From the Service-Oriented Architecture to the Web API Economy

As Web APIs become the backbone of Web, cloud, mobile, and machine learning applications, the services computing community will need to expand and embrace opportunities and challenges from these domains.

[1]  Alessandra Gorla,et al.  Checking app behavior against app descriptions , 2014, ICSE.

[2]  Jia Zhang,et al.  Network Analysis of Scientific Workflows: A Gateway to Reuse , 2010, Computer.

[3]  CARLOS A. GOMEZ-URIBE,et al.  The Netflix Recommender System , 2015, ACM Trans. Manag. Inf. Syst..

[4]  Lei Shu,et al.  IEEE Access Special Session Editorial: Big Data Services and Computational Intelligence for Industrial Systems , 2015, IEEE Access.

[5]  Zibin Zheng,et al.  Reputation Measurement and Malicious Feedback Rating Prevention in Web Service Recommendation Systems , 2015, IEEE Transactions on Services Computing.

[6]  Jason Nieh,et al.  A measurement study of google play , 2014, SIGMETRICS '14.

[7]  Xuanzhe Liu,et al.  Data-Driven Composition for Service-Oriented Situational Web Applications , 2015, IEEE Transactions on Services Computing.

[8]  Cheng Wu,et al.  Category-Aware API Clustering and Distributed Recommendation for Automatic Mashup Creation , 2015, IEEE Transactions on Services Computing.

[9]  Schahram Dustdar,et al.  Service Provisioning in Content-Centric Networking: Challenges, Opportunities, and Promising Directions , 2016, IEEE Internet Computing.

[10]  Liana L. Fong,et al.  Faster and Cheaper: Parallelizing Large-Scale Matrix Factorization on GPUs , 2016, HPDC.

[11]  Jia Zhang,et al.  Time-Aware Service Recommendation for Mashup Creation , 2015, IEEE Transactions on Services Computing.

[12]  Vijay K. Naik,et al.  Riding and thriving on the API hype cycle , 2016, Commun. ACM.

[13]  MengChu Zhou,et al.  Business and Scientific Workflows: A Web Service-Oriented Approach , 2013 .

[14]  Chao Liu,et al.  Distributed nonnegative matrix factorization for web-scale dyadic data analysis on mapreduce , 2010, WWW '10.