The collection of multiple genome-scale datasets is now routine, and the frontier of research in systems biology has shifted accordingly. Rather than clustering a single dataset to produce a static map of functional modules, the focus today is on data integration, network alignment, interactive visualization and ontological markup. Because of the intrinsic noisiness of high-throughput measurements, statistical methods have been central to this effort. In this review, we briefly survey available datasets in functional genomics, review methods for data integration and network alignment, and describe recent work on using network models to guide experimental validation. We explain how the integration and validation steps spring from a Bayesian description of network uncertainty, and conclude by describing an important near-term milestone for systems biology: the construction of a set of rich reference networks for key model organisms.

Current systems that publish relational data as nested (XML) views are passive in the sense that they can only respond to user-initiated queries over the nested views. In this article, we propose an active system whereby users can place triggers on (unmaterialized) nested views of relational data. In this architecture, we present scalable and efficient techniques for processing triggers over nested views by leveraging existing support for SQL triggers over flat relations in commercial relational databases. We have implemented our proposed techniques in the context of the Quark XML middleware system. Our performance results indicate that our proposed techniques are a feasible approach to supporting triggers over nested views of relational data.

XML has emerged as a dominant standard for information exchange on the Internet. However, a large fraction of data continues to be stored in relational databases. At a high level, there are two approaches to supporting triggers over XML views. The first is to materialize the entire view and store it in an XML database with support for XML triggers. However, this approach suffers from the overhead of replicating and incrementally maintaining the materialized XML on every relational update affecting the view, even though users may only be interested in relatively rare events. In this paper, we propose the alternative approach of translating XML triggers into SQL triggers. There are some challenges involved in this approach, however, because triggers can be specified over complex XML views with nested predicates, while SQL triggers can only be specified over flat tables. Consequently, even identifying the parts of an XML view that could have changed due to a (possibly deeply nested) SQL update is a non-trivial task, as is the problem of computing the old and new values of an updated fragment of the view. We address the above challenges and propose a system architecture and an algorithm for supporting triggers over XML views of relational data. We implement and evaluate our system; the performance results indicate our techniques are a feasible approach to supporting triggers over XML views of relational data.