@article{WOS:000361923100018,
abstract = {Support for component discovery has been identified as a key challenge
in various forms of composite application development. In this paper, we
describe a general method for component recommendation based on
structural similarity of compositions. The method dynamically ranks and
recommends components as a composition is incrementally developed.
Recommendations are based on structural comparison of the partial
composition begin developed with a database of previously completed
compositions. Using this method, we define a probabilistic graph edit
distance algorithm for component recommendation. We evaluate the
accuracy, catalog coverage and response time of the presented algorithm
and compare it to a neighborhood-based collaborative filtering approach
and two simple statistical algorithms. The evaluation is performed on a
Yahoo Pipes dataset and a synthetic dataset that models more complex
composite applications. The results show that the proposed algorithm is
competitive with the collaborative filtering algorithm in accuracy and
outperforms it significantly in coverage. The results on the synthetic
dataset suggest that the presented approach can be applied successfully
to other composition environments where there is regularity in how
components are connected. (C) 2015 Elsevier Ltd. All rights reserved.},
address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND},
author = {Budiselic, Ivan and Vladimir, Klemo and Srbljic, Sinisa},
doi = {10.1016/j.eswa.2015.07.012},
issn = {0957-4174},
journal = {EXPERT SYSTEMS WITH APPLICATIONS},
keywords = {Composite applications; Development tools; Recomme},
month = dec,
number = {22},
pages = {8573--8587},
publisher = {PERGAMON-ELSEVIER SCIENCE LTD},
title = {{Component recommendation for composite application development}},
type = {Article},
volume = {42},
year = {2015}
}

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