Raza, M, Hussain, FK, Hussain, OK, Zhao, M & Rehman, ZU 2019, 'A comparative analysis of machine learning models for quality pillar assessment of SaaS services by multi-class text classification of users' reviews', Future Generation Computer Systems, vol. 101, pp. 341-371.View/Download from: Publisher's site
© 2019 Elsevier B.V. Software as a Service (SaaS) has emerged as the most widely used of all the current software delivery models. With the growth of edge computing, as SaaS services increasingly become distributed, selecting the best SaaS provider from those available is challenging but it is of critical importance. In the recent past, well-known cloud service providers such as Amazon Web Services and Microsoft have developed frameworks and service quality pillars for cloud applications. However, there are currently no mechanisms for product users to know if and to what extent a service satisfies the defined service pillar. Having such information would enable users to form trustworthy associations in edge computing. In this paper, we address this drawback by adopting a systematic approach of analysing customer reviews related to SaaS products and ascertain to which service quality pillar they refer. We use eleven traditional machine learning classification approaches and a weighted voting ensemble of these classifiers to achieve this task and test the performance of each of them. Since the dataset is unbalanced in terms of sample distribution per class, we use 10-fold cross-validation on the training dataset to determine the best parameters for each machine learning algorithm to achieve optimal performance. Friedman test and Nemenyi's post hoc test is applied to identify the significant difference among the classifiers performance during cross-validation. Based on the experimental results, a comparative analysis is conducted to identify the best performing machine learning classification model on the SaaS reviews. The results show that the performance of the logistic regression model has a higher performance among the individual classifiers and the weighted voting ensemble shows minimal improvement in overall performance.
Ikram, MA, Sharma, N, Raza, M & Hussain, FK 2020, 'Dynamic Ranking System of Cloud SaaS Based on Consumer Preferences - Find SaaS M2NFCP', Advances in Intelligent Systems and Computing, pp. 1000-1010.View/Download from: Publisher's site
© 2020, Springer Nature Switzerland AG. Software as a Service (SaaS) is a type of software application that runs and operates over a cloud computing infrastructure. SaaS has grown more dramatically compared to other cloud services delivery models (i.e. PaaS and IaaS) in terms of the number of available services. This rapid growth in SaaS brings a lot of challenges for consumers in selecting the optimum services. The aim of this article is to propose a ranking system for SaaS based on consumer's preferences called Find SaaS M2NFCP. The proposed ranking system is based on measuring the shortest distance to the minimum and maximum of the selected consumer's non-functional preferences. In addition, linguistic terms are taken into account to weight the most important non-functional preferences. The proposed system is evaluated against traditional SaaS ranking systems using data collected from online CRM SaaS and achieved improved results.
Raza, M, Hussain, FK, Hussain, O & Chang, E 2010, 'Md(2) Metrics For Optimizing Trust Prediction In Digital Business Ecosystem', Intelligent Decision Making Systems, International Conference on Intelligent Systems and Knowledge Engineering, World Scientific And Engineering Acad And Soc, Hasselt, BELGIUM, pp. 402-410.View/Download from: Publisher's site
The process of predicting the future trust value of an entity, based on its past value is a challenging issue The prediction process is even more imperative in the scenario where in the interaction would take place at a future point in time Being able to
Raza, M, Hussain, FK, Hussain, OK & Chang, E 2010, 'Q-Contract Net: A negotiation protocol to enable quality-based negotiation in Digital Business Ecosystems', CISIS 2010 - The 4th International Conference on Complex, Intelligent and Software Intensive Systems, pp. 161-167.View/Download from: Publisher's site
The Digital Business Ecosystem (DBE) is the result of the co-evolution of the Business Ecosystem and the Digital Ecosystem. There are numerous approaches and enabling technologies which are used in modeling open business marketplaces and, due to the similarities between the Digital Business environments, they can also help to enable the Digital Business Ecosystem but with some limitations. The complete lifecycle of the DBE can be decomposed into the following phases: formation, evolution and dissipation. In this work, our main focus is on the importance of negotiation in the DBE formation phase and especially on the structure of Contract Net Protocol. We will present an extension to the primitive Contract Net Protocol and name it Contract Net with Quality Protocol (CNQP or Q-Contract Net) to facilitate the negotiation process by adding the quality evaluation steps during the negotiation phase of the DBE formation. © 2010 IEEE.
Raza, M, Hussain, FK & Chang, E 2009, 'Quality measures for digital business ecosystems formation', Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, pp. 118-121.View/Download from: Publisher's site
To execute a complex business task, business entities may need to collaborate with each other as individually they may not have the capability or willingness to perform the task on its own. Such collaboration can be seen implemented in digital business ecosystems in the form of simple coalitions using multi-agent systems or by employing Electronic Institutions. A major challenge is choosing optimal partners who will deliver the agreed commitments, and act in the coalition's interest. Business entities are scaled according to their quality level. Determining the quality of previously unknown business entities and predicting the quality of such an entity in a dynamic environment are crucial issues in Business Ecosystems. A comprehensive quality management system grounded in the concepts of Trust and Reputation can help address these issues. © 2009 ICST Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering.
Raza, M, Hussain, FK & Chang, E 2008, 'A methodology for quality-based mashup of data sources', Proceedings of the 10th International Conference on Information Integration and Web-based Applications and Services, iiWAS 2008, pp. 528-533.View/Download from: Publisher's site
The concept of mashup is gaining tremendous popularity and its application can be seen in a large number of domains. Enterprises using and relying upon mashup have improved their mass collaboration and personalization. In order for mashup technology to be widely accepted and widely used, we need a methodology by which can make use of the quality of the input to the mashup process as a governing principle to carry out mashup. This paper reviews the concept of mashup in different domains and proposes a conceptual solution framework for providing quality based mashup process. © 2008 ACM.