4.7 Article

Model-Based Automated Navigation and Composition of Complex Service Mashups

Journal

IEEE TRANSACTIONS ON SERVICES COMPUTING
Volume 8, Issue 3, Pages 494-506

Publisher

IEEE COMPUTER SOC
DOI: 10.1109/TSC.2014.2347293

Keywords

Service mashups; recommendation; layered graph

Funding

  1. National Basic Research Program (973) of China [2014CB347701]
  2. High-Tech Research and Development Program of China [2013AA01A605]
  3. Natural Science Foundation of China [61121063, 61370020, 61222203, 61361120097]
  4. Star-Track Young Scholar Program of Microsoft Research Asia

Ask authors/readers for more resources

Service computing promotes a large number of web-delivered services, including web services, APIs and data feeds. Composing data, functionalities and even UI from these web-delivered services into a single web application, usually called service mashup, becomes a popular web development paradigm. The web-delivered services can be modeled as mashup components, while the development of mashup actually yields a set of inter-connected mashup components. The growing popularity of mashup components enriches functionality and user experiences, while the possible connections among components are complex and difficult to mashup developers, who might be non-professional programmers or even end-users, as actions over one component may have potential impacts on another. This paper proposes a novel approach for recommending developers in terms of navigation and completion of mashup components with a large-scale components repository. From data-driven perspective, we model the relationships between mashup components by a generic layered-graph model. Developers are allowed to select some initial components as starting point, while a graph-based algorithm recommends how to navigate to potentially relevant mashup components and complete the relevant mashup application. We experimentally demonstrate the efficiency and effectiveness of our approach for rapid mashup construction.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available