A New Efficient Method for Calculating Similarity Between Web Services

Rachad, Taoufik; Boutahar, Jaouad; ghazi, S. El

doi:10.14569/ijacsa.2014.050809

Cited by 10 publications

(10 citation statements)

References 9 publications

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“…3) Extraction of DM algorithm knowledge attributes using Algorithm 4, which collectively produces DM algorithm knowledge, K A (see definition in Table 1). 4) Matching among the attributes of data knowledge, goal knowledge and DM algorithm knowledge are performed using Algorithm 5 to find similarity of knowledge based on the concept of similarity checking approach introduced in [24]. This technique enables the proposed model to select appropriate DM algorithms dynamically for the heterogeneous type of data domains having multiple goals in each domain.…”

Section: Definition Of Parametersmentioning

confidence: 99%

A Novel Framework for Recommending Data Mining Algorithm in Dynamic IoT Environment

et al. 2020

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Internet of Things (IoT) has been the driving force for many smart city applications. The huge volume of IoT data generated from these applications require efficient processing to get the insight, which poses significant difficulty. Data mining and machine learning (DM) algorithms are used to minimize such difficulty. However, it is still very challenging to select a particular DM algorithm that can process a dynamic IoT dataset based on some application-specific goals to achieve better accuracy. This paper proposes a knowledge-driven framework that considers the knowledge of datasets, available DM algorithms, and application goals to select the suitable DM algorithm for performing a target data processing task. This work considers data from cultural domain, health domain, and transportation domain in the experiment. The results show that the proposed approach dynamically selects the best-suited DM algorithms for the available datasets and target goals that exhibits satisfactory performance in obtaining accurate results compared to the existing work. The proposed approach not only provides flexibility in conducting dynamic IoT data mining tasks, but also reduces the complexity that would otherwise be necessary while adopting the traditional data mining approaches.

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Section: Definition Of Parametersmentioning

confidence: 99%

A Novel Framework for Recommending Data Mining Algorithm in Dynamic IoT Environment

et al. 2020

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“…The works led by [8], [9] concludes that Jaro-Winkler has performed better in term of results in several experiments and can be used in several fields and it is much faster than the others methods.…”

Section: A) Syntactic Analysismentioning

confidence: 99%

“…According to [8], it cannot be said that there are more efficient or more optimal methods than others because the studies conducted to examine them took some evaluation criteria and ignored others. But the method that provides better results with WordNet is Wu -Palmer.…”

Section: ) Hybrid Approachmentioning

confidence: 99%

A New Approach for Grouping Similar Operations Extracted from WSDLs Files using K-Means Algorithm

Sara¹,

Fatima²,

Loukil³

2017

ijacsa

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Abstract-Grouping similar operations is an effective solution to the various problems, especially those related to research because the services will be classified by joint operations. Searching for a particular operation returns, as a result, all services with this same operation, but also the problems related to the substitution (such as, during a call failure or a malfunction). A list of similar operations is returned to the client. He chooses an operation, based on non-functional criteria. In this work, our goal is to study the functional similarity between operations, and thus constituting groups of similar operations, while benefiting from the K-means algorithm.

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“…Rashad et al in [4] proposed an approach for measuring the similarity between the identifiers of operations based on a semantic and syntactic study. The study ignores different types that have a part in the result of the study.…”

Section: Introductionmentioning

confidence: 99%

“…The work in [4] allowed the authors in [5] the adoption of a method for the substitution of web services. A work of detecting correspondence relations between operations was realized (For two similar Web services, an operation of one replaces an operation of the other).…”

Section: Introductionmentioning

confidence: 99%

A new approach to gather similar operations extracted from web services

Sara¹,

Bakhta²,

Loukil³

2019

IJECE

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<span lang="EN-US">A web service is an autonomous software that exposes a set of features on the Internet, it is developed and published by providers and accessed by customers who discover it, select it, invoke and use it. Several research policies have been implemented such as searching through keywords, searching according to semantics and searching by estimating the similarity. A customer is looking for a service for the operations he/she carries out, hence the interest of guiding the search for services towards a search for operations: finding the desired operations amounts to finding the services. For this, groupings of similar operations would make it possible to obtain all the services that can meet the desired functionalities. The customer can then select, in this set the service or services according to its non-functional criteria. The paper presents a study of the similarity between operations. The proposed approach is validated through an experimental study conducted on web services belonging to various domains.</span>

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A New Efficient Method for Calculating Similarity Between Web Services

Cited by 10 publications

References 9 publications

A Novel Framework for Recommending Data Mining Algorithm in Dynamic IoT Environment

A Novel Framework for Recommending Data Mining Algorithm in Dynamic IoT Environment

A New Approach for Grouping Similar Operations Extracted from WSDLs Files using K-Means Algorithm

A new approach to gather similar operations extracted from web services

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