Web Service Composition Methods


Nowadays, social media are regularly used by businesses to share information about their products and services and get immediate feedback from a large pool of users. Considering this, social media sites create multiple opportunities for social media commerce or, in other words, active participation in “the marketing and selling of products and services in online marketplaces and communities” (Baethge, Klier, & Klier, 2015, p. 269).

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To meet various functional and non-functional requirements of users on social commerce platforms, the right tools must be implemented to render multiple relevant web services. Thus, the present research project will aim to identify the best possible web service composition technique able to satisfy those requirements.

Research Proposal

To evaluate various composition methods, the quality of services (QoS) metrics will be applied. According to Rajeswari et al. (2014), as well as Sethuramana, Sasiprabha, and Sandhya (2015), they include cost, execution time, reliability, reputation, availability, throughput, trust, and security. Along with this, a holistic assessment taxonomy developed by Lemos, Benatallah, and Daniel (2015) will be used as it allows us to define the overall ability of composition methods to solve fundamental tasks within the social commerce environment. Their taxonomy includes the following elements: composition language, knowledge reuse, automation, tool support, execution platform, and target users.

To attain the formulated research objectives, a large sample of literature on the subject of interest will be systematically and critically reviewed and high-quality evidence will be retrieved to answer the following questions:

  • What are the major requirements of users on social commerce platforms and what characteristics do the latter have?
  • How suitable are the existing workflow techniques and AI techniques in supporting web services on social commerce platforms: which advantages and drawbacks do they have in terms of the QoS and holistic taxonomy metrics?

Besides the systematic literature review that will provide background information, the quantitative analytical methods will be used to compare the identified web service composition techniques based on the suggested evaluation criteria. Compared to qualitative methods that aim to establish dynamic links among variables and answer “why” questions, quantitative analysis tools allow obtaining objective findings.

Both descriptive and inferential statistics instruments will be applied in the proposed research. The former will help summarize evaluation results in the form of mean numbers, while the latter will assist in revealing more complex associations between the studied web service composition techniques and social commerce characteristics (Ali & Bhaskar, 2016). Overall, a combination of both inductive and deductive reasoning approaches will be applied to solve the intended research task.

Induction implies the construction of theory and hypotheses from observation of relationships among distinct variables, and it will be implemented at the literature review stage (Zalaghi & Khazaei, 2016). Conversely, the deduction starts with the identification of objectives and proceeds to building a logical structure that would satisfy them (Zalaghi & Khazaei, 2016). This approach will be utilized during the analytical and comparative part of the project after the most suitable web service composition technique for social commerce will be suggested.

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Social commerce is an emerging and increasingly promising phenomenon that currently draws a lot of interest due to its multiple benefits for both consumers and businesses. Considering high interoperability demands on such commercial platforms and in applications, the issue of service delivery is of significant concern. The analysis of web service composition techniques allows addressing this concern and look at its root.

The evaluation of various web service composition techniques about their quality and suitability to the social commerce environment will thus provide theoretical implications for further research, as well as the practical ones for algorithm developers.


Ali, Z., & Bhaskar, S. B. (2016). Basic statistical tools in research and data analysis. Indian Journal of Anaesthesia, 60(9), 662-669.

Baethge, C., Klier, J., & Klier, M. (2016). Social commerce—state-of-the-art and future research directions. Electronic Markets, 26(3), 269-290.

Lemos, A., Benatallah, B., & Daniel, F. (2015). Web service composition: A survey of techniques and tools. ACM Computing Surveys, 48(3), 33-41.

Rajeswari, M., Sambasivam, G., Balaji, N., Basha, M. S., Vengattaraman, T., & Dhavachelvan, P. (2014). Appraisal and analysis on various web service composition approaches based on QoS factors. Journal of King Saud University – Computer and Information Sciences, 26(1), 143-152.

Sethuraman, R., Sasiprabha, T., & Sandhya, A. (2015). An effective QoS based web service composition algorithm for integration of travel & tourism resources. Procedia Computer Science, 48, 541-547.

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Zalaghi, H., & Khazaei, M. (2016). The role of deductive and inductive reasoning in accounting research and standard setting. Asian Journal of Finance & Accounting, 8(1), 23.

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StudyCorgi. (2021, April 18). Web Service Composition Methods. Retrieved from https://studycorgi.com/web-service-composition-methods/

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"Web Service Composition Methods." StudyCorgi, 18 Apr. 2021, studycorgi.com/web-service-composition-methods/.

1. StudyCorgi. "Web Service Composition Methods." April 18, 2021. https://studycorgi.com/web-service-composition-methods/.


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StudyCorgi. 2021. "Web Service Composition Methods." April 18, 2021. https://studycorgi.com/web-service-composition-methods/.


StudyCorgi. (2021) 'Web Service Composition Methods'. 18 April.

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