TY - GEN
T1 - Using sequential pattern mining and interactive recommendation to assist pipe-like mashup development
AU - Xinyi, Liu
AU - Hailong, Sun
AU - Hanxiong, Wu
AU - Richong, Zhang
AU - Xudong, Liu
PY - 2014
Y1 - 2014
N2 - Mashups represent a typical type of service oriented applications targeting end-user development. However, due to lack of development expertise, end-users usually find it hard to build a mashup. Therefore, it is of paramount importance to provide effective assistance to achieve efficient mashup development. In this work, we aim at leveraging the expertise that can be mined from voluminous mashups on Internet to recommend appropriate mashup modules and their composition patterns to facilitate pipe-like mashup development. First, we crawl all the mashups available in Yahoo!Pipes and extract the meta-data of each mashup from original JSON data. Second, we use GSP (Generalized Sequential Pattern) algorithm to mine the frequent composition pattern of mashup modules, and design an interactive recommendation algorithm to assist mashup development. Third, we implement a system prototype based on the proposed method and evaluate its effectiveness with 848 Yahoo! mashups through cross-validation.
AB - Mashups represent a typical type of service oriented applications targeting end-user development. However, due to lack of development expertise, end-users usually find it hard to build a mashup. Therefore, it is of paramount importance to provide effective assistance to achieve efficient mashup development. In this work, we aim at leveraging the expertise that can be mined from voluminous mashups on Internet to recommend appropriate mashup modules and their composition patterns to facilitate pipe-like mashup development. First, we crawl all the mashups available in Yahoo!Pipes and extract the meta-data of each mashup from original JSON data. Second, we use GSP (Generalized Sequential Pattern) algorithm to mine the frequent composition pattern of mashup modules, and design an interactive recommendation algorithm to assist mashup development. Third, we implement a system prototype based on the proposed method and evaluate its effectiveness with 848 Yahoo! mashups through cross-validation.
KW - end-user programming
KW - interactive recommendation
KW - mashup
KW - sequential pattern mining
UR - https://www.scopus.com/pages/publications/84903624881
U2 - 10.1109/SOSE.2014.24
DO - 10.1109/SOSE.2014.24
M3 - 会议稿件
AN - SCOPUS:84903624881
SN - 9781479925049
T3 - Proceedings - IEEE 8th International Symposium on Service Oriented System Engineering, SOSE 2014
SP - 173
EP - 180
BT - Proceedings - IEEE 8th International Symposium on Service Oriented System Engineering, SOSE 2014
PB - IEEE Computer Society
T2 - 8th IEEE International Symposium on Service Oriented System Engineering, SOSE 2014
Y2 - 7 April 2014 through 11 April 2014
ER -