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e-Business Engineering (ICEBE), 2011 IEEE 8th International Conference on

Date 19-21 Oct. 2011

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Displaying Results 1 - 25 of 82
  • [Front cover]

    Page(s): C1
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  • [Title page i]

    Page(s): i
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  • [Title page iii]

    Page(s): iii
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  • [Copyright notice]

    Page(s): iv
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  • Table of contents

    Page(s): v - xi
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  • Message from ICEBE 2011 Chairs

    Page(s): xii
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  • Message from ICEBE 2011 Program Chairs

    Page(s): xiii - xiv
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  • ICEBE 2011 Organizing Committee

    Page(s): xv - xvi
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  • ICEBE 2011 Program Committee

    Page(s): xvii - xx
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  • Message from ASOC 2011 Workshop Chairs

    Page(s): xxi
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  • ASOC 2011 Workshop Chairs and Program Committee

    Page(s): xxii
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  • Message from EM2I 2011 Workshop Co-chairs

    Page(s): xxiii
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  • EM2I 2011 Workshop Co-chairs and Program Committee

    Page(s): xxiv
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  • Message from IWCS 2011 Workshop Co-chairs

    Page(s): xxv
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  • IWCS 2011 Workshop Co-chairs and Program Committee

    Page(s): xxvi
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  • Message from SOAIC 2011 Workshop Co-chairs

    Page(s): xxvii
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  • SOAIC 2011 Workshop Co-chairs and Program Committee

    Page(s): xxviii
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  • Message from SOKMBI 2011 Workshop Co-chairs

    Page(s): xxix
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  • SOKMBI 2011 Workshop Co-chairs and Program Committee

    Page(s): xxx - xxxi
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  • Keynote Abstracts

    Page(s): xxxii - xxxiv
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (131 KB)  

    The following topics are dealt with: e-business engineering; data management; service-oriented knowledge management; emergency communications; multi-agent supply chain collaboration operation model; cloud computing; business analytics; business optimization; Web service selection; user centric security mode; mobile commerce; pervasive commerce; service engineering; software engineering; warehousing approach; RFID; process automation; nonintrusive load monitoring system; e-marketplace integration; e-marketplace interoperability; cloud services; and business intelligence. View full abstract»

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  • Exploring the Relationships between Annual Earnings and Subjective Expressions in US Financial Statements

    Page(s): 1 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (285 KB) |  | HTML iconHTML  

    Subjective assertions in financial statements influence the judgments of market participants when they assess the value and profitability of the reporting corporations. Hence, the managements of corporations may attempt to conceal the negative and to accentuate the positive with "prudent" wording. To excavate this accounting phenomenon hidden behind financial statements, we designed an artificial intelligence based strategy to investigate the linkage between financial status measured by annual earnings and subjective multi-word expressions (MWEs). We applied the conditional random field (CRF) models to identify opinion patterns in the form of MWEs, and our approach outperformed previous work employing unigram models. Moreover, our novel algorithms take the lead to discover the evidences that support the common belief that there are inconsistencies between the implications of the written statements and the reality indicated by the figures in the financial statements. Unexpected negative earnings are often accompanied by ambiguous and mild statements and sometimes by promises of glorious future. View full abstract»

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  • A Neural Network Based Forecasting Method For the Unemployment Rate Prediction Using the Search Engine Query Data

    Page(s): 9 - 15
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (346 KB) |  | HTML iconHTML  

    Unemployment rate prediction has become critically important, because it can help government to make decision and design policies. In recent years, forecast of unemployment rate attracts much attention from governments, organizations, and research institutes, and researchers. Recently, a novel method using search engine query data to forecast unemployment was proposed by scholars. In this paper, a data mining based framework using web information is introduced for unemployment rate prediction. Under the framework, a neural network method, as one of the most effective data mining tools, is developed to forecast unemployment trend using search engine query data. In the proposed method, search engine query data related with employment activities is firstly found. Secondly, feature selection models including correlation coefficient method and genetic algorithm are constructed to reduce the dimension of the query data. Thirdly, various neural networks are employed to model the relationship between unemployment rate data and query data. Fourthly, an optimal neural network is selected as the selective predictor by using the cross-validation method. Finally, the selective neural network predictor with the best feature subset is used to forecast unemployment trend. The empirical results show that the proposed method clearly outperforms the classical forecasting approaches for the unemployment rate prediction. These findings imply that data mining method, such as neural networks, together with web information, can be used as an alternative tool to forecast social/economic hotspot. View full abstract»

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  • A Collaborative Learning System Based on Cloud and E-commerce

    Page(s): 16 - 23
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (486 KB) |  | HTML iconHTML  

    The number of learners using e-learning has been explosively increasing in the past decade by virtue of easy access to preferable educational resources at Internet. On the other hand, the number of teachers in schools or universities is growing slowly. As a result, instructional problems have emerged due to lack of sufficient support to learners in their e-learning process. Collaborative learning is suggested as a solution to this problem. However, current collaborative learning has focused on teamwork or group discussion without sufficient support to each individual. This paper presents a new model for collaborative e-learning called Collaborative Cloud, in which knowledge modelling and market economic mechanism are utilized to optimize the collaborative use of e-learning resources including teachers, students, and artifacts in collaboration. To implement the approach, cloud computing and electronic commerce are applied to connect learners and coordinate the resources in e-learning in a more effective way. View full abstract»

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  • Instance-Driven Ontology Evolution Mechanism towards Enterprise Data Management

    Page(s): 24 - 30
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (752 KB) |  | HTML iconHTML  

    With the wide application of information systems, more and more enterprises adopt ontology as conceptual backbone for business data management to take advantage of knowledge base and semantic web technology. However, dynamic business motivates modifying existed ontology according to the continuous changes, which is hard to be done manually due to the growing size of ontology. Thus automatic method is needed for ontology evolution. In this paper, an instance-driven ontology evolution approach is proposed to cope with the dynamic changes in ontology evolution. The proposed approach suggests changing directions to users to control the process of evolution. Then the changes of ontology are formalized with elementary and composite changes which are considered as the basis of evolution. Moreover, the identification of changes is realized by instances analyzing. Finally, the approach is tested in a hospital database. The result shows instance-driven ontology evolution is an efficient method to cope with data conception changes in enterprise data management. View full abstract»

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  • A Method of Building Virtual Datacenter Based on Semantic Views

    Page(s): 31 - 35
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (576 KB) |  | HTML iconHTML  

    Massive data exists in Well-Engineering domain, and they are distributed or heterogeneous. These features lead to difficulties during data integration and global decision making. For this problem, this paper proposes a solution of building virtual data center by means of semantic integration technology. In this method, schemas of data sources are firstly mapped to semantic views on domain ontology, secondly, with translation algorithm, global semantic query is rewritten into series of SQL statements which will be dispatched to relevant data sources and executed on local sites, finally, query results are reorganized and submitted to users. By practical application, this virtual data center can supply semantic-based data support for well production decision. View full abstract»

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