A survey on Web Service Selection and Ranking Methods

Kayastha, Ronak R; Baria, Jwalant

doi:10.5120/20636-3290

Cited by 6 publications

(2 citation statements)

References 6 publications

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“…The flourish of web-based services gave rise to the research area called services computing, and its academic community has kept continuously expanding since nearly 20 years ago [1]. Services computing involves many classical research problems, such as service selection [2], service discovery [3], service composition [4], service classification [5], Quality of Service (QoS) prediction [6], and service recommendation [7].…”

Section: Introductionmentioning

confidence: 99%

External Service Sensing (ESS): Research Framework, Challenges and Opportunities

Wang,

Liu,

et al. 2021

Preprint

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The flourish of web-based services gave birth to the research area services computing, a rapidly-expanding academic community since nearly 20 years ago. Consensus has been reached on a set of representative research problems in services computing, such as service selection, service composition, service recommendation, and service quality prediction. An obvious fact is that most services keep constant changes to timely adapt to changes of external business/technical environment and changes of internal development strategies. However, traditional services computing research does not consider such changes sufficiently. Many works regard services as static entities; this leads to the situation that some proposed models/algorithms do not work in real world. Sensing various types of service changes is of great significance to the practicability and rationality of services computing research. In this paper, a new research problem External Service Sensing (ESS) is defined to cope with various changes in services, and a research framework of ESS is presented to elaborate the scope and boundary of ESS. This framework is composed of four orthogonal dimensions: sensing objects, sensing contents, sensing channels, and sensing techniques. Each concrete ESS problem is defined by combining different values in these dimensions, and existing research work related to service changes can be well adapted to this framework. Real-world case studies demonstrate the soundness of ESS and its framework. Finally, some challenges and opportunities in ESS research are listed for researchers in the services computing community. To the best of our knowledge, this is the first time to systematically define service change-related research as a standard services computing problem, and thus broadening the research scope of services computing.

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Section: Introductionmentioning

confidence: 99%

External Service Sensing (ESS): Research Framework, Challenges and Opportunities

Wang,

Liu,

et al. 2021

Preprint

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“…Finally, section V presents the conclusion. Kayastha and Baria (2015)surveyed the various QoS-based WS recommendation with the consideration of non-functional properties namely, response time, reliability and the availability. The prediction of user's navigation is the major objective of WS recommendation.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Haversine-Genetic Geographical Modelling for an Optimal Web Service Selection/Recommendation

Kanagaraj¹,

Suresh²

2019

ECECSR

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Web Service selection and recommendation becomes major aspects of service providing the community. Quality of Service prediction through Collaborative Filtering (CF) or Matrix Factorization (MF) is the key process in WS recommendation. Few or absence of QoS records (cold start), integration of QoS factors (response time and throughput), extensible models utilization and inaccurate similarity measures are the major problems in CF/MF approaches. This paper focuses on integration of similarity values with the geographical distance and QoS values (minimum response time and maximum throughput) to provide efficient mapping relationship (user-service and service-user). Initially, the geographical model is constructed by Haversine distance formulation based on latitude/longitude values. The Genetic Algorithm (GA) based recommendation list construction provides best WS as the responses to user query. The experimental validation of proposed work regarding response time, memory space, computation time, precision, coverage and number of patterns mined against existing methods assure effectiveness in QoS prediction.

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