Data-driven synthesis of multiple recommendation patterns to create situational Web mashups  

作　　者：MA Yun LU Xuan LIU XuanZhe WANG XuDong BLAKE M.Brian 

机构地区：[1]Key Laboratory of High Confidence Software Technologies(Peking University),Ministry of Education [2]University of Miami

出　　处：《Science China(Information Sciences)》2013年第8期133-148,共16页中国科学（信息科学）（英文版）

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

摘　　要：As a typical situational application, Web mashup reflects and accommodates some key features of Internetware paradigm. Mashup provides a development fashion that integrates data, computation and UI ele- ments from multiple resources into a single Web application, and promises the quick rollout of creating potential new functionalities opportunistically. This paper focuses on the problem of recommending useful suggestions for developing data-driven mashups by synthesis of multiple patterns. We present a rapid and intuitive system called iMashupAdvisor, for aiding mashup development based on a novel automated suggestion mechanism. The 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 manner. Such 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, etc. iMashupAdvisor leverages the synthesis of these patterns to recommend useful suggestions for a partial mashup, such as the missing components, connec- tions between them, or potentially relevant options, to assist mashup completion. This paper presents the data model and ranking metrics of the synthesis process, and introduces efficient algorithms for the retrieval of rec- ommendations. We also experimentally demonstrate the efficiency of our approach for benefiting the proposed rapid mashup development.As a typical situational application, Web mashup reflects and accommodates some key features of Internetware paradigm. Mashup provides a development fashion that integrates data, computation and UI ele- ments from multiple resources into a single Web application, and promises the quick rollout of creating potential new functionalities opportunistically. This paper focuses on the problem of recommending useful suggestions for developing data-driven mashups by synthesis of multiple patterns. We present a rapid and intuitive system called iMashupAdvisor, for aiding mashup development based on a novel automated suggestion mechanism. The 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 manner. Such 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, etc. iMashupAdvisor leverages the synthesis of these patterns to recommend useful suggestions for a partial mashup, such as the missing components, connec- tions between them, or potentially relevant options, to assist mashup completion. This paper presents the data model and ranking metrics of the synthesis process, and introduces efficient algorithms for the retrieval of rec- ommendations. We also experimentally demonstrate the efficiency of our approach for benefiting the proposed rapid mashup development.

关 键 词：MASHUP INTERNETWARE RECOMMENDATION data-driven model situational 

分 类 号：TP393.09[自动化与计算机技术—计算机应用技术] TQ174.758[自动化与计算机技术—计算机科学与技术]