Monitoring web services’ quality of service: a literature review

Hasan, Mohd Hilmi; Jaafar, Jafreezal; Hassan, Mohd Fadzil

doi:10.1007/s10462-012-9358-7

Cited by 13 publications

(10 citation statements)

References 68 publications

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“…Therefore, only the records that fulfill a minimum support count of 2 will be included into the next cycle of algorithm processing. (Han et al 2012) In many cases, the Apriori algorithm reduces the size of candidate itemsets significantly and provides a good performance gain. However, it is still suffering from two critical limitations (Han et al 2012).…”

Section: Apriori Algorithmmentioning

confidence: 99%

“…(Han et al 2012) In many cases, the Apriori algorithm reduces the size of candidate itemsets significantly and provides a good performance gain. However, it is still suffering from two critical limitations (Han et al 2012). First, a large number of candidate itemsets may still need to be generated if the total count of a frequent k-itemsets increases.…”

Section: Apriori Algorithmmentioning

confidence: 99%

“…First, the EClaT algorithm transforms data from the horizontal format into the Table 3 Transactional data in vertical data format. Reproduced with permission from (Han et al 2012) itemset TID_set I1 {T100, T400, T500, T700, T800, T900} I2 {T100, T200, T300, T400, T600, T800, T900} I3 {T300, T500, T600, T700, T800, T900} I4 {T200, T400} I5 {T100, T800} Table 4 2-Itemsets in vertical data format. Reproduced with permission from (Han et al 2012) itemset TID_set {I1, I2} {T100, T400, T800, T900}…”

Section: Eclat Algorithmmentioning

confidence: 99%

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Algorithms for frequent itemset mining: a literature review

Chee¹,

Jaafar²,

Aziz³

et al. 2018

Artif Intell Rev

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Data Analytics plays an important role in the decision making process. Insights from such pattern analysis offer vast benefits, including increased revenue, cost cutting, and improved competitive advantage. However, the hidden patterns of the frequent itemsets become more time consuming to be mined when the amount of data increases over the time. Moreover, significant memory consumption is needed in mining the hidden patterns of the frequent itemsets due to a heavy computation by the algorithm. Therefore, an efficient algorithm is required to mine the hidden patterns of the frequent itemsets within a shorter run time and with less memory consumption while the volume of data increases over the time period. This paper reviews and presents a comparison of different algorithms for Frequent Pattern Mining (FPM) so that a more efficient FPM algorithm can be developed.

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Section: Apriori Algorithmmentioning

confidence: 99%

Section: Apriori Algorithmmentioning

confidence: 99%

Section: Eclat Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Algorithms for frequent itemset mining: a literature review

Chee¹,

Jaafar²,

Aziz³

et al. 2018

Artif Intell Rev

Self Cite

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show abstract

“…These QoS values are provided during publication time by the service provider (P r(S i )) as the advertised QoS. They can be established by the system after a short period of service testing and monitoring (The system can use one of the approaches proposed in this survey [14]). Then, to bootstrap the reputation of the new service S i , the system goes through three phases: i) provider reputation evaluation, which is explained in Section III, ii) reputation estimation from similar services, which is described in Section IV, and iii) reputation estimation from multiple regression models built from QoS and reputation values of long-standing services in the system, which is detailed in Section V.…”

Section: Reputation Bootstrapping Modelmentioning

confidence: 99%

Regression-Based Bootstrapping of Web Service Reputation Measurement

Tibermacine

Cherif

2015

2015 IEEE International Conference on Web Services

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Abstract-In the literature, many solutions for measuring the reputation of web services have been proposed. These solutions help in building service recommendation systems. Nonetheless, there are still many challenges that need to be addressed in this context, such as the "cold start" problem, and the lack of estimation of the initial reputation values of newcomer web services. As reputation measurement depends on the previous reputation values, the lack of initial values can subvert the performance of the whole service recommendation system, making it vulnerable to different threats, like the Sybil attack. In this paper, we propose a new bootstrapping mechanism for evaluating the reputation of newcomer web services based on their initial Quality of Service (QoS) attributes, and their similarity with "long-standing" web services. Basically, the technique uses regression models for estimating the unknown reputation values of newcomer services from their known values of QoS attributes. The technique has been experimented on a large set of services, and its performance has been measured using some statistical metrics, such as the coefficient of determination (R 2 ) and the Mean Square Error (MSE).

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“…In relation to that, we proposed a fuzzy-based model for web services' QoS monitoring using fuzzy interval type-2 (IT2) [5,6]. The main reason for proposing IT2-based monitoring system was to allow customers to specify their QoS requirements using linguistic definitions instead of exact numeric data.…”

Section: Introductionmentioning

confidence: 99%

Experimental study on the effective range of FCM's fuzzifier values for web services' QoS data

Hasan

Jaafar

Hassan

2014

2014 International Conference on Computer and Information Sciences (ICCOINS)

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The work presented in this paper is part of the development of a fuzzy Interval Type-2 (IT2)-based system. An IT2 system contains fuzzy type-2 membership functions that can be generated using a pair of FCM fuzzifier, m values. Evidences show that the effective range of m values is influenced by the underlying dataset. Hence, the objective of this paper is to present the experimental study on finding the effective range of m values for web services' QoS data. The study aimed at identifying range of m values that successfully generated Gaussian membership functions. As proposed by previous work, the experiment was carried out upon the m values in the range of 1.4 to 2.6. The works involved the datasets of three QoS parameters. The results showed that two of the datasets has the effective range of 1.7 to 2.6, while another one was 1.6 to 2.6.

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Monitoring web services’ quality of service: a literature review

Cited by 13 publications

References 68 publications

Algorithms for frequent itemset mining: a literature review

Algorithms for frequent itemset mining: a literature review

Regression-Based Bootstrapping of Web Service Reputation Measurement

Experimental study on the effective range of FCM's fuzzifier values for web services' QoS data

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