A P2P Integration Architecture for Protein Resources

The availability of a direct pathway from a primary sequence (denovo or DNA derived) to macromolecular structure to biological function using computer-based tools is the ultimate goal for a protein scientist. Today\u27s state of the art protein resources and on-going research and experiments provide the raw data that can enable protein scientists to achieve at least some steps of this goal. Thus, protein scientists are looking towards taking their benchtop research from the specific to a much broader base of using the large resources of available electronic information. However, currently the burden falls on the scientist to manually interface with each data resource, integrate the required information, and then finally interpret the results. Their discoveries are impeded by the lack of tools that can not only bring integrated information from several known data resources, but also weave in information as it is discovered and brought online by other research groups. We propose a novel peer-to-peer based architecture that allows protein scientists to share resources in the form of data and tools within their community, facilitating ad hoc, decentralized sharing of data. In this paper, we present an overview of this integration architecture and briefly describe the tools that are essential to this framework

Finding a Needle in the Haystack: A Technique for Ranking Matches Between Components

Abstract. Searching and subsequently selecting reusable components from com-ponent repositories has become a key impediment for not only component-based development but also for achieving the overall usability of component develop-ment environments and the ultimate re-usability of the components themselves. Component matching, a fundamental aspect of the component search problem, has been a well-studied problem, resulting in many different matching technique

QMatch - A Hybrid Match Algorithm for XML Schemas

Integration of multiple heterogeneous data sources continues to be a critical problem for many application domains and a challenge for researchers world-wide. With the increasing popularity of the XML model and the proliferation of XML documents on-line, automated matching of XML documents and databases has become a critical problem. In this paper, we present a hybrid schema match algorithm- QMatch- that provides a unique framework for combining existing structural and linguistic algorithms while exploiting additional information inherent in XML documents such as the order of XML elements to provide improved levels of matching between two given XML Schemas. QMatch is based on an extension of our previous work on QoM, a Quality of Match metric that measures the “goodness” of a match of two UML-based schemas. We now extend the QoM taxonomy to encapsulate the richness of information captured in XML Schemas and provide a qualitative and quantitative analysis of the information capacity of XML Schemas. QoM provides not only a means of tuning existing schema match algorithms to output at desired levels of matching but also provides an effective basis for a new schema match algorithm. In this paper we show via a set of experiments the benefits of QMatch over using individual structural and linguistic algorithms for schema matching, and provide an empirical measure of the accuracy of QMatch in terms of the true positives discovered by the algorithm.