62,767 research outputs found
Grid service discovery with rough sets
Copyright [2008] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.The computational grid is evolving as a service-oriented computing infrastructure that facilitates resource sharing and large-scale problem solving over the Internet. Service discovery becomes an issue of vital importance in utilising grid facilities. This paper presents ROSSE, a Rough sets based search engine for grid service discovery. Building on Rough sets theory, ROSSE is novel in its capability to deal with uncertainty of properties when matching services. In this way, ROSSE can discover the services that are most relevant to a service query from a functional point of view. Since functionally matched services may have distinct non-functional properties related to Quality of Service (QoS), ROSSE introduces a QoS model to further filter matched services with their QoS values to maximise user satisfaction in service discovery. ROSSE is evaluated in terms of its accuracy and efficiency in discovery of computing services
Integrating and Ranking Uncertain Scientific Data
Mediator-based data integration systems resolve exploratory queries by joining data elements across sources. In the presence of uncertainties, such multiple expansions can quickly lead to spurious connections and incorrect results. The BioRank project investigates formalisms for modeling uncertainty during scientific data integration and for ranking uncertain query results. Our motivating application is protein function prediction. In this paper we show that: (i) explicit modeling of uncertainties as probabilities increases our ability to predict less-known or previously unknown functions (though it does not improve predicting the well-known). This suggests that probabilistic uncertainty models offer utility for scientific knowledge discovery; (ii) small perturbations in the input probabilities tend to produce only minor changes in the quality of our result rankings. This suggests that our methods are robust against slight variations in the way uncertainties are transformed into probabilities; and (iii) several techniques allow us to evaluate our probabilistic rankings efficiently. This suggests that probabilistic query evaluation is not as hard for real-world problems as theory indicates
Mass-Radius Relationships for Solid Exoplanets
We use new interior models of cold planets to investigate the mass-radius
relationships of solid exoplanets, considering planets made primarily of iron,
silicates, water, and carbon compounds. We find that the mass-radius
relationships for cold terrestrial-mass planets of all compositions we
considered follow a generic functional form that is not a simple power law:
for up to , where and are scaled mass and radius
values. This functional form arises because the common building blocks of solid
planets all have equations of state that are well approximated by a modified
polytrope of the form .
We find that highly detailed planet interior models, including temperature
structure and phase changes, are not necessary to derive solid exoplanet bulk
composition from mass and radius measurements. For solid exoplanets with no
substantial atmosphere we have also found that: with 5% fractional uncertainty
in planet mass and radius it is possible to distinguish among planets composed
predominantly of iron or silicates or water ice but not more detailed
compositions; with ~5% uncertainty water ice planets with
water by mass may be identified; the minimum plausible planet size for a given
mass is that of a pure iron planet; and carbon planet mass-radius relationships
overlap with those of silicate and water planets due to similar zero-pressure
densities and equations of state. We propose a definition of "super Earths''
based on the clear distinction in radii between planets with significant gas
envelopes and those without.Comment: ApJ, in press, 33 pages including 16 figure
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