A simulation study comparing supertree and combined analysis methods using SMIDGen

<p>Abstract</p> <p>Background</p> <p>Supertree methods comprise one approach to reconstructing large molecular phylogenies given multi-marker datasets: trees are estimated on each marker and then combined into a tree (the "supertree") on the entire set of taxa. Supertrees can be constructed using various algorithmic techniques, with the most common being matrix representation with parsimony (MRP). When the data allow, the competing approach is a combined analysis (also known as a "supermatrix" or "total evidence" approach) whereby the different sequence data matrices for each of the different subsets of taxa are concatenated into a single supermatrix, and a tree is estimated on that supermatrix.</p> <p>Results</p> <p>In this paper, we describe an extensive simulation study we performed comparing two supertree methods, MRP and weighted MRP, to combined analysis methods on large model trees. A key contribution of this study is our novel simulation methodology (Super-Method Input Data Generator, or <it>SMIDGen</it>) that better reflects biological processes and the practices of systematists than earlier simulations. We show that combined analysis based upon maximum likelihood outperforms MRP and weighted MRP, giving especially big improvements when the largest subtree does not contain most of the taxa.</p> <p>Conclusions</p> <p>This study demonstrates that MRP and weighted MRP produce distinctly less accurate trees than combined analyses for a given base method (maximum parsimony or maximum likelihood). Since there are situations in which combined analyses are not feasible, there is a clear need for better supertree methods. The source tree and combined datasets used in this study can be used to test other supertree and combined analysis methods.</p

SPHINX: Schema Integration by Example

We focus on the problem of semi-automated query discovery for XML views without requiring the intervention of an expert to guarantee a correct final result. Given multiple independent sources of heterogeneous XML data structures, our tool, SPHINX, lets a nave user define views using simple, example-based interaction. We show how federating view definitions may be represented using the version-space model. The benefits of having a meta-model of federating view definition include, first, integrating an active-learning method which removes from the user the burden of generating the examples. Second, we guarantee that SPHINX converges to a vetted, semantically accurate result, even if the user cannot understand a formal specification of the transformations

Miranker: Interactive Schema Integration with Sphinx

Abstract. The Internet has instigated a critical need for automated tools that facilitate integrating countless databases. Since non-technical end users are often the ultimate repositories of the domain information required to distinguish differences in data types, we suppose an effective solution must integrate simple GUI based data browsing tools and automatic mapping methods that eliminate technical users from the solution. We develop a meta-model of data integration as the basis for absorbing feedback from an end-user. The schema integration algorithm draws examples from the data and learns integrating view definitions by asking a user simple yes or no questions. The meta-model enables a search mechanism that is guaranteed to converge to a correct integrating view definition without the user having to know a view definition language such as SQL or even having to inspect the final view definition. We show how data catalog statistics, normally used to optimize queries, can be exploited to parameterize the search heuristics and improve the convergence of the learning algorithm.