research-article

Simplifying mashup component selection with a combined similarity- and social-based technique

Authors:

Boris Tapia,

Romina Torres,

Hernán AstudilloAuthors Info & Claims

Mashups '11: Proceedings of the 5th International Workshop on Web APIs and Service Mashups

Article No.: 8, Pages 1 - 8

https://doi.org/10.1145/2076006.2076015

Published: 14 September 2011 Publication History

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Abstract

Web mashups are becoming the main approach to build Web applications. Current approaches to enable component selection include description-based techniques and socially generated metadata. The explosive growth of APIs makes increasingly harder selecting appropriate components for each mashup. Unfortunately, description-based techniques rely heavily on the quality of authors' information, and social-based approaches suffer problems like "cold-start" and "preferential attachment". This article proposes (1) two new measures of socially ranked fitness of candidate components, (2) an API functional taxonomy using Formal Concept Analysis based on descriptions, and (3) a combined approach that improves description-based techniques with these social ranking measures. We use social rankings based on past (co-)utilization of APIs: WAR (Web API Rank) measures API utilization over time, and CAR (Co-utilization API Rank) measures its co-utilization with other APIs. The measures and the combined approach are illustrated with a case study using the well-known Web APIs catalog ProgrammableWeb. A prototype tool allows iterative discovery of APIs and assists the mashup creation process.

References

[1]

C. Carpineto and G. Romano. Exploiting the potential of concept lattices for information retrieval with credo. J. UCS, pages 985--1013, 2004.

Google Scholar

[2]

H. Elmeleegy, A. Ivan, R. Akkiraju, and R. Goodwin. Mashup advisor: A recommendation tool for mashup development. In Web Services, 2008. ICWS '08. IEEE International Conference on, pages 337--344, sept. 2008.

Digital Library

Google Scholar

[3]

K. Goarany, G. Kulczycki, and M. B. Blake. Mining social tags to predict mashup patterns. In Proceedings of the 2nd international workshop on Search and mining user-generated contents, SMUC '10, pages 71--78, New York, NY, USA, 2010. ACM.

Digital Library

Google Scholar

[4]

O. Greenshpan, T. Milo, and N. Polyzotis. Autocompletion for mashups. Proc. VLDB Endow., 2:538--549, August 2009.

Digital Library

Google Scholar

[5]

C. Lindig. Fast concept analysis. In Working with Conceptual Structures - Contributions to ICCS 2000, pages 152--161. Shaker Verlag, 2000.

Google Scholar

[6]

G. R. G. Michael Weiss. Modeling the mashup ecosystem: Structure and growth. In R & D Management, pages 40--49, 2010.

Google Scholar

[7]

M. F. Porter. An algorithm for suffix stripping. Program, 14(3):130--137, 1980.

Crossref

Google Scholar

[8]

A. Ranabahu, M. Nagarajan, A. P. Sheth, and K. Verma. A faceted classification based approach to search and rank web apis. In Proceedings of the 2008 IEEE International Conference on Web Services, pages 177--184, Washington, DC, USA, 2008. IEEE Computer Society.

Digital Library

Google Scholar

[9]

C. Roth and P. Bourgine. Lattice-based dynamic and overlapping taxonomies: The case of epistemic communities. Scientometrics, 69(2):429--447, 2006.

Crossref

Google Scholar

[10]

J. W. Shuli Yu. Innovation in the programmable web: Characterizing the mashup ecosystem. In ICSOC 2008, LNCS 5472, pages 136--147. Springer-Verlag, 2009.

Digital Library

Google Scholar

[11]

R. Torres, B. Tapia, and H. Astudillo. Improving web api discovery by leveraging social information. to appear in the proceedings of the 9th IEEE International Conference on Web Services, 2011.

Digital Library

Google Scholar

[12]

M. Weiss and S. Sari. Evolution of the mashup ecosystem by copying. In Proceedings of the 3rd and 4th International Workshop on Web APIs and Services Mashups, Mashups '09/'10, pages 11:1--11:7, New York, NY, USA, 2010. ACM.

Digital Library

Google Scholar

[13]

Q. Wu, A. Iyengar, R. Subramanian, I. Rouvellou, I. Silva-Lepe, and T. Mikalsen. Combining quality of service and social information for ranking services. In Proceedings of the 7th International Joint Conference on Service-Oriented Computing, ICSOC-ServiceWave '09, pages 561--575, Berlin, Heidelberg, 2009. Springer-Verlag.

Digital Library

Google Scholar

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Kang GLiang BLiu JWen YXiao YNie H(2024)Web API Recommendation via Leveraging Content and Network SemanticsIEEE Transactions on Network and Service Management10.1109/TNSM.2024.342223321:6(5977-5991)Online publication date: Dec-2024
https://doi.org/10.1109/TNSM.2024.3422233
Kang GLiu JXiao YCao BXu YCao M(2021)Neural and Attentional Factorization Machine-Based Web API Recommendation for Mashup DevelopmentIEEE Transactions on Network and Service Management10.1109/TNSM.2021.312502818:4(4183-4196)Online publication date: Dec-2021
https://doi.org/10.1109/TNSM.2021.3125028
Adeleye OYu JYongchareon Yongchareon SHan YSheng Q(2020)Complex Network-Based Web Service for Web-API DiscoveryProceedings of the Australasian Computer Science Week Multiconference10.1145/3373017.3373035(1-10)Online publication date: 4-Feb-2020
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Index Terms

Simplifying mashup component selection with a combined similarity- and social-based technique
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Web-based interaction
2. Information systems

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Published In

Mashups '11: Proceedings of the 5th International Workshop on Web APIs and Service Mashups

September 2011

81 pages

ISBN:9781450308236

DOI:10.1145/2076006

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 September 2011

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UTFSM DGIP
FONDEF
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Mashups '11

Mashups '11: 5th International Workshop on Web APIs and Services Mashups

September 14, 2011

Lugano, Switzerland

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Cited By

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Kang GLiang BLiu JWen YXiao YNie H(2024)Web API Recommendation via Leveraging Content and Network SemanticsIEEE Transactions on Network and Service Management10.1109/TNSM.2024.342223321:6(5977-5991)Online publication date: Dec-2024
https://doi.org/10.1109/TNSM.2024.3422233
Kang GLiu JXiao YCao BXu YCao M(2021)Neural and Attentional Factorization Machine-Based Web API Recommendation for Mashup DevelopmentIEEE Transactions on Network and Service Management10.1109/TNSM.2021.312502818:4(4183-4196)Online publication date: Dec-2021
https://doi.org/10.1109/TNSM.2021.3125028
Adeleye OYu JYongchareon Yongchareon SHan YSheng Q(2020)Complex Network-Based Web Service for Web-API DiscoveryProceedings of the Australasian Computer Science Week Multiconference10.1145/3373017.3373035(1-10)Online publication date: 4-Feb-2020
https://dl.acm.org/doi/10.1145/3373017.3373035
Li ZLiu XWang THe WXia C(2020)GHSCN: A Graph Neural Network-based API Popularity Prediction Method in Service EcosystemIEEE Access10.1109/ACCESS.2020.3011439(1-1)Online publication date: 2020
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Liang TChen LWu JXu GWu Z(2019)SMS: A Framework for Service Discovery by Incorporating Social Media InformationIEEE Transactions on Services Computing10.1109/TSC.2016.263152112:3(384-397)Online publication date: 1-May-2019
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Xiao YLiu JHu RCao BCao Y(2019)DINRec: Deep Interest Network Based API Recommendation Approach for Mashup CreationWeb Information Systems Engineering – WISE 201910.1007/978-3-030-34223-4_12(179-193)Online publication date: 29-Oct-2019
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Caruccio LDeufemia VEsposito SPolese G(2019)Combining Collaborative Filtering and Semantic-Based Techniques to Recommend Components for Mashup DesignComputational Intelligence for Semantic Knowledge Management10.1007/978-3-030-23760-8_2(25-37)Online publication date: 12-Jul-2019
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