Data-driven synthesis of multiple recommendation patterns to create situational Web mashups
Data-driven synthesis of multiple recommendation patterns to create situational Web mashups作者机构：Key Laboratory of High Confidence Software Technologies(Peking University)Ministry of Education University of Miami
出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))
年 卷 期：2013年第56卷第8期
页 面：137-152页
核心收录：
学科分类：08[工学] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术]
基 金：supported by National Basic Research Program of China (973 Program) (Grant No. 2009CB320703) National Natural Science Foundation of China (Grant Nos. 91118004,61003010) National High-tech R&D Program of China (863 Program) (Grant No. 2012AA011207) the NCET
主 题：mashup Internetware recommendation data-driven model situational
摘 要：As a typical situational application,Web mashup reflects and accommodates some key features of Internetware *** provides a development fashion that integrates data,computation and UI elements from multiple resources into a single Web application,and promises the quick rollout of creating potential new functionalities *** paper focuses on the problem of recommending useful suggestions for developing data-driven mashups by synthesis of multiple *** present a rapid and intuitive system called iMashupAdvisor,for aiding mashup development based on a novel automated suggestion *** key observation guiding the development of iMashupAdvisor is that mashups developed by different users might share some common patterns,for instance,selecting similar mashup components for similar goals,and gluing them in a similar *** patterns could reside in multiple sources,e.g.,the data dependency between mashup components,the interaction between users and mashup components,or the collective intelligence from existing applications created and maintained by programmers,*** leverages the synthesis of these patterns to recommend useful suggestions for a partial mashup,such as the missing components,connections between them,or potentially relevant options,to assist mashup *** paper presents the data model and ranking metrics of the synthesis process,and introduces efficient algorithms for the retrieval of *** also experimentally demonstrate the efficiency of our approach for benefiting the proposed rapid mashup development.
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