No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
The Role of Machine Learning in Personalizing User Experiences in SaaS Products
Abstract
This paper explores the pivotal role of Machine Learning (ML) algorithms in personalizing user experiences within Software-as-a-Service (SaaS) products and its influence on market success. In today's competitive landscape, delivering personalized user experiences has become paramount for SaaS providers to enhance engagement, retention, and ultimately, market competitiveness. ML algorithms offer a powerful mechanism to analyze vast amounts of user data, derive meaningful insights, and dynamically tailor user experiences to individual preferences and behaviors. By investigating how ML algorithms can personalize user experiences, this research aims to shed light on the intricate relationship between personalization, user engagement, and market success in the realm of SaaS products. Through a comprehensive analysis of existing literature and a survey analysis conducted as part of this study, this paper elucidates the various ways in which ML-driven personalization strategies can drive user satisfaction, increase product adoption, and foster long-term customer loyalty.The survey analysis reveals compelling insights into the perceptions and behaviors of users regarding personalized user experiences within SaaS products. The findings suggest that ML-driven personalization significantly influences users' decisions to continue using SaaS products, contributes to user advocacy and word-of-mouth recommendations, enhances perceived product value, justifies premium pricing, and positively impacts users' perceptions of product quality.
... EDI was further advanced by the development of the Trade Data Elements Directory, which defined data element positions for EDI documents, and the Trade Data Interchange Directory which defined rules for electronic document interchange message format. These trends began to foster EDI as an orderly transmission of structured data through value-added networks and computer networks with agreed protocols [10]. ...
EDI currently remains one of the most significant technological formats that respond to contemporary rapid and connected business environment by ensuring real-time, efficient and accurate data exchange in supply chain management. This paper's central idea is the evolution of EDI from the early days of standard trade papers to contemporary technologies like blockchain, AI, ML, and IoT. Focusing on processes where these innovations add value for EDI, providing real-time data, improving security, and facilitating better decisions, this paper emphasises the essential functions of EDI in the modernisation of supply chains. In addition, it examines how EDI integration can help to minimise cost; manage stocks; and enhance business associations. Altogether, this paper showcases EDI's positive effects on performance-enhancing aspects of the supply chain and outlines prospective applications of novel technologies to add further value to global supply chain processes.
... There has been encouraging progress in a field of fraud detection using ML approaches, namely supervised and unsupervised learning [13]. While, supervised methods require pre-labeled datasets for training models to detect fraudulent transactions, the unsupervised methods are employed to detect anomalies in flow of transactions [14]. However, in line with increasing complexity of the fraud detection, DL has emerged as a subfield of ML. • The data is preprocessed by dealing with uneven data, missing values, and duplicate entries. ...
Credit card fraud is a major problem for financial institutions. Credit card fraud costs over one million people every year across several different nations. Research studies involving the study of actual credit card data are few and few between because of privacy issues. In order to prevent financial losses and make sure that transactions are safe, CCFD is an essential application in financial systems. This paper focuses on the analysis of financial fraud detection system that uses ML algorithms with special emphasis on the CCFD dataset. The system methods for increasing the model’s accuracy consist of data preprocessing, feature engineering, and class balancing with Borderline-SMOTE. LSTM model improves the performance of the competitors’ algorithms with 99.9% accuracy, precision80.3%, a recall92.1% and F1 - score85.8%. Regarding the trade-off among accuracy and recall rate, LSTM is superior to other models from the fraud detection portfolio, including DT, RF, and SVM. Continuous real-time data, CNNs and other forms of DL may be further explored in future research to extend the detection of fraud. More improvements could also center on model interpretation and bringing up the models’ suitability for practical utility in big conventional financial structures.
... Khalil & Winkler (2023), Arora et al. (2024) Fuente: Elaboración propia (2024) ...
Introducción: Este estudio examina la adopción del Software como Servicio (SaaS) en organizaciones, destacando su crecimiento y las variaciones en su difusión debido a factores tecnológicos, organizacionales y ambientales. Metodología: Utilizando una revisión de la literatura, se propone un modelo de preparación en trípode que incluye aspectos tecnológicos, organizacionales y ambientales. Resultados: Los resultados empíricos indican que todos los aspectos son importantes para la adopción de SaaS, aunque sus influencias varían entre variables psicológicas y resultados manifiestos. Discusión: Los hallazgos sugieren que las organizaciones deben estar preparadas en múltiples dimensiones para adoptar eficazmente SaaS. Conclusiones: Se subraya la importancia de una preparación integral para maximizar los beneficios del SaaS en entornos organizacionales.
... SaaS is a widely used paradigm for cloud applications due to its simplicity and ease of implementation. Instead of keeping an application on your own server or computer, with SaaS, you pay a subscription fee each month to use a program that is hosted on the internet [48]. This software has a wide range of uses, from making vacation reservations to watching films online. ...
Web development and online applications have progressed to the point that several frameworks and tools have been created to make development easier. The use of such frameworks does improve the speed and efficiency of web development, but it is not without its drawbacks. In the era of digital transformation, the demand for efficient and scalable web applications has significantly increased. This paper explores the landscape of web application development, comparing key technologies and frameworks that have shaped modern web services. Specifically, it delves into the advantages and characteristics of Ruby on Rails (RoR) and Python, two prominent frameworks used in web development, highlighting their strengths, challenges, and use cases. This work examines the role of cloud-based solutions in enhancing web application efficiency, scalability, and accessibility, emphasising models like Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS). The paper also investigates the integration of Ruby on Rails and Python with cloud platforms, illustrating how major companies leverage these technologies to drive innovation. Furthermore, a comparative analysis of Ruby on Rails and Python is presented, focusing on aspects such as learning curve, development speed, scalability, performance, and community support. The insights gathered aim to assist developers and businesses in selecting the right technologies for their web application needs, ensuring optimal performance, flexibility, and future scalability.
... The future of ERP development is centred around leveraging emerging technologies such as AI, ML, and blockchain to improve automation, analytics, and security [26]. The adoption of hybrid cloud models is expected to grow, combining the flexibility of cloud ERP with the control of on-premise systems. ...
... The process of label encoding involves assigning numerical values to category data in order to make them more accessible to ML algorithms [39][40]. In order to make the training phase of a ML model easier, we need to convert categorical information to numerical representations [41]. This is achieved by replacing categorical values with numbers between 0 and (n-1), where 'n' represents the total number of different classes displayed in Figure 3 Figure 4 demonstrates the process of label encoding for attack types, where each unique attack type is assigned a distinct numerical label, ranging from 0 to 10 in this example. ...
There has been an increase in the number of cyberattacks, which has led to an increase in the need for NIDSs, which are essential for protecting against prospective attacks. There are several important study fields, one of which is the detection and prevention of cyberattacks. In addition to having poor accuracy, existing NIDSs are not equipped to handle the latest, unidentified cyberattacks. Using the UNSW-NB15 dataset and cutting-edge ML algorithms together with thorough data pretreatment, this study suggests a complete framework for improving network intrusion detection. Following the division of the training dataset (80%) and testing (20%) subsets, several models such as LSTM, ET, DT, and RF were assessed using performance metrics including ROC-AUC, F1-score, recall, accuracy, and precision. Outcomes displays the superior performance of the LSTM model, achieving 98.31% accuracy, 97.87% precision, 98.74% recall, and an F1-score98.30%, with an AUC0.9988. Comparative analysis confirms the LSTM model's effectiveness over other models, establishing it as a reliable solution for intrusion detection. The findings underscore the potential of integrating data balancing, feature optimization, and advanced machine learning models to enhance cybersecurity frameworks.
... Serverless computing is not easily defined as it has so many synonyms, including PaaS and SaaS [19] [20]. The many degrees to which developers may manipulate the underlying cloud architecture provide one angle from which to view serverless [21] [22]. ...
The importance of using web applications in business has rapidly increased as it has influenced the change of business activities but, at the same time, poses a high risk in terms of security and privacy. A possible answer is serverless architecture that allows developers to focus on their code and utilise the opportunities of cloud platforms making old traditional server management unnecessary. This work aims to discuss the advancement that serverless architecture has to offer in the safety and ease of use of data in web apps. FaaSt and BaaS are looked at, as well as their integrated threats like injection attacks, insecure dependencies and vendor lock-in. There are recommendations concerning these risks: strong authentication, API gateway security, and conformity to regulations and standards. Future research avenues focus on the design of AI architectures for security, approaches to maintain privacy, as well as the creation of standard methodologies for debugging distributed systems. A more safe and scalable digital future is within reach with serverless architecture, which takes the use of new technology like quantum computing and blockchain to further strengthen the privacy and security of online services.
... advanced machine learning methods to construct and compare models' ability to quickly and accurately identify anomalies in time series data. With such techniques when implemented in the big data set, the study will show how machine learning (S. S. A. Pranav Khare, 2024;Rohilla et al., 2020;sahil Arora, 2023) models can be used to support the prediction maintenance, detection of anomalies, and analysis of trends in real applications. The contribution of this work is as follows: ...
The process of identifying new patterns or peculiarities that exist in the regular time series data is called time series novelty detection or anomaly detection. Although it is one of the most difficult data mining areas, it is gaining popularity due to its quick application to real-world problems. This research proposes a novel way to detect time series novelty using ML algorithms. An usage of suggested ML techniques to find outliers in time series data has increased recently. Using a dataset from Stack Overflow, this research investigates the use of machine learning for anomaly and novelty identification from time series data.The initial data preparations were dealing with missing values, examining tags of datasets and data visualisation through individuals’ words and words using the following assessment metrics: MAE=0. 0629, RMSE=0. 089, and MSE=0. 007. The performance of the second-best model, ARIMA, yielded an MAE = 0. 068, RMSE = 0. 0936 and MSE = 0. 008. The lowest accuracy for this task was witnessed with the Decision Tree Regressor since its error rates were the highest. The results confirm the suitability of the Random Forest Regressor in increasing accuracy in time series data with a special focus on novel and abnormal data point detection while emphasising the significance of the model choice.
... It also helps that generative design can generate several design concepts at once, which enables designers to experiment with different forms and different capabilities. Due to this, AI has the capability of testing a product and establishing areas of usability challenges before the final product hits the market [22] [23]. ...
The skill set needed for enterprise application development is more extensive than what is taught in typical programming courses. Enterprise applications are often rather complicated, distributed, networked, and multi-user. As a result, proficient software engineers need to know more than just the fundamentals of a programming language. GenAI has radically changed the world of enterprise application development and pointed to new approaches and tools for enterprises. This review provides a history and analysis of GenAI with a focus on current use for code generation, testing, and more efficient usability/UX design. Some of the summarised GenAI models like GANs and Transformer architecture are compared with their potential aspects concerning content generation, data synthesis, and predictive analysis. This paper examines the performance, security, and other concerns of implanting GenAI across enterprise environments and how organisations can effectively deploy these tools to promote progress, cut time to market, and enhance development. Besides, the obstacles regarding the implementation of GenAI are also discussed, such as data security, ethical issues and the role of governance. This review should also be beneficial to practitioners and researchers who want to design or incorporate GenAI in enterprise application development.
... Out of all the ways that AI is being used in this field, SAAS and AI stand out as a paradigm-shifting example that is changing the way that the development process is traditionally structured. Using AI and SaaS for enterprise app development has some advantages that can influence business greatly [10] [11]. First, AI facilitates decision-making by evaluating large data sets and giving organisations precise, up-to-date information so they can use it [12] [13]. ...
Purpose: This paper aims to explore the transformative impact of artificial intelligence (AI) on enterprise models within the context of Software as a Service (SaaS), highlighting the necessity for organisations to adopt AI-driven strategies to sustain their competitive edge. Methodology: The study utilises a comprehensive framework that outlines five critical steps for implementing AI in business settings, including executing pilot projects, assembling in-house AI teams, providing extensive training, developing holistic AI strategies, and ensuring effective communication. Findings: The integration of AI within SaaS not only enhances operational efficiency and process optimisation but also allows for real-time customisation and improved user engagement, thus fostering customer loyalty and driving sustainable growth. Unique contribution to theory, practice and policy: The paper contributes to the understanding of AI and SaaS as essential components in the evolution of enterprise applications, proposing that organisations adopt a collaborative approach to harness their combined potential, ultimately informing policymakers about the strategic importance of AI in shaping future business landscapes.
Well-implemented personalization systems significantly boost user engagement and drive substantial increases in conversion rates across industries. These systems leverage cutting-edge technologies to deliver personalized experiences at massive scale: modern architectures process hundreds of thousands of requests instantly, while edge computing dramatically reduces response times and advanced privacy frameworks protect user data without compromising performance. Organizations implementing these systems report transformative business outcomes, from remarkably higher new user retention to increased feature adoption. Leading platforms demonstrate the real-world impact, with major streaming services efficiently processing enormous volumes of data while serving personalized content to hundreds of millions of users with near-perfect availability. This article examines how modern machine learning architectures achieve these results, exploring both current capabilities and emerging technologies that are reshaping the future of personalized digital experiences.
Over the past 20 years, the SaaS business model has undergone significant
development. The pandemic, technological progress and the widespread use of artificial
intelligence are more favorable conditions for the rapid distribution of products using
cloud technologies. Armenia's expanding technological ecosystem, marked by a
multitude of startups and IT companies, creates favorable conditions for the development
and promotion of SaaS solutions. The presence of highly qualified specialists and a culture
of innovation significantly contributes to the growth of the SaaS market in Armenia. In
addition, the growing demand of local Armenian companies for SaaS products and the
success of some Armenian SaaS products in international markets also have a positive
impact on the development of the industry. This article discusses the approaches used to
promote Armenian SaaS products and the main problems associated with their success.
Through expert surveys and observation of about four dozen Armenian SaaS companies,
the marketing strategies of these organizations aimed at increasing the recognition of the
latter, attracting and retaining customers, as well as obstacles to entering the international
market were studied. The main marketing tools and approaches that are most conducive
to solving the marketing tasks that are primary at a given time in different stages of the
SaaS product lifecycle have been identified.
A paradigm for high-performance computing services, cloud computing integrates the latest developments in distributed computing, virtualization, load balancing, parallel processing, network storage, and hot backup redundancy. In light of the fact that there is currently no reliable method for DAG task scheduling that guarantees a balanced distribution of resources across nodes, this study suggests an approach for LB algorithms in cloud computing. This research presents a new paradigm for resource selection and job scheduling, which is abstracted from swarm intelligence such as PSO, SA, Cuckoo search, etc. The LBA tackles critical issues with the system under investigation, such as system overhead or resource limits, with the goal of achieving lowest makespan and execution time while concurrently improving resource usage. This component is incorporated in the CloudSim simulation environment where the framework measures performance under different cloud models, task intensity and resource provisioning. Experimental outcomes prove that an LBA is better than an existing algorithm, where it gives an average makespan of 894.85ms, execution time of 614.88ms, and resource utilization was 69%. Comparative analysis with PSO and CSSA confirms the superior efficiency of LBA in maximizing resource allocation. These results highlight the possibility of optimization methods derived from nature to improve cloud performance by means of efficient scheduling and load balancing.
The rapid growth of cloud computing has created a demand for efficient resource utilization and high-performance computing (HPC) solutions. An essential component for overseeing cloud workloads is Kubernetes, a top-tier container orchestration platform. However, challenges such as resource underutilization, workload inefficiencies, and performance bottlenecks persist in dynamic cloud environments. This paper presents a comprehensive evaluation of optimizing Kubernetes for high-performance computing (HPC) in hybrid cloud-edge environments. Kubernetes is configured with components such as Etcd, Kube-APIServer, Kube-controller-manager, and Kube-scheduler to enable efficient resource management and workload orchestration. Kubeflow operators are integrated to automate machine learning workflows, including distributed training, hyperparameter tuning, and model serving. Performance metrics were analyzed for two methods, KFT and KFL. KFT achieved a deployment time of 173 seconds, a task completion time of 4.62 hours, CPU utilization of 3.47%, and RAM utilization of 3708 MB. Conversely, KFL demonstrated a faster deployment time of 51.29 seconds, a task completion time of 5.31 hours, CPU utilization of 13.77%, and RAM utilization of 2725 MB. Furthermore, KFT outperformed KFL in accuracy, reaching 0.55 at epoch 10, compared to 0.50 for KFL. These findings highlight a trade-off between resource utilization and performance, offering key insights into optimizing Kubernetes for scalable HPC systems in cloud-native environments.
In this paper, introduce a novel machine learning (ML) approach to optimize role management and permission control within Role-Based Access Control (RBAC) systems. Leveraging the concept lattice from formal concept analysis, we propose the Role Minimization Optimization Algorithm (RMOA) to depict RBAC systems as conceptual lattices, thereby capturing interdependencies and intrinsic hierarchies among users, roles, and permissions. This method aims to minimize the number of roles while efficiently assigning responsibilities. By leveraging the Role Minimization Optimization Algorithm (RMOA) based on formal concept analysis, the approach effectively addresses the complexities of dynamic environments and enhances access control accuracy and efficiency. Key findings include the superiority of supervised learning over traditional association mining and unsupervised learning methods in handling discrete data and generating accurate access control rules. Comparative analysis demonstrates the superiority of supervised learning in generating accurate access control rules over other approaches. Future research should focus on scalability, real-world application, and enhancing the security of ML-based RBAC systems to fully realise the potential of these innovations in dynamic environments.
This paper presents an approach that aims at personalizing Web services composition and provisioning using context. Composition addresses the situation of a user's request that cannot be satisfied by any available service, and thus requires the combination of several Web services. Provisioning focuses on the deployment of Web services according to users' preferences. A Web service is an accessible application that other applications and humans can discover and trigger. Context is the information that characterizes the interactions between humans, applications, and the surrounding environment. Web services are subject to personalization if there is a need of accommodating users' preferences during service performance and outcome delivery. To be able to track personalization in terms of what happened, what is happening, and what might happen three types of context are devised, and they are referred to as user-, Web service-, and resource-context.
Offering personalized services through dynamically formed ecosystems is essential to personal wellness management. In this paper, we present the design of a cloud-enabled platform to facilitate the collection and delivery of evidence for personalization in a multi-provider ecosystem environment. In addition, the platform also provides essential building blocks of personalization services: smarter analytics for active personalization and dynamic provisioning. While the former common service takes charge of inferring user wellness risks from multiple data sources on the fly and making risk-driven recommendations, the latter common service determines optimal platform pricing and resource allocation given the constraint of acceptable quality of service.
Cloud computing has become a hot topic in the IT industry. Great efforts have been made to establish cloud computing platforms for enterprise users, mostly small businesses. However, there are few researches about the impact of cloud computing over individual users. In this paper we focus on how to provide personalized services for individual users in the cloud environment. We argue that a personalized cloud service shall compose of two parts. The client side program records user activities on personal de-vices such as PC. Besides that, the user model is also computed on the client side to avoid server overhead. The cloud side program fetches the user model periodically and adjusts its results accordingly. We build a personalized cloud data search engine prototype to prove our idea.
Collaborative recommender systems allow personalization for e-commerce by exploiting similarities and dissimilarities among customers' preferences. We investigate the use of association rule mining as an underlying technology for collaborative recommender systems. Association rules have been used with success in other domains. However, most currently existing association rule mining algorithms were designed with market basket analysis in mind. Such algorithms are inefficient for collaborative recommendation because they mine many rules that are not relevant to a given user. Also, it is necessary to specify the minimum support of the mined rules in advance, often leading to either too many or too few rules; this negatively impacts the performance of the overall system. We describe a collaborative recommendation technique based on a new algorithm specifically designed to mine association rules for this purpose. Our algorithm does not require the minimum support to be specified in advance. Rather, a target range is given for the number of rules, and the algorithm adjusts the minimum support for each user in order to obtain a ruleset whose size is in the desired range. Rules are mined for a specific target user, reducing the time required for the mining process. We employ associations between users as well as associations between items in making recommendations. Experimental evaluation of a system based on our algorithm reveals performance that is significantly better than that of traditional correlation-based approaches.
The demand of software as a service (SaaS)-based services delivering computing resources as on-demand software is on the rise in the IT industry. However, one of the drawbacks of the existing SaaS services is that they offer limited or no personalization of the services provided according to the users profile. Personalization as mentioned in the literature has been a key driver in the adoption and usage of various applications and in providing better service experience to the users. However, overwhelming majority of such personalized services rely extensively on the server side, without embracing fast-developing client-side technologies. In SaaS-based cloud services, utilizing this technology is necessary considering their limited processing specifications. Approaches have been proposed in the literature that focus on cloud-based personalization using client-side technologies but none of them actually address all the different components that are required for a scalable and holistic personalization framework for SaaS-based cloud services. In this paper, we address this drawback by proposing a user-focussed personalization framework. Our proposed framework takes advantage of powerful client side browsers to reduce server overheads, ameliorate performance, establish high intelligence and enrich data processing. To validate and demonstrate the applicability of our framework, we build a prototype model and compare its performance against existing approaches using different metrics. Copyright © 2014 John Wiley & Sons, Ltd.
There is an increasing need for various e-service, e-commerce and e-business sites to provide personalized recommendations to on-line customers. This paper proposes a new type of personalized recommendation agents called fuzzy cognitive agents. Fuzzy cognitive agents are designed to give personalized suggestions based on the user’s current personal preferences, other user’s common preferences, and expert’s domain knowledge. Fuzzy cognitive agents are able to represent knowledge via extended fuzzy cognitive maps, to learn users’ preferences from most recent cases and to help customers make inferences and decisions through numeric computation instead of symbolic and logic deduction. A case study is included to illustrate how personalized recommendations are made by fuzzy cognitive agents in e-commerce sites. The case study demonstrates that the fuzzy cognitive agent is both flexible and effective in supporting e-commerce applications.
As the world becomes a highly interconnected place, there is an increasing demand for providing ubiquitous access to content and services over a wide variety of platforms and devices. There is an increasing need to provide rich, intelligent and personalized services to the user irrespective of his location or method of access. There is a need to be connected and have access to resources and services round the clock. All of the above requirements need to be fulfilled in a seamless and scalable fashion in order to support a rich user experience. The cloud computing paradigm has been proposed as the next generation infrastructure to support a highly scalable service oriented architecture to achieve these goals. Providing personalized content recommendation services that recommend content relevant to the user irrespective of his location or access device is an important step in improving user experience. In this paper we investigate the use of a context aware recommendation service based on the cloud model.
Building composite Web services can save significant time and cost for developing new applications and enhancing the interoperability and collaboration among E-business partners. This article proposes a framework for dynamic and personalized composition of Web services using an approach that integrates not only functional attributes of Web services, but also nonfunctional attributes such as service requirements, quality of service, and the preferences and constraints of individual service consumers. A prototype system based on the proposed framework and some implementation details are also presented.
Traditional electronic program guides (EPGs) cannot be used to find popular TV programs. A personalized digital video broadcasting-terrestrial (DVB-T) digital TV program recommendation system is ideal for providing TV program suggestions based on statistics results obtained from analyzing large-scale data. The frequency and duration of the programs that users have watched are collected and weighted by data mining techniques. A large dataset produces results that best represent a viewer’s preferences of TV programs in a specific area. To process such a massive amount of viewer preference data, the bottleneck of scalability and computing power must be removed. In this paper, an architecture for a TV program recommendation system based on cloud computing and a map-reduce framework, the map-reduce version of k-means and the k-nearest neighbor (kNN) algorithm, is introduced and applied. The proposed architecture provides a scalable and powerful backend to support the demand of large-scale data processing for a program recommendation system.
Traditional collaborative filtering generates recommendations for the active user based solely on ratings of items by other users. However, most businesses today have item ontologies that provide a useful source of content descriptors that can be used to enhance the quality of recommendations generated. In this article, we present a novel approach to integrating user rating vectors with an item ontology to generate recommendations. The approach is novel in measuring similarity between users in that it first derives factors, referred to as impacts , driving the observed user behavior and then uses these factors within the similarity computation. In doing so, a more comprehensive user model is learned that is sensitive to the context of the user visit.
An evaluation of our recommendation algorithm was carried out using data from an online retailer of movies with over 94,000 movies, 44,000 actors, and 10,000 directors within the item knowledge base. The evaluation showed a statistically significant improvement in the prediction accuracy over traditional collaborative filtering. Additionally, the algorithm was shown to generate recommendations for visitors that belong to sparse sections of the user space, areas where traditional collaborative filtering would generally fail to generate accurate recommendations.
Dynamic composition of atomic services to create composite Web service is the main problem of the Web service creation. Selecting Web services with respect to user preferences is another problem. This paper addresses the problem of selecting user preferred service for dynamic composition. The solution is based on emerging semantic Web service technologies. We have proposed algorithm which is optimized to select Web services on user preferences
Enterprise data integration towards web service personalization
- M Rahman
- H Longe
- O Abass
- F Ahmad
M. Rahman, H. Longe, O. Abass, F. Ahmad, Enterprise data integration towards web service
personalization, in: Computer and Electrical Engineering, 2008. ICCEE 2008. International Conference on,
2008, pp. 715-720.