20441735
Web Services Discovery with Rough Sets
Web services are emerging as a major technology for building service-oriented distributed systems. Potentially, various resources on the Internet can be virtualized as Web services for a wider use by their communities. Service discovery becomes an issue of vital importance for Web services applications. This article presents ROSSE, a Rough Sets based Search Engine for Web service discovery. One salient feature of ROSSE lies in its capability to deal with uncertainty of service properties when matching services. A use case is presented to demonstrate the use of ROSSE for discovery of car services. ROSSE is evaluated in terms of its accuracy and efficiency in service discovery.
42842563
A WSRF Based Shopping Cart System
Web Services Resource Framework (WSRF) is a set of specifications that represents a convergence point of the Web services and the Grid services communities. This paper presents our early experience with WSRF. A shopping cart system has been implemented with WSRF supported Globus toolkit 3.9.2 (GT3.9.2). Based on the system, the performance of the WSRF core in GT3.9.2 has also been evaluated.
16993770
Service matchmaking with rough sets
With the wide adoption of open grid services architecture (OGSA) and Web services resource framework (WSRF), the grid is emerging as a service-oriented computing infrastructure for engineers and scientists to solve data and computationally intensive problems. It is envisioned that computing resources in a future grid environment will be exposed as services. Service discovery becomes an issue of vital importance for a wider uptake of the grid. This paper presents RSSM, a rough sets based service matchmaking algorithm for service discovery with an aim to tolerate uncertainty in identifying service properties. The evaluation results show that the RSSM algorithm is more effective in service discovery compared with other mechanisms such as UDDI and OWLS.
1687904
PGGA: A predictable and grouped genetic algorithm for job scheduling
This paper presents a predictable and grouped genetic algorithm (PGGA) for job scheduling. The novelty of the PGGA is two-fold: (1) a job workload estimation algorithm is designed to estimate a job workload based on its historical execution records and (2) the divisible load theory (DLT) is employed to predict an optimal fitness value by which the PGGA speeds up the convergence process in searching a large scheduling space. Comparison with traditional scheduling methods, such as first-come-first-serve (FCFS) and random scheduling, heuristics, such as a typical genetic algorithm, Min-Min and Max-Min indicates that the PGGA is more effective and efficient in finding optimal scheduling solutions.
45224109
Service Matchmaking and Discovery with Rough Sets
The past few years have seen the Grid is evolving as a service-oriented computing infrastructure. It is foreseen that various resources in a Grid environment will be exposed as services for use by a wider user community. Service discovery becomes an issue of vital importance for utilising Grid facilities. This paper presents Rosse, a Rough Sets based Search Engine for service discovery that can deal with uncertainty of service properties when matching service advertisements with service requests. The evaluation results show that Rosse is more effective in service discovery compared with other mechanisms such as UDDI keyword matching and OWL-S matchmaking.
15008871
Large Sample Properties of Weighted Monte Carlo Estimators
A general approach to improving simulation accuracy uses information about auxiliary control variables with known expected values to improve the estimation of unknown quantities. We analyze weighted Monte Carlo estimators that implement this idea by applying weights to independent replications. The weights are chosen to constrain the weighted averages of the control variables. We distinguish two cases (unbiased and biased), depending on whether the weighted averages of the controls are constrained to equal their expected values or some other values. In both cases, the number of constraints is usually smaller than the number of replications, so there may be many feasible weights. We select maximally uniform weights by minimizing a separable convex function of the weights subject to the control variable constraints. Estimators of this form arise (sometimes implicitly) in several settings, including at least two in finance: calibrating a model to market data (as in the work of Avellaneda et al. 2001) and calculating conditional expectations to price American options. We analyze properties of these estimators as the number of replications increases. We show that in the unbiased case, weighted Monte Carlo reduces asymptotic variance, and that all convex objective functions within a large class produce estimators that are very close to each other in a strong sense. In contrast, in the biased case the choice of objective function does matter. We show explicitly how the choice of objective determines the limit to which the estimator converges.
34993492
Microarray image compression: SLOCO and the effect of information loss
Microarray image technology is a powerful tool for monitoring the expression of thousands of genes simultaneously. Each microarray experiment produces immense amounts of image data, and efficient storage and transmission require compression that takes advantage of microarray image structure. In this paper we develop a compression scheme for microarray images which can be either lossless or lossy with successive refinements. Existing measures of distortion such as mean squared pixel-wise error and visual fidelity are not appropriate for microarray images. We introduce a new measure of distortion for lossy compression: the sensitivity of microarray information extraction to compression loss. Furthermore, our scheme has a coded data structure that allows fast decoding and reprocessing of image sub-blocks, and includes summary statistics and image segmentation information. The average lossless compression ratio is 1.83:1 for our cDNA test images and 2.43:1 for our inkjet test images, comparable or better than state-of-the-art lossless schemas, yet with additional structure and information. At an average lossy compression ratio of 8:1 for cDNA microarrays, we find that our scheme minimizes the effects of compression loss compared to other algorithms. We show that the variability in differential gene expression levels extracted from lossily vs. losslessly compressed microarray images is less than both the variability between different arrays and the variability between different extraction algorithms. In fact, lossy compression can improve the estimation of gene expression levels for cDNA images.
28131114
Utilizing Fuzzy Petri Net for Choreography Based Semantic Web Services Discovery
Semantic Web Services have received great popularity these days for their capability of providing more automatic web services discoveries and compositions. However, the issue of behavior compatibility is still not properly solved. In this paper, we propose a choreography-based formal approach to discover semantic web services that are compatible with their behaviors. The fuzzy petri net is used to perform the modeling, verification, analysis and evaluation for the matchmaking of semantic web services. The requested and provided services are modeled by two fuzzy petri nets combined through a set of combination rules. By analyzing the composite fuzzy petri net, the behavior compatibility can be verified, and the matching degree between requested and provided services can be obtained. With our approach, the success rate and accuracy of the semantic web services discoveries can be improved, and the final services set can be optimized.
17737533
Semantic Matching of Web Services Based on Choreographies
Web services facilitate the efficient execution of B2B e-commerce by integrating business processes over the Internet, which needs dynamic and flexible binding of services. However, current Web service standards do not support it. A formal approach for semantic matching of service specifications based on choreographies, i.e., the behavior of Web services, is developed in this paper. It can accurately and automatically discover required services for integrating business processes. Firstly an extended deterministic finite state automaton (EDFA) is proposed by labeling state transitions using binary-tuples (input, output) rather than letters. The automata describe Web services in a more accurate way: the nodes represent states maintained by services; the state transitions represent communication activities of services. The automata depict the temporal sequences of communication activities that describe the behaviors of services. Further, the intersection of two EDFAs is defined. Finally, an algorithm for testing the emptiness of the languages accepted by EDFAs is presented and used to evaluate the compatibility of Web services
677559
TEEVE: the next generation architecture for tele-immersive environments
Tele-immersive 3D multi-camera room environments are starting to emerge and with them new challenging research questions. One important question is how to organize the large amount of visual data, being captured, processed, transmitted and displayed, and their corresponding resources, over current COTS computing and networking infrastructures so that "everybody" would be able to install and use tele-immersive environments for conferencing and other activities. In this paper, we propose a novel cross-layer control and streaming framework over general purpose delivery infrastructure, called TEEVE (tele-immersive environments for everybody). TEEVE aims for effective and adaptive coordination, synchronization, and soft QoS-enabled delivery of tele-immersive visual streams to remote room(s). The TEEVE experiments between two tele-immersive rooms residing in different institutions more than 2000 miles apart show that we can sustain communication of up to 12 3D video streams with 4/spl sim/5 3D frames per second for each stream, yielding 4/spl sim/5 tele-immersive video rate.