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Funding
Grant Number: 1R01HL087795-01A1
NATIONAL HEART, LUNG, AND BLOOD INSTITUTE (NHLBI), NATIONAL INSTITUTES OF HEALTH (NIH)
Principal Investigator: Amit Sheth, Kno.e.sis Center, Wright State University
Co-Principal Investigator: Rick Tarleton, Center for Tropical and Emerging Global Diseases (CTEGD), University of Georgia
Co-Principal Investigator: Mark Musen, National Center for Biological Ontologies (NCBO), Stanford University
Co-Principal Investigator: Natasha Noy, National Center for Biological Ontologies (NCBO), Stanford University
Co-Principal Investigator: Prashant Doshi, Large Scale Distributed Information Systems (LSDIS) Lab, University of Georgia
The study of complex biological systems increasingly depends on vast amounts of dynamic information from diverse sources. The scientific analysis of the parasite Trypanosoma cruzi (T.cruzi), the principal causative agent of human Chagas disease, is the driving biological application of this proposal. Approximately 18 million people, predominantly in Latin America, are infected with the T.cruzi parasite. As many as 40 percent of these are predicted eventually to suffer from Chagas disease, which is the leading cause of heart disease and sudden death in middle-aged adults in the region. Research on T. cruzi is therefore an important human disease related effort. It has reached a critical juncture with the quantities of experimental data being generated by labs around the world, due in large part to the publication of the T.cruzi genome in 2005. Although this research has the potential to improve human health significantly, the data being generated exist in independent heterogeneous databases with poor integration and accessibility. The scientific objectives of this research proposal are to develop and deploy a novel ontology-driven semantic problem-solving environment (PSE) for T.cruzi. This is in collaboration with the National Center for Biomedical Ontologies (NCBO) and will leverage its resources to achieve the objectives of this proposal as well as effectively to disseminate results to the broader life science community, including researchers in human pathogens. The PSE allows the dynamic integration of local and public data to answer biological questions at multiple levels of granularity. The PSE will utilize state-of-the-art semantic technologies for effective querying of multiple databases and, just as important, feature an intuitive and comprehensive set of interfaces for usability and easy adoption by biologists. Included in the multimodal datasets will be the genomic data and the associated bioinformatics predictions, functional information from metabolic pathways, experimental data from mass spectrometry and microarray experiments, and textual information from Pubmed. Researchers will be able to use and contribute to a rigorously curated T.cruzi knowledge base that will make it reusable and extensible. The resources developed as part of this proposal will be also useful to researchers in T.cruzi related kinetoplastids, Trypanosoma brucei and Leishmania major (among other pathogenic organisms), which use similar research protocols and face similar informatics challenges.
Project Funding: $1500000
Project Period: 2008 - 2012
Web service composition
WSs are self-contained and modular software applications with standardized interfaces accessible to programs over the World Wide Web and provide the building blocks to realize the service-oriented computing paradigm. A key vision behind adopting service-oriented architectures is the ambitious potential for automatically and flexibly formulating compositions of services resulting in new composite services with integrated functionalities. Researchers in the THINC lab are investigating scalable approaches for automatically composing Web services under uncertainty into executable software processes. The research focuses on building compositions that satisfy functional preferences of users and provably optimize multiple non-functional parameters in the context of uncertainty of the WS outcomes. The research is being performed in the context of a novel hierarchical framework called Haley that enables logical composition of Web services. To form executable compositions, research is also being performed in the area of data mediation.
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Adaptation of Web service compositions
Researchers in the THINC lab are investigating ways to keep Web service compositions optimal in the context of a volatile environment that exhibits varying quality-of-service parameters for Web services. In order to remain up-to-date with the parameters, selected providers may be queried for their revised information. In this context, a novel method called the value of changed information is proposed that selects those services for querying whose revised parameters are expected to bring about the most change in the composition while being cognizant of the cost of querying. This method is being applied toward adapting compositions with varying structure in an efficient manner.
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Trust in Web service compositions
Web service composition techniques traditionally utilize the functional and quality-of-service parameters of candidate services to decide which services to include in the composition. However, users of services often form an opinion, somewhat subjective, of a service. This opinion may be based on prior interactions with the service, and may include judgments such as whether the perceived behavior of the service conforms to its stated behavior and intangibles such as the overall experience of the user with the service. Composition techniques that additionally consider this assessment of services by users will form compositions that likely behave in practice as stated, and which are better received by the users. Researchers in the THINC lab have developed a separate trust framework, called Wisp, that computes the aggregate trust in the composition from trust in individual Web services, and selects the composition that is most trustworthy.
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Amit Sheth
LexisNexis Professor of Computer Science Director of the Kno.e.SiS Center Wright State University, USA
John Miller
Professor of Computer Science Faculty member of the LSDIS Lab University of Georgia, USA