22845738
A Policy-Driven Approach for Software-as-Services Customization
Software as services (SaS) is emerging as a new application software delivery approach, and has extensively leveraged Web services and other SOA concepts and technologies. Compared to the traditional ones, SaS has the following distinctive features: application delivery over Internet, usage based pricing mechanism, etc. Potentially, this approach can significantly reduce the total cost of ownership of application software. However, it does have a number of unique issues that include service customization, multi-tenancy, complex billing model, etc. If not handled properly, these issues can result in ad-hoc and manual service consumption practices, which could eliminate the benefits of the SaS approach. In this paper, we are going to specifically deal with the service customization issue. To that end, we proposed a novel SaS customization policy and a supporting framework that is realized through a design-time tooling and a runtime environment. We also provided a case study to verify the effectiveness of the proposed approach.
43377731
Software as a Service: An Integration Perspective
Software as a Service (SaaS) is gaining momentum in recent years with more and more successful adoptions. Though SaaS is delivered over Internet and charged on per-use basis, it is software application in essence. SaaS contains business data and logics which are usually required to integrate with other applications deployed by a SaaS subscriber. This makes Integration become one of the common requirements in most SaaS adoptions. In this paper, we analyze the key functional and non-functional SaaS integration requirements from an industry practitioner point of view; and summarize the SaaS integration patterns and existing offerings; then point out the gaps from both technology and tooling perspectives; finally we introduce a SaaS integration framework to address those gaps. Considering there is no much academic work on SaaS service modeling, we come up with a SaaS service description framework as an extension of Web Service description, so as to model SaaS unique features in a unified way. With the supported tooling and runtime platform, the framework can facilitate the SaaS integration lifecycle in a model-driven approach.
9437535
Practical Application of FDC in Software Service Pricing
This paper proposes a modified fully distributed cost (FDC) pricing method with third-degree price discrimination for software service. The method is a feasible and simple method to be used in practice. The pricing process is generalized as involving data collection, strategy analysis and strategy establishment. Then a practical SCM software service pricing in retail industry is described
207113548
A Hybrid Two-Phase Algorithm For Face Recognition
Scientists have developed numerous classifiers in the pattern recognition field, because applying a single classifier is not very conducive to achieve a high recognition rate on face databases. Problems occur when the images of the same person are classified as one class, while they are in fact different in poses, expressions, or lighting conditions. In this paper, we present a hybrid, two-phase face recognition algorithm to achieve high recognition rates on the FERET data set. The first phase is to compress the large class number database size, whereas the second phase is to perform the decision-making. We investigate a variety of combinations of the feature extraction and pattern classification methods. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM) are examined and tested using 700 facial images of different poses from FERET database. Experimental results show that the two combinations, LDA+LDA and LDA+SVM, outperform the other types of combinations. Meanwhile, when classifiers are considered in the two-phase face recognition, it is better to adopt the L1 distance in the first phase and the class mean in the second phase.
7532408
Simplifying Mixture Models Through Function Approximation
The finite mixture model is widely used in various statistical learning problems. However, the model obtained may contain a large number of components, making it inefficient in practical applications. In this paper, we propose to simplify the mixture model by minimizing an upper bound of the approximation error between the original and the simplified model, under the use of the L 2 distance measure. This is achieved by first grouping similar components together and then performing local fitting through function approximation. The simplified model obtained can then be used as a replacement of the original model to speed up various algorithms involving mixture models during training (e.g., Bayesian filtering, belief propagation) and testing [e.g., kernel density estimation, support vector machine (SVM) testing]. Encouraging results are observed in the experiments on density estimation, clustering-based image segmentation, and simplification of SVM decision functions.
4300632
Keyword Extraction Using Support Vector Machine
This paper is concerned with keyword extraction. By keyword extraction, we mean extracting a subset of words/phrases from a document that can describe the ‘meaning' of the document. Keywords are of benefit to many text mining applications. However, a large number of documents do not have keywords and thus it is necessary to assign keywords before enjoying the benefit from it. Several research efforts have been done on keyword extraction. These methods make use of the ‘global context information', which makes the performance of extraction restricted. A thorough and systematic investigation on the issue is thus needed. In this paper, we propose to make use of not only ‘global context information', but also ‘local context information' for extracting keywords from documents. As far as we know, utilizing both ‘global context information' and ‘local context information' in keyword extraction has not been sufficiently investigated previously. Methods for performing the tasks on the basis of Support Vector Machines have also been proposed in this paper. Features in the model have been defined. Experimental results indicate that the proposed SVM based method can significantly outperform the baseline methods for keyword extraction. The proposed method has been applied to document classification, a typical text mining processing. Experimental results show that the accuracy of document classification can be significantly improved by using the keyword extraction method.
8216383
Locating object contours in complex background using improved snakes
An active contour model, called snake, can adapt to object boundary in an image. A snake is defined as an energy minimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines or edges. The traditional snake model fails to locate object contours that appear in complex background. In this paper, we present an improved snake model associated with new regional similarity energy and a gravitation force field to attract the snake approaching the object contours efficiently. Experiment results show that our snake model works successfully for convex and concave objects in a variety of complex backgrounds.
35977333
Multi-Media Retrieval with Semantic Web: A Case Study in Airport Security Inspection Applications
Multi-media retrieval based on content meaning is an important issue. It has significant applications in the tracing of terrorists based on airport security inspection information. In this paper, we review the defects of traditional image retrieval technology. A semantic Web based image retrieval method is proposed, which is deployed into an airport security inspection information retrieval system. We present the techniques of domain ontology construction and semantic-Web annotation for the media. An image retrieval technique based on semantic annotation is suggested, and the design of a semantic information retrieval prototype system for airport security inspection application is described.