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Contents:
  1. Top 10 Analytics And Business Intelligence Trends for 12222
  2. Trends in Information Technology for - Vista College
  3. Is Today’s Data Market Outgrowing the Relational Database Model?
  4. Slide Show & Sessions

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Top 10 Analytics And Business Intelligence Trends for 12222

Search for all books with this author and title. Customers who bought this item also bought. Stock Image. Published by Springer Users can collaboratively engage in model building and curation processes. Technological implications and advantages of Web services as a means to support computer-to-computer interaction were discussed earlier. Providing Web services based pipeline will help users to explore and manipulate data, mix and match Web services that can use the data in variety of novel ways and solve complex problems. Launching several useful tools and algorithms that are often used for analysis, data management and visualization similar to Google docs, 14 , as Web applications and services software as a service, SaaS will help the scientific community in multiple ways.

It is better and advantageous way, doing work online constantly connected, collaborating and sharing information.

Trends in Information Technology for - Vista College

Software can be constantly improved in response to user feedback and needs. It also eliminates software platform dependency enabling use on diverse operating platforms e. All this eases data assembling and integration from heterogeneous data sources and as a result Web2. The role of modeling and simulation in the Systems driven analysis of living systems is now clearly established. Emerging disciplines, such as systems biology, and other worldwide research initiatives, such as the Physiome project Hunter et al.

To handle such complex integration challenges and improve our ability to conduct biologically meaningful system scale analysis require a unique, interoperable, universal information framework with the following characteristics Boyle et al. Number of novel solutions for developing information frameworks with the above functionalities has been proposed in terms of prototype systems e.

Like many other research groups, we also agree that future of systems research requires semantically based data integration through ontologies. Putting data into easily accessible repository in the standardized format is essential part of realizing Semantic Web vision. The potential benefits of SWT are being realized increasingly in life science and health care community.

Is Today’s Data Market Outgrowing the Relational Database Model?

There is work in progress trying to address the issues, develop, and support the use of SWT through internationally organized efforts e. Just as any evolving new technology, Semantic Web is also full of issues. Two particular prevailing problem of Semantic Web is the ambiguous identification of resources and vagueness of resource definition Wang et al.


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  5. This means that different ontologies often refer to the same concept with different URIs and a particular resource could have ambiguous descriptions. This issue of ambiguity calls for URI resolution steps, while integrating data. Since biologists who are involved in ontology development are focused on Semantic rather than the Web technology. Just creation of ontologies will be of little use if developed ontological concepts are kept hidden inaccessible or redundantly identified in the Semantic Web.

    It is extremely important to provide explicit access to each ontological concept via resolvable URIs.

    Either ontology providers should build concept resolution system by themselves or they should make the ontologies available to consortiums that have infrastructure to launch ontology resolution services. Also URI harmonization strongly requires both technical and social collaboration. Currently several proposal for standardized identification of biological entities and relationship e. However there is no consensus yet reached to use a common accepted identification scheme. The Semantic community should work toward a possible agreement of using an explicit identification system that could help unambiguously specify biological resources.

    To address this problem, efforts by Shared Names initiative 16 is a valuable beginning to normalize URI's in the biomedical context. Having met the above requirements, in future, SWT will receive wide adoption with in systems driven research community. The reason lies in practical difficulties implementing such mechanisms to extract meaningful knowledge from raw integrated data. Very few recent projects have tried to leverage the capabilities of inference technology.

    The RDFScape Splendiani, , is one of the few recent work in life sciences domain, which has attempted to use reasoners program which can determine relations among ontology classes on BioPAX data inside Cytoscape Shannon et al. In another novel study a different approach using Semantic Web methodologies to integrate gene data with phenotype data was demonstrated. It used RDF graph network analysis with reasoners to prioritize candidate cardiovascular disease genes Gudivada, However all the projects that have used inference technology are tried on the data integrated from limited number of resources, but not on Web-scale datasets.

    Slide Show & Sessions

    Ideally where RDFS and OWL constructions are used, it should be possible to apply automated reasoning over data schema and innovate meaningful knowledge. However at present Semantic Web is not completely ready to equip with inference engines. One of the potential reason is problems posed by large ontologies [e. It is currently unfeasible to retrieve, modify and process concepts at runtime as conceived for their utilization on Semantic Web. Because current reasoners and other tools that support Semantic Web require that all the information that they process should be loaded into memory.

    This can severely curtail performance or even fail when scaled to large ontologies.

    What it is and why it matters

    Several algorithms have been proposed to decompose large ontologies into less manageable and meaningful pieces retaining some of the semantics of the full version. However multiple limitations still persist, requiring future research aimed in this direction. In previous section Federated Databases and Web Services we discussed how Web services can help to develop federated systems that could keep pace with the rapid advances in systems research. But, numerous issues associated with Web services could hinder the progress.

    Issues related to maintenance of code that might affect the scalability and ease of development when Web services are built on SOA. Newer software development paradigm like Aspect Oriented Programming AOP solves the problems associated with code-tangling and code-scattering Kiczales et al. But, adoption of AOP is yet to percolate into life science development stream. Ontologically described Web service interfaces are not yet completely available which need to be addressed for realization of automatic discovery of services. A limitation of WSDL and SOAP is it being purely syntactical cannot express the semantics of underlying data and services which renders them inaccessible by machine.

    Adding semantics to represent the requirements and capabilities of Web services is essential for achieving unambiguity and machine interpretability. Work on automatic higher level integration of Web services and data by machine is in its incipient stage and progressing slow. The reason underlying slow implementation of such a useful infrastructure is: 1 it presupposes a presence of formal logic over Web resources i. Recently, WSDL-S semantic markup of Web services description language was proposed as an alternative solution to the problem Miller et al.

    D, and BioMoby Wilkinson et al. Adoption of Web3. The Web3. This means Web3.