551 research outputs found

    The Semantic Grid: A future e-Science infrastructure

    No full text
    e-Science offers a promising vision of how computer and communication technology can support and enhance the scientific process. It does this by enabling scientists to generate, analyse, share and discuss their insights, experiments and results in an effective manner. The underlying computer infrastructure that provides these facilities is commonly referred to as the Grid. At this time, there are a number of grid applications being developed and there is a whole raft of computer technologies that provide fragments of the necessary functionality. However there is currently a major gap between these endeavours and the vision of e-Science in which there is a high degree of easy-to-use and seamless automation and in which there are flexible collaborations and computations on a global scale. To bridge this practice–aspiration divide, this paper presents a research agenda whose aim is to move from the current state of the art in e-Science infrastructure, to the future infrastructure that is needed to support the full richness of the e-Science vision. Here the future e-Science research infrastructure is termed the Semantic Grid (Semantic Grid to Grid is meant to connote a similar relationship to the one that exists between the Semantic Web and the Web). In particular, we present a conceptual architecture for the Semantic Grid. This architecture adopts a service-oriented perspective in which distinct stakeholders in the scientific process, represented as software agents, provide services to one another, under various service level agreements, in various forms of marketplace. We then focus predominantly on the issues concerned with the way that knowledge is acquired and used in such environments since we believe this is the key differentiator between current grid endeavours and those envisioned for the Semantic Grid

    e-Business challenges and directions: important themes from the first ICE-B workshop

    Get PDF
    A three-day asynchronous, interactive workshop was held at ICE-B’10 in Piraeus, Greece in July of 2010. This event captured conference themes for e-Business challenges and directions across four subject areas: a) e-Business applications and models, b) enterprise engineering, c) mobility, d) business collaboration and e-Services, and e) technology platforms. Quality Function Deployment (QFD) methods were used to gather, organize and evaluate themes and their ratings. This paper summarizes the most important themes rated by participants: a) Since technology is becoming more economic and social in nature, more agile and context-based application develop methods are needed. b) Enterprise engineering approaches are needed to support the design of systems that can evolve with changing stakeholder needs. c) The digital native groundswell requires changes to business models, operations, and systems to support Prosumers. d) Intelligence and interoperability are needed to address Prosumer activity and their highly customized product purchases. e) Technology platforms must rapidly and correctly adapt, provide widespread offerings and scale appropriately, in the context of changing situational contexts

    Using metarules to integrate knowledge in knowledge based systems. An application in the woodworking industry

    Get PDF
    The current study addresses the integration of knowledge obtained from Data Mining structures and models into existing Knowledge Based solutions. It presents a technique adapted from commonKADS and spiral methodology to develop an initial knowledge solution using a traditional approach for requirement analysis, knowledge acquisition, and implementation. After an initial prototype is created and verified, the solution is enhanced incorporating new knowledge obtained from Online Analytical Processing, specifically from Data Mining models and structures using meta rules. Every meta rule is also verified prior to being included in the selection and translation of rules into the Expert System notation. Once an initial iteration was completed, responses from test cases were compared using an agreement index and kappa index. The problem domain was restricted to remake and rework operations in a cabinet making company. For Data Mining models, 8,674 cases of Price of Non Conformance (PONC) were used for a period of time of 3 months. Initial results indicated that the technique presented sufficient formalism to be used in the development of new systems, using Trillium scale. The use of 50 additional cases randomly selected from different departments indicated that responses from the original system and the solution that incorporated new knowledge from Data Mining differed significantly. Further inspection of responses indicated that the new solution with additional 68 rules was able to answer, although with an incorrect alternative in 28 additional cases that the initial solution was not able to provide a conclusion

    User Review Analysis for Requirement Elicitation: Thesis and the framework prototype's source code

    Get PDF
    Online reviews are an important channel for requirement elicitation. However, requirement engineers face challenges when analysing online user reviews, such as data volumes, technical supports, existing techniques, and legal barriers. Juan Wang proposes a framework solving user review analysis problems for the purpose of requirement elicitation that sets up a channel from downloading user reviews to structured analysis data. The main contributions of her work are: (1) the thesis proposed a framework to solve the user review analysis problem for requirement elicitation; (2) the prototype of this framework proves its feasibility; (3) the experiments prove the effectiveness and efficiency of this framework. This resource here is the latest version of Juan Wang's PhD thesis "User Review Analysis for Requirement Elicitation" and all the source code of the prototype for the framework as the results of her thesis

    An ebd-enabled design knowledge acquisition framework

    Get PDF
    Having enough knowledge and keeping it up to date enables designers to execute the design assignment effectively and gives them a competitive advantage in the design profession. Knowledge elicitation or acquisition is a crucial component of system design, particularly for tasks requiring transdisciplinary or multidisciplinary cooperation. In system design, extracting domain-specific information is exceedingly tricky for designers. This thesis presents three works that attempt to bridge the gap between designers and domain expertise. First, a systematic literature review on data-driven demand elicitation is given using the Environment-based Design (EBD) approach. This review address two research objectives: (i) to investigate the present state of computer-aided requirement knowledge elicitation in the domains of engineering; (ii) to integrate EBD methodology into the conventional literature review framework by providing a well-structured research question generation methodology. The second study describes a data-driven interview transcript analysis strategy that employs EBD environment analysis, unsupervised machine learning, and a range of natural language processing (NLP) approaches to assist designers and qualitative researchers in extracting needs when domain expertise is lacking. The second research proposes a transfer-learning method-based qualitative text analysis framework that aids researchers in extracting valuable knowledge from interview data for healthcare promotion decision-making. The third work is an EBD-enabled design lexical knowledge acquisition framework that automatically constructs a semantic network -- RomNet from an extensive collection of abstracts from engineering publications. Applying RomNet can improve the design information retrieval quality and communication between each party involved in a design project. To conclude, this thesis integrates artificial intelligence techniques, such as Natural Language Processing (NLP) methods, Machine Learning techniques, and rule-based systems to build a knowledge acquisition framework that supports manual, semi-automatic, and automatic extraction of design knowledge from different types of the textual data source
    corecore