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Clustering and Association Rules for Web Service Discovery and Recommendation: A Systematic Literature Review

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Abstract

The purpose of this study is to identify, summarize, and systematically compare various clustering and association rule techniques for web service discovery and recommendation, identify the most common data sets used in the extant literature, and highlight current trends and future research directions. Following the methodology of Kitchenham and Charters (Guidelines for performing Systematic Literature reviews in Software Engineering, 2007) for a systematic literature review (SLR), a set of research questions are designed. Six digital databases are searched. A total of 4581 papers were initially retrieved, and a rigorous two-stage scanning process resulted in 66 relevant papers. Based on the selection criteria and data extraction, 57 final studies were selected. These papers are summarized and compared, and the relevant information is extracted to answer the research questions. The synthesis resulted in knowledge of currently proposed methods for web service discovery and recommendation based on clustering and association rule techniques. Furthermore, it identifies algorithms, similarity measures, evaluation metrics, and data sets. Also identifies challenges, research gaps, trends, and future directions. We propose a classification of web service discovery and recommendation methods and map the 57 final selected papers into these classes. This review will help researchers to understand the current state-of-the-art in clustering and association rules techniques for web service discovery and recommendation, and also recognize trends and future directions for improvement. Future studies should broaden the basis of discovery and recommendation by including various types of web service descriptions including plain text that are currently used in web APIs. An opportunity for improvement by utilizing modern techniques based on big data analytics and social network analysis.

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Notes

  1. 7 http://db.cs.washington.edu/projects/woogle.html.

  2. https://en.wikipedia.org.

  3. https://www.programmableweb.com/.

  4. http://www.seekda.com.

  5. https://www.programmableweb.com.

  6. http://www.webservicex.net.

  7. http://www.webservicelist.com.

  8. http://www.xmethods.com/.

References

  1. Kitchenham B, Charters S, Guidelines for performing Systematic Literature reviews in Software Engineering, 2007.

  2. Obidallah WJ, Ruhi U, Raahemi B, Current landscape of web service discovery: a typology based on five characteristics, In: 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI), 2016. pp. 678–683.

  3. Crasso M, Zunino A, Campo M. Easy web service discovery: a query-by-example approach. Sci Comput Program. 2008;71(2):144–64.

    Article  MathSciNet  MATH  Google Scholar 

  4. Ngane LD, Goh A, Tru CH, A survey of web service discovery systems. In: Alkhatib GI, Rine DC, editors. IGI Global; 2008.

  5. M. Klusch, Service discovery, Encyclopedia of Social Networks Min. R. Alhajj J. Rokne, editors. New York:Springer; 2014.

  6. Garofalakis A, Panagis J, Sakkopoulos Y, Tsakalidis E, Web service discovery mechanisms: looking for a needle in a haystack? In: International workshop on web engineering; 2004, vol. 5, no. 3. pp. 265–290.

  7. Zhang M, Liu X, Zhang R, Sun H A web service recommendation approach based on QoS prediction using fuzzy clustering. In: 2012 IEEE ninth international conference on services computing; 2012, pp. 138–145.

  8. Le DN, Soong Goh AE. A survey of web service discovery systems. Int J Inf Technol Web Eng. 2007;2(2):65–80.

    Article  Google Scholar 

  9. Newcomer E, Understanding Web Services: XML, Wsdl, Soap, and UDDI. Addison-Wesley Professional; 2002.

  10. Obidallah WJ, Raahemi B, A survey on web service discovery approaches. In: Proceedings of the second international conference on internet of things and cloud computing—ICC ’17; 2017, pp. 1–8.

  11. Liu W, Wong W. Web service clustering using text mining techniques. Int. J. Agent-Oriented Softw. Eng. 2009;3(1):6.

    Article  Google Scholar 

  12. Elgazzar K, Hassan AE, Martin P, Clustering WSDL documents to bootstrap the discovery of Web services. In: ICWS 2010–2010 IEEE 8th international conference on web services; 2010, pp. 147–154.

  13. Kumara BTGS, Paik I, Siriweera THAS, Koswatte KRC, Cluster-based web service recommendation. In: 2016 IEEE international conference on services computing (SCC); 2016, pp. 348–355.

  14. He P, Zhu J, Xu J, Lyu MR, A hierarchical matrix factorization approach for location-based web service QoS prediction. In: 2014 IEEE 8th international symposium on service oriented system engineering; 2014, pp. 290–295.

  15. Chen X, Zheng Z, Yu Q, Lyu MR. Web service recommendation via exploiting location and QoS information. IEEE Trans Parallel Distrib Syst. 2014;25(7):1913–24.

    Article  Google Scholar 

  16. Zhu J, Kang Y, Zheng Z, Lyu MR, A clustering-based QoS prediction approach for web service recommendation. In: 2012 IEEE 15th international symposium on object/component/service-oriented real-time distributed computing workshops; 2012, pp. 93–98.

  17. Kitchenham B, Procedures for performing systematic reviews, Keele: Keele University; 2004, vol. 33, no. TR/SE-0401, p. 28.

  18. Mavergames C, Covidence (Systematic Review Software); 2013. https://www.covidence.org/. Accessed 07 Dec 2016.

  19. Kitchenham B, Pearl Brereton O, Budgen D, Turner M, Bailey J, Linkman S. Systematic literature reviews in software engineering—a systematic literature review. Inf Softw Technol. 2009;51(1):7–15.

    Article  Google Scholar 

  20. Dybå T, Dingsøyr T. Empirical studies of agile software development: a systematic review. Inf Softw Technol. 2008;50(9–10):833–59.

    Article  Google Scholar 

  21. Cong Z, Fernandez A, Billhardt H, Lujak M. Service discovery acceleration with hierarchical clustering. Inf Syst Front. 2015;17(4):799–808.

    Article  Google Scholar 

  22. Wu J, Chen L, Zheng Z, Lyu MRMR, Wu Z. Clustering Web services to facilitate service discovery. Knowl Inf Syst. 2014;38(1):207–29.

    Article  Google Scholar 

  23. Tian G, et al. Leveraging auxiliary knowledge for web service clustering. Chin J Electron. 2016;25(5):858–65.

    Article  Google Scholar 

  24. Tian G, Sun C, He K-Q, Ji X-M. Transferring auxiliary knowledge to enhance heterogeneous web service clustering. Int J High Perform Comput Netw. 2016;9(1/2):160.

    Article  Google Scholar 

  25. Sowmya Kamath S, Ananthanarayana VS, Similarity analysis of service descriptions for efficient Web service discovery. In: 2014 International Conference on Data Science and Advanced Analytics (DSAA), 2014, pp. 142–148.

  26. Yu Q, Wang H, Chen L, Learning sparse functional factors for large-scale service clustering. In: 2015 IEEE international conference on web services; 2015, pp. 201–208.

  27. Kumara BTGS, Paik I, Lee G, Ontology learning method for Web services clustering. In: Proceedings—2012 7th international conference on computing and convergence technology (ICCIT, ICEI and ICACT), ICCCT 2012; 2012, pp. 705–710.

  28. Upadhyaya B, Khomh F, Zou Y, Lau A, Ng J, A concept analysis approach for guiding users in service discovery. In: 2012 Fifth IEEE international conference on service-oriented computing and applications (SOCA); 2012, pp. 1–8.

  29. Ma J, Zhang Y, He J, Web services discovery based on latent semantic approach. In: 2008 IEEE international conference on web services; 2008, pp. 740–747.

  30. Aznag M, Quafafou M, Jarir Z, Leveraging formal concept analysis with topic correlation for service clustering and discovery. In: 2014 IEEE international conference on web services; 2014, pp. 153–160.

  31. Ma J, Zhang Y, He J, Efficiently finding web services using a clustering semantic approach. In: Proceedings of the 2008 international workshop on Context enabled source and service selection, integration and adaptation organized with the 17th International World Wide Web Conference (WWW 2008)—CSSSIA ’08; 2008, vol. 292, pp. 1–8.

  32. Wen T, Sheng G, Li Y, Guo Q, Research on web service discovery with semantics and clustering. In: 2011 6th IEEE joint international information technology and artificial intelligence conference; 2011, vol. 1, pp. 62–67.

  33. Elgazzar K, Hassanein HS, Martin P. DaaS: cloud-based mobile web service discovery. Pervasive Mob Comput. 2014;13:67–84.

    Article  Google Scholar 

  34. Kumara BTGS, Paik I, Ohashi H, Chen W, Koswatte KRC, Context aware post-filtering for web service clustering. In: 2014 IEEE international conference on services computing; 2014, pp. 440–447.

  35. Kumara BTGSBTGS, Paik II, Chen WW, Ryu KHKH. Web service clustering using a hybrid term-similarity measure with ontology learning. Int J Web Serv Res. 2014;11(2):24–45.

    Article  Google Scholar 

  36. Kumara BTGS, Paik I, Koswatte KRC, Chen W, Ontology learning with complex data type for Web service clustering. In: IEEE SSCI 2014–2014 IEEE symposium series on computational intelligence—CIDM 2014: 2014 IEEE symposium on computational intelligence and data mining, proceedings; 2014, pp. 129–136.

  37. Dasgupta S, Bhat S, Lee Y, Taxonomic clustering and query matching for efficient service discovery. In: 2011 IEEE international conference on web services; 2011, pp. 363–370.

  38. Liang T, Chen L, Ying H, Wu J, Co-clustering WSDL documents to bootstrap service discovery. In: 2014 IEEE 7th international conference on service-oriented computing and applications; 2014, pp. 215–222.

  39. Liu J, Liu F, Li X, He K, Ma Y, Wang J. Web service clustering using relational database approach. Int J Softw Eng Knowl Eng. 2015;25(08):1365–93.

    Article  Google Scholar 

  40. Platzer C, Rosenberg F, Dustdar S. Web service clustering using multidimensional angles as proximity measures. ACM Trans Internet Technol. 2009;9(3):1–26.

    Article  Google Scholar 

  41. Rodriguez JM, Zunino A, Mateos C, Segura FO, Rodriguez E, Improving REST service discovery with unsupervised learning techniques. In: 2015 ninth international conference on complex, intelligent, and software intensive systems; 2015, pp. 97–104.

  42. Nagy A, Oprisa C, Salomie I, Pop CB, Chifu VR, Dinsoreanu M, Particle swarm optimization for clustering semantic web services. In: 2011 10th international symposium on parallel and distributed computing; 2011, pp. 170–177.

  43. De Jesus Silva L, Claro DB, Lopes DCP. Semantic-based clustering of web services. J Web Eng. 2015;14(3–4):325–45.

    Google Scholar 

  44. Chen F, Li M, Wu H, Xie L. Web service discovery among large service pools utilising semantic similarity and clustering. Enterp Inf Syst. 2017;11(3):452–69.

    Article  Google Scholar 

  45. Gao H, Wang S, Sun L, Nian F, Hierarchical clustering based web service discovery. In: IFIP advances in information and communication technology, vol. 426; 2014, pp. 281–291.

  46. Chen LL, Yang GG, Zhu WW, Zhang YY, Yang ZZ. Clustering facilitated web services discovery model based on supervised term weighting and adaptive metric learning. Int J Web Eng Technol. 2013;8(1):58.

    Article  Google Scholar 

  47. Chen F, Yuan S, Mu B, User-QoS-Based web service clustering for QoS prediction. In: 2015 IEEE international conference on web services; 2015, pp. 583–590.

  48. Liu R, Xu X, Wang Z, Service recommendation using customer similarity and service usage pattern. In: 2015 IEEE international conference on web services; 2015, pp. 408–415.

  49. Yu C, Huang L. CluCF: a clustering CF algorithm to address data sparsity problem. Serv Oriented Comput Appl. 2017;11(1):33–45.

    Article  Google Scholar 

  50. Su K, Xiao B, Liu B, Zhang H, Zhang Z. TAP: a personalized trust-aware QoS prediction approach for web service recommendation. Knowl-Based Syst. 2016;115:55–65.

    Article  Google Scholar 

  51. Chen L, Wu J, Zheng Z, Lyu MRMR, Wu Z. Modeling and exploiting tag relevance for Web service mining. Knowl Inf Syst. 2014;39(1):153–73.

    Article  Google Scholar 

  52. He P, Zhu J, Zheng Z, Xu J, Lyu MR, Location-based hierarchical matrix factorization for web service recommendation. In: 2014 IEEE international conference on web services; 2014, pp. 297–304.

  53. Lei Y, Jiantao Z, Junxing Z, Fengqi W, Juan W, Time-aware semantic web service recommendation. In: 2015 IEEE international conference on services computing; 2015, no. PG-664-671, pp. 664–671.

  54. Yu Q. QoS-aware service selection via collaborative QoS evaluation. World Wide Web. 2014;17(1):33–57.

    Article  Google Scholar 

  55. Chen L, Yu Q, Yu PS, Wu J, WS-HFS: a heterogeneous feature selection framework for web services mining. In: 2015 IEEE international conference on web services; 2015, pp. 193–200.

  56. Bianchini D, Garza P, Quintarelli E. Characterization and search of web services through intensional knowledge. J Intell Inf Syst. 2016;47(3):375–401.

    Article  Google Scholar 

  57. Rong W, Liu K, Liang L, Rong W, Liu K, Liang L, Personalized web service ranking via user group combining association rule. In: 2009 IEEE international conference on web services; 2009, pp. 445–452.

  58. Rong W, Peng B, Ouyang Y, Liu K, Xiong Z. Collaborative personal profiling for web service ranking and recommendation. Inf Syst Front. 2015;17(6):1265–82.

    Article  Google Scholar 

  59. Lin M, Cheung DW, Automatic tagging web services using machine learning techniques. In: 2014 IEEE/WIC/ACM international joint conferences on web intelligence (WI) and intelligent agent technologies (IAT); 2014, vol. 2, pp. 258–265.

  60. Vadivelou G, Ilavarasan E. Collaborative filtering based hybrid approach for web service recommendations. Res J Appl Sci Eng Technol. 2014;8(5):615–22.

    Article  Google Scholar 

  61. Gong M, Xu Z, Xu L, Li Y, Chen L, Recommending Web service based on user relationships and preferences. In: 2013 IEEE 20th international conference on web services; 2013, pp. 380–386.

  62. Paliwal AV, Shafiq B, Vaidya J, Xiong H, Adam N. Semantics-based automated service discovery. IEEE Trans Serv Comput. 2012;5(2):260–75.

    Article  Google Scholar 

  63. Miller GA. WordNet: a lexical database for English. Commun ACM. 1995;38(11):39–41.

    Article  Google Scholar 

  64. Cilibrasi RL, Vitanyi PMB. The google similarity distance. IEEE Trans Knowl Data Eng. 2007;19(3):370–83.

    Article  Google Scholar 

  65. Levenshtein VI. Binary codes capable of correcting deletions, insertions, and reversals. Sov Phys Dokl. 1966;10(8):707–10.

    MathSciNet  Google Scholar 

  66. Heß A, Kushmerick N, Machine learning for annotating semantic web services. In: AAAI spring symposium on semantic web services; 2004.

  67. Wohlin C, Runeson P, Höst M, Ohlsson MC, Regnell B, Wesslén A. Experimentation in software engineering, vol. 15. Berlin Heidelberg: Springer; 2012.

    Book  MATH  Google Scholar 

  68. Kumara BTGS, Paik I, Siriweera THAS, Koswatte KRC, QoS aware service clustering to bootstrap the web service selection. In: 2017 IEEE international conference on services computing (SCC); 2017, pp. 233–240.

  69. Li S, Wen J, Luo F, Gao M, Zeng J, Dong ZY. A new QoS-aware web service recommendation system based on contextual feature recognition at server-side. IEEE Trans Netw Serv Manag. 2017;14(2):332–42.

    Article  Google Scholar 

  70. Cao B et al., Mashup service clustering based on an integration of service content and network via exploiting a two-level topic model. In: Proceedings—2016 IEEE international conference on web services, ICWS 2016; 2016, pp. 212–219.

  71. Cao B, Liu X, Liu J, Tang M. Domain-aware Mashup service clustering based on LDA topic model from multiple data sources. Inf Softw Technol. 2017;90:40–54.

    Article  Google Scholar 

  72. Slaimi F, Sellami S, Boucelma O, Ben Hassine A, Leveraging track relationships for web service recommendation. In: 2016 IEEE 13th international conference on e-business engineering (ICEBE); 2016, pp. 220–225.

  73. Cheng B, Zhao S, Li C, Chen J. A web services discovery approach based on mining underlying interface semantics. IEEE Trans Knowl Data Eng. 2017;29(5):950–62.

    Article  Google Scholar 

  74. Rupasingha RAHM, Paik I, Kumara BTGS, Improving web service clustering through a novel ontology generation method by domain specificity. In: 2017 IEEE international conference on web services (ICWS); 2017. pp. 744–751.

  75. Shi M, Liu J, Zhou D, Tang M, Cao B, WE-LDA: a word embeddings augmented LDA model for web services clustering. In: 2017 IEEE international conference on web services; 2017. pp. 9–16.

  76. Shi W, Liu X, Yu Q, Correlation-aware multi-label active learning for web service tag recommendation. In: 2017 IEEE International Conference on Web Services (ICWS); 2017. pp. 229–236.

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Author information

Authors and Affiliations

  1. Knowledge Discovery and Data Mining Lab, University of Ottawa, Ottawa, Canada

    Waeal J. Obidallah & Bijan Raahemi

  2. Telfer School of Management, University of Ottawa, Ottawa, Canada

    Bijan Raahemi & Umar Ruhi

  3. College of Computer and Information Sciences, Imam Muhammad Ibn Saud Islamic University, Riyadh, Saudi Arabia

    Waeal J. Obidallah

Authors
  1. Waeal J. Obidallah
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Appendices

Appendix A: The Search Queries for Each Selected Digital Library

ACM Digital Library: Does not support abstract, title, and keywords search.

“query”: {(“Web Service*” “Web Service* Discovery” “Web Service* Recommend*” “Clustering” “Association Rule*”)}

“filter”: {“publicationYear”:{“gte”:2006}},

{owners.owner = HOSTED}

IEEE Digital Library: Does not support abstract, title, and keywords search.

“Web Service*” OR “Web Service* Discovery” OR “Web Service* Recommend*”) AND (“Clustering” OR “Association Rule*”) and refined by Year: 2006-2016

SpringerLink: Does not support abstract, title, and keywords search.

‘(“Web Service*” OR “Web Service* Discovery” OR “Web Service* Recommend*”) AND (“Clustering” OR “Association Rule*”)’

within English Computer Science Article 2006 2016

ISI Web of Science: support abstract, title, and keywords search.

TOPIC: ((“Web Service*” OR “Web Service* Discovery” OR “Web Service* Recommend*”) AND (“Clustering” OR “Association Rule*”))

Timespan = 2006–2016

ScienceDirect: support abstract, title, and keywords search.

pub-date > 2006 and TITLE-ABS-KEY (“Web Service?” OR “Web Service? Discovery” OR “Web Service? Recommend*”) AND TITLE-ABS-KEY (“Clustering” OR “Association Rule?”)

Scopus: support abstract, title, and keywords search.

TITLE-ABS-KEY ((“Web Service*” OR “Web Service* Discovery” OR “Web Service* Recommend*”) AND (“Clustering” OR “Association Rule*”)) AND PUBYEAR > 2006 AND (LIMIT-TO (LANGUAGE, “English”)) AND (EXCLUDE (PUBYEAR, 2017))

Appendix B: List of Final Selected Papers

ID

Title

Venues

Year

Citation

Quality Score

[FS1]

TAP: A personalized trust-aware QoS prediction approach for web service recommendation [50]

KNOSYS (J)

2016

0

8.5

[FS2]

Clustering web services to facilitate service discovery [22]

KAIS (J)

2014

21

8.5

[FS3]

Modeling and exploiting tag relevance for web service mining [51]

KAIS (J)

2014

9

8

[FS4]

Web Service Recommendation via Exploiting Location and QoS Information [15]

TPDS (J)

2014

26

8

[FS5]

Web service discovery among large service pools utilizing semantic similarity and clustering [44]

EIS (J)

2015

1

8

[FS6]

Characterization and search of web services through intensional knowledge [56]

JIIS (J)

2016

1

8

[FS7]

Automatic Tagging Web Services Using Machine Learning Techniques [59]

WI-IAT (C)

2014

4

7.5

[FS8]

Leveraging Formal Concept Analysis with Topic Correlation for Service Clustering and Discovery [30]

ICWS(C)

2014

12

7.5

[FS9]

Location-based Hierarchical Matrix Factorization for Web Service Recommendation [52]

ICWS(C)

2014

11

7.5

[FS10]

Collaborative personal profiling for web service ranking and recommendation [58]

ISF(J)

2015

9

7.5

[FS11]

CluCF: a clustering CF algorithm to address data sparsity problem [49]

SOCA (J)

2016

1

7.5

[FS12]

Transferring auxiliary knowledge to enhance heterogeneous web service clustering [24]

IJHPCN (J)

2016

0

7.5

[FS13]

Clustering facilitated web services discovery model based on supervised term weighting and adaptive metric learning [46]

IJWET (J)

2013

5

7.5

[FS14]

Recommending web service based on user relationships and preferences [61]

ICWS (C)

2013

7

7.5

[FS15]

Context-Aware Post-filtering for Web Service Clustering [34]

SCC (C )

2014

1

7

[FS16]

Personalized Web Service Ranking via User Group Combining Association Rule [57]

ICWS (C)

2009

49

7

[FS17]

User-QoS-based Web Service Clustering for QoS Prediction [47]

ICWS (C)

2015

0

7

[FS18]

Service discovery acceleration with hierarchical clustering [21]

ISF(J)

2015

5

7

[FS19]

Co-clustering WSDL Documents to Bootstrap Service Discovery [38]

SOCA (C)

2014

2

7

[FS20]

Service Recommendation Using Customer Similarity and Service Usage Pattern [48]

ICWS (C )

2015

1

7

[FS21]

Time-aware Semantic Web Service Recommendation [53]

SCC (C )

2015

1

7

[FS22]

Collaborative filtering-based hybrid approach for web service recommendations [60]

RJASET (J)

2014

0

7

[FS23]

Particle swarm optimization for clustering semantic web services [42]

ISPDC (C )

2011

5

7

[FS24]

Clustering WSDL documents to bootstrap the discovery of web services [12]

ICWS (C )

2010

193

7

[FS25]

Web Services Discovery Based on Latent Semantic Approach [29]

ICWS ( C )

2009

12

6.5

[FS26]

A Hierarchical Matrix Factorization Approach for Location-based Web Service QoS Prediction [14]

SOSE ( C )

2014

0

6.5

[FS27]

Web Service Clustering using a Hybrid Term-Similarity Measure with Ontology Learning [35]

IJWSR (J)

2014

5

6.5

[FS28]

Web Service Clustering using Multidimensional Angles as Proximity Measures [40]

TOIT (J)

2009

85

6.5

[FS29]

Semantic-Based Clustering of Web Services [43]

JWE (J)

2015

0

6.5

[FS30]

QoS-aware service selection via collaborative QoS evaluation [54]

WWW (J)

2014

6

6.5

[FS31]

Taxonomic clustering and query matching for efficient service discovery [37]

ICWS (C )

2011

19

6.5

[FS32]

Leveraging Auxiliary Knowledge for Web Service Clustering [23]

CJE (J)

2016

0

6

[FS33]

Semantic-Based Automated Service Discovery [62]

TSC (J)

2012

42

6

[FS34]

Ontology-learning method for web services clustering [27]

ICCCT (C )

2012

0

6

[FS35]

Cluster-Based Web Service Recommendation [13]

SCC (C )

2016

0

6

[FS36]

Similarity analysis of service descriptions for efficient web service discovery [25]

DSAA (C )

2014

1

6

[FS37]

Ontology Learning with Complex Data Type for Web Service Clustering [36]

CIDM (C )

2014

0

6

[FS38]

Web Service Clustering Using Relational Database Approach [39]

IJSEKE (J )

2015

1

6

[FS39]

Improving REST Service Discovery with Unsupervised Learning Techniques [41]

CISIS (C )

2015

1

6

[FS40]

A clustering-based QoS prediction approach for web service recommendation [16]

ISORCW (C )

2012

7

6

[FS41]

Web service clustering using text-mining techniques [11]

IJAOSE ( J)

2009

93

6

[FS42]

Efficiently finding web services using a clustering semantic approach [31]

CSSSIA (C )

2008

77

6

[FS43]

Research on web service discovery with semantics and clustering [32]

ITAIC (C )

2011

20

5.5

[FS44]

A concept analysis approach for guiding users in service discovery [28]

SOCA (C )

2012

4

5.5

[FS45]

DaaS: Cloud-based mobile web service discovery [33]

PMC (J)

2013

22

5.5

[FS46]

WS-HFS: A Heterogeneous Feature Selection Framework for Web Services Mining [55]

ICWS (C )

2015

1

5.5

[FS47]

Learning Sparse Functional Factors for Large-Scale Service Clustering [26]

ICWS (C )

2015

3

5.5

[FS48]

Hierarchical clustering-based web service discovery [45]

ICISO (C )

2014

1

5.5

[FS49]

QoS-Aware Service Clustering to Bootstrap the Web Service Selection [68]

SCC (C )

2017

0

6

[FS50]

A new QoS-aware web service recommendation system based on contextual feature recognition at server-side [69]

TNSM (J)

2017

0

7

[FS51]

Mashup service clustering based on an integration of service content and network via exploiting a two-level topic model [70]

ICWS (C )

2016

6

7

[FS52]

Domain-aware Mashup service clustering based on LDA topic model from multiple data sources [71]

IST(J)

2017

0

6.5

[FS53]

Leveraging Track Relationships for Web Service Recommendation [72]

ICEBE ( C )

2016

1

6

[FS54]

A Web Service Discovery Approach Based on Mining Underlying Interface Semantics [73]

TKDE (J)

2017

2

7

[FS55]

Improving Web Service Clustering through a Novel Ontology Generation Method by Domain Specificity [74]

ICWS (C )

2017

0

7

[FS56]

WE-LDA: A Word Embeddings Augmented LDA Model for Web Services Clustering [75]

ICWS (C )

2017

0

7.5

[FS57]

Correlation-Aware Multi-Label Active Learning for Web Service Tag Recommendation [76]

ICWS (C )

2017

0

6.5

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Obidallah, W.J., Raahemi, B. & Ruhi, U. Clustering and Association Rules for Web Service Discovery and Recommendation: A Systematic Literature Review. SN COMPUT. SCI. 1, 27 (2020). https://doi.org/10.1007/s42979-019-0026-8

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