develop a new paradigm for databases, called aspect-oriented data (AOD). AOD enables cross-cutting data concerns to be added to a database using aspect-oriented programming. A cross-cutting data concern is a data need that is universal (potentially applicable to the entire database) and widespread (can be used to enhance many, different databases). Data has a wide variety of cross-cutting data concerns, including provenance, time, lineage, and security. So this research has the potential to impact every database. The project achieves its goal by conducting the following tasks: 1) Develop a model for aspect-oriented data in which advice (metadata) can "tag" or annotate data, 2) Build a data aspect weaver to weave the advice into queries, constraints, and data modification, 3) Develop advice-specific modules for common cross-cutting concerns that plug into the data aspect weaver and add advice-specific behaviors, 4) Demonstrate that aspects themselves can be aspected to model meta-metadata, and 5) Develop test cases for AOD. The data aspect weaver is being developed for Pig Latin, which is a cloud-computing platform for data analysis. The research impacts database management systems, scientific databases, and digital government. The project supports a Ph.D. student to pursue research in databases. Publications, technical reports, and software from this research are disseminated on the project's web site (www.cs.usu.edu/~cdyreson/aspectOrientedData).
The goal of this research program was to help database developers add cross-cutting data concerns to their databases. A cross-cutting data concern is a data need that is universal (potentially applicable to the entire database) and widespread (can be used to enhance many, different databases). Data has a wide variety of cross-cutting concerns from provenance to security. Since every database needs some kind of cross-cutting data concern, this research could potentially benefit the many users of real-world databases. In our approach, a programmer creates creates aspect-oriented data by "tagging" data with metadata from a cross-cutting data concern. The metadata becomes active whenever the data is used. Aspect-oriented data can regulate, mutate, or construct new data. We re-engineered Pig Latin to support aspect-oriented data. Pig Latin is a cloud computing platform, used primarily for the analysis of massive data sets. We built a data aspect weaver for weaving metadata into Pig Latin programs. The data weaver is extensible; developers can "plug-in" the semantics of different cross-cutting data concerns by specifying only a few core functions. In this way, many different aspects are seamlessly supported. The project advanced scientific discovery and understanding by the active involvement and mentoring of several student researchers, the publication of research on aspect-oriented data, the release of software in the public domain, and the creation of teaching materials which were integrated into database courses at Utah State University.
