research-article

Recommendation and Weaving of Reusable Mashup Model Patterns for Assisted Development

Authors:

Soudip Roy Chowdhury,

Florian Daniel,

Fabio CasatiAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 14, Issue 2-3

Article No.: 21, Pages 1 - 23

https://doi.org/10.1145/2663500

Published: 28 October 2014 Publication History

Abstract

With this article, we give an answer to one of the open problems of mashup development that users may face when operating a model-driven mashup tool, namely the lack of modeling expertise. Although commonly considered simple applications, mashups can also be complex software artifacts depending on the number and types of Web resources (the components) they integrate. Mashup tools have undoubtedly simplified mashup development, yet the problem is still generally nontrivial and requires intimate knowledge of the components provided by the mashup tool, its underlying mashup paradigm, and of how to apply such to the integration of the components. This knowledge is generally neither intuitive nor standardized across different mashup tools and the consequent lack of modeling expertise affects both skilled programmers and end-user programmers alike.

In this article, we show how to effectively assist the users of mashup tools with contextual, interactive recommendations of composition knowledge in the form of reusable mashup model patterns. We design and study three different recommendation algorithms and describe a pattern weaving approach for the one-click reuse of composition knowledge. We report on the implementation of three pattern recommender plugins for different mashup tools and demonstrate via user studies that recommending and weaving contextual mashup model patterns significantly reduces development times in all three cases.

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Index Terms

Recommendation and Weaving of Reusable Mashup Model Patterns for Assisted Development

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Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 14, Issue 2-3

Special Issue on Pricing and Incentives in Networks and Systems and Regular Papers

October 2014

287 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/2684804

Editors:
Munindar P. Singh
Department of Computer Science, North Carolina State University
,
Costas Courcoubetis
SUTD, Singapore
,
Roch Guérin
Washington University in St. Louis, USA
,
Patrick Loiseau
EURECOM, France
,
David Parkes
Harvard University, USA
,
Jean Walrand
University of California, Berkeley, USA
,
Adam Wierman
Caltech, USA

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Copyright © 2014 ACM.

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Association for Computing Machinery

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Publication History

Published: 28 October 2014

Accepted: 01 June 2014

Revised: 01 March 2014

Received: 01 October 2013

Published in TOIT Volume 14, Issue 2-3

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“Evaluation and enhancement of social, economic and emotional wellbeing of older adult” under the agreement no.14.Z50.31.0029
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Wang XXi MLi YPan XWu YDeng SYin J(2024)SEHGN: Semantic-Enhanced Heterogeneous Graph Network for Web API RecommendationIEEE Transactions on Services Computing10.1109/TSC.2024.341732317:5(2836-2849)Online publication date: Sep-2024
https://doi.org/10.1109/TSC.2024.3417323
Fan GChen SHe QWu HLi JXue XFeng Z(2024)Service Recommendations for Mashup Based on Generation ModelIEEE Transactions on Services Computing10.1109/TSC.2023.332951117:4(1820-1834)Online publication date: Jul-2024
https://doi.org/10.1109/TSC.2023.3329511
Hu X(2024)Biased Random Walk based Web API Recommendation in Heterogeneous Network2024 IEEE International Conference on Web Services (ICWS)10.1109/ICWS62655.2024.00038(172-177)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ICWS62655.2024.00038
钟卓(2023)Multi-Model Fusion and Multi-Task Learning Based on Knowledge Graph Embedding for Industrial Software Component RecommendationComputer Science and Application10.12677/CSA.2023.131226013:12(2613-2622)Online publication date: 2023
https://doi.org/10.12677/CSA.2023.1312260
Wang XXi MYin J(2023)Functional and Structural Fusion based Web API Recommendations in Heterogeneous Networks2023 IEEE International Conference on Web Services (ICWS)10.1109/ICWS60048.2023.00025(91-96)Online publication date: Jul-2023
https://doi.org/10.1109/ICWS60048.2023.00025
Wu HDuan YYue KZhang L(2022)Mashup-Oriented Web API Recommendation via Multi-Model Fusion and Multi-Task LearningIEEE Transactions on Services Computing10.1109/TSC.2021.309875615:6(3330-3343)Online publication date: 1-Nov-2022
https://doi.org/10.1109/TSC.2021.3098756
Almonte LGuerra ECantador Ide Lara J(2021)Recommender systems in model-driven engineeringSoftware and Systems Modeling10.1007/s10270-021-00905-xOnline publication date: 26-Jul-2021
https://doi.org/10.1007/s10270-021-00905-x
Kasuya SZhou XTago KNishimura SJin Q(2020)Cyber-Enabled Well-Being Oriented Daily Living Support Based on Personal Data AnalysisIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2017.27639668:2(493-502)Online publication date: 1-Apr-2020
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Zhou ZCheng ZZhang LGaaloul WNing K(2018)Scientific Workflow Clustering and Recommendation Leveraging Layer Hierarchical AnalysisIEEE Transactions on Services Computing10.1109/TSC.2016.254280511:1(169-183)Online publication date: 1-Jan-2018
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