34237375
On-Demand Business Collaboration Enablement with Services Computing
In this talk, I will introduce a model for on-demand business process based collaboration, namely, Extended Business Collaboration (eBC), and its major characteristics: modeling, solution stack, configurable business protocol enabling framework, on-demand services deployment, as well as monitoring and analytical control. Then we present a few major research issues associated with facilitating extended business collaboration, followed by our proposed Annotated Business HyperChain technology leveraging Services computing and semantic annotation model. Services computing is the evolution of Internet computing by leveraging services oriented architecture. Services computing is mainly targeting for a new ground break technology suite – Web services and services oriented Grid/Utility Computing, Business Computing, and Autonomic Computing. A research prototype is illustrated at the end of the talk followed by some observations and discussion of open research issues requiring further exploration.
17426553
On-demand business collaboration enablement with web services
Businesses are increasingly outsourcing key operations and interacting with ever extending nets of partners. Running extended business-to-business (B2B) operations creates the need for more advanced human interaction while advancing the automation base of B2B functions. In this paper, we introduce a model for on-demand business process-based collaboration, namely, Extended Business Collaboration (eBC), and its major elements of modeling, and a configurable business protocol-enabling framework. We discuss some of the major research issues associated with facilitating extended business collaboration, and present our proposed Annotated Business HyperChain technology leveraging Web services and semantic annotation model. A research prototype is described, followed by some observations and discussion of open research issues requiring further exploration.
32965761
The Next Big Thing: Web Services Collaboration
In this paper, we introduce the lifecycle of a Web services solution that consists of Web services modeling, development, deployment, publishing, as well as discovery, composition and collaboration, plus monitoring and analytical control. Then some research and development challenges of building a Web services solution in the context of Web services solution lifecycle are described. Especially, we present an emerging Web services research direction, namely, Web services collaboration, followed by a few major research issues associated with facilitating extended business collaboration using Web services and semantic annotation.
36054855
MQML-Message Queuing Markup Language
Message Queuing allows applications to use components that communicate with one another using queued messages. In this paper, we propose a Message Queuing Markup Language (MQML), which is an XML based markup language designed to describe e-business applications and enterprise integration. The MQML language is extensible. The detailed description of the MQML is presented in this paper. An research prototype is given to validate the effectiveness and value of our proposed MQML for enterprise integration.
19524119
Challenges and Opportunities for Web Services Research - Editorial Preface
21758360
WS-trustworthy: a framework for Web services centered trustworthy computing
The emerging paradigm of Web services has been gaining significant momentum in the recent years since it offers a promising way to facilitate business-to-business (B2B) collaboration. However, it is not clear that this new model of Web services provides any measurable increase in computing trustworthiness. We propose a generic framework to control the trustworthiness of computing in the domain of Web services. A layered model is established to highlight four key elements: resources, policies, validation processes, and management. The robustness of this model exhibits its flexibility and extensibility. Examples utilizing our framework are reported.
1882847
User Feedback-Based Refinement for Web Services Retrieval using Multiple Instance Learning
A critical step in the process of reusing existing WSDL-specified components is the discovery of potentially relevant Web services. Traditional category based Web service retrieval usually can achieve good recall but worse precision because some semantically relevant Web services are not actually relevant as they cannot provide suitable interfaces. In this paper, we present an interactive Web services retrieval mechanism to refine the coarse retrieval results set in category based retrieval. In the refinement, the signature matching of Web services that concerning the structure of operation specifications is investigated from a multi-instances view. In detail, each Web service is represented as a bag in multiple instance learning, while each operation in this Web service is regarded as an instance. This representation lies in that a user regards a service as useful if at least one operation provided by this Web service is useful. Experimental results show that our approach can improve the retrieval performance significantly: It can gain 83% precision in average after two rounds of user relevance feedback
14488825
Image annotation using search and mining technologies
In this paper, we present a novel solution to the image annotation problem which annotates images using search and data mining technologies. An accurate keyword is required to initialize this process, and then leveraging a large-scale image database, it 1) searches for semantically and visually similar images, 2) and mines annotations from them. A notable advantage of this approach is that it enables unlimited vocabulary, while it is not possible for all existing approaches. Experimental results on real web images show the effectiveness and efficiency of the proposed algorithm.
15719994
IGroup: presenting web image search results in semantic clusters
Current web image search engines still rely on user typing textual description: query word(s) for visual targets. As the queries are often short, general or even ambiguous, the images in resulting pages vary in content and style. Thus, browsing with these results is likely to be tedious, frustrating and unpredictable.
 IGroup, a proposed image search engine addresses these problems by presenting the result in semantic clusters. The original result set was clustered in semantic groups with a cluster name relevant to user typed queries. Instead of looking through the result pages or modifying queries, IGroup users can refine findings to the interested sub-result sets with a navigational panel, where each cluster (sub-result set) was listed with a cluster name and representative thumbnails of the cluster.
 We compared IGroup with a general web image search engine: MSN, in term of efficiency, coverage, and satisfaction with a substantial user study. Our tool shows significant improvement in such criteria.
8491161
Focused named entity recognition using machine learning
In this paper we study the problem of finding most topical named entities among all entities in a document, which we refer to as focused named entity recognition. We show that these focused named entities are useful for many natural language processing applications, such as document summarization, search result ranking, and entity detection and tracking. We propose a statistical model for focused named entity recognition by converting it into a classification problem. We then study the impact of various linguistic features and compare a number of classification algorithms. From experiments on an annotated Chinese news corpus, we demonstrate that the proposed method can achieve near human-level accuracy.
52862068
Robust face alignment based on local texture classifiers
We propose a robust face alignment algorithm with a novel discriminative local texture model. Different from the conventional descriptive PCA local texture model in ASM, classifiers using LUT-type Haar-like features are trained from a large data set as local texture model. The strong discriminative power of the classifier greatly improves the accuracy and robustness of local searching on faces with expression variation and ambiguous contours. A Bayesian framework is configured for shape parameter optimization and the algorithm is implemented in a hierarchical structure for efficiency. Extensive experiments are reported to show its accuracy and robustness.