DocumentCode :
249394
Title :
Three-Level Views of the Web Service Network: An Empirical Study Based on ProgrammableWeb
Author :
Saixia Lyu ; Jianxun Liu ; Mingdong Tang ; Guosheng Kang ; Buqing Cao ; Yucong Duan
Author_Institution :
Sch. of Comput. Sci. & Eng., Hunan Univ. of Sci. & Technol., Xiangtan, China
fYear :
2014
fDate :
June 27 2014-July 2 2014
Firstpage :
374
Lastpage :
381
Abstract :
With the increase of the amount of open services on the Web, combining these Web services to develop interesting and innovative applications has attracted extensive attention. Web services, service-based applications, service providers and consumers on the Internet have contributed to the emergence of the so-called "Web service ecosystem". Accurate understanding and systematic analysis of the Web service ecosystem is of paramount value. This work takes the service data collected from ProgrammableWeb.com as an empirical dataset and focuses on analyzing the Web service composition network. We propose a three-level view model for visualization of the Web service network, i.e., the Web API graph, the tag graph, and the domain graph. We firstly present the models and the constructing approaches of the graphs, and then analyze their structural characteristics. This work is not only helpful for understanding the Web service ecosystem, but also can provide support to service consumers for discovering appropriate services in developing interesting and innovative applications.
Keywords :
Web services; application program interfaces; graph theory; ProgrammableWeb; Web API graph; Web service composition network analysis; Web service ecosystem; Web service network visualization; domain graph; empirical dataset; graph constructing approach; graph modelling; open services; service consumers; service discovery; service providers; service-based applications; structural characteristics analysis; tag graph; three-level view model; Big data; Mashup; ProgrammableWeb; Web API; Web service network; network analysis; tag;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Big Data (BigData Congress), 2014 IEEE International Congress on
Conference_Location :
Anchorage, AK
Print_ISBN :
978-1-4799-5056-0
Type :
conf
DOI :
10.1109/BigData.Congress.2014.62
Filename :
6906805
Link To Document :
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