SaaS Service Research Articles

Web-платформа для організації хмарних обчислень у нейрофізіологічних дослідженнях на основі даних айтрекінгу

The purpose of the work is to develop the architecture and web version of the software complex based on the proposed new concept of cloud computing organization, which allows to expand the diagnostic capabilities of the tools of model-oriented information technology for assessing the neurophysiological state of a person using methods of nonlinear dynamic identification of the oculomotor system based on eye tracking data. The concept of cloud computing is proposed, which is based on the combination of PaaS and SaaS services as part of the developed software complex, due to which the cross-platform nature of cloud computing is ensured, the productivity and efficiency of scientific research increases. The developed architecture allows you to easily expand the functionality of the software complex and adapt it to different application conditions. The key feature of the complex is its undemanding hardware on the client side thanks to the use of cloud computing and its modular structure, which allows for easy scaling of the complex, as well as the isolation of the script code execution process in the cloud computing environment to increase the level of security when interpreting the script code on the server . Compared to other similar services, the complex has several advantages: it provides effective work in research and educational applications, supports Python and JavaScript programming languages, and also allows the use of software-implemented identification methods through specially developed GUI interfaces. In addition, the complex offers social opportunities and a high level of abstraction, which allows to optimize the research process.

Lifetime deals: the role of software service offering in convincing consumers

Purpose This study aims to reveal consumer purchase intentions for Software-as-a-Service (SaaS) lifetime deals and the role of service offerings in shaping these intentions. Design/methodology/approach Lifetime deals − an aggressive market penetration strategy − have the potential to allow startups to gain market share, user base and the cash necessary for growth. However, startups need to mitigate consumer concerns for which service offering design plays a key role. Drawing on expectancy-value and signaling theories, this study developed a research model and then conducted empirical research on 2,173 consumers via choice-based conjoint analysis to reveal the critical service offering attributes for consumer utility in lifetime deals in the SaaS presentation tool market context. After using the hierarchical Bayes model to derive each respondent’s part-worth utilities for service offering attributes, the hypotheses were tested via the factor score regression method. Findings The results show that the service offering attributes of low price, refund option, human support and feature updates enhance consumer utility in SaaS lifetime deals. Three of these four attributes, namely, low price, refund option and feature updates, enhance consumers' purchase intentions by reducing their concerns about the service’s performance, seller and lifespan, respectively. Originality/value This study elucidates consumer purchase intentions for SaaS services in digital marketplaces. By investigating a widespread market entry strategy − lifetime deals − it shows consumer preferences and behavior for these deals in the fast-growing online tools market. This study also shows how startups can use lifetime deals through a well-designed service offering to mitigate various consumer concerns.

Grey relational analysis-driven optimization of neutrosophic MAGDM for cloud SaaS credibility evaluation

In the basic cloud service delivery model, since SaaS (Software as a Service) is located at the application layer, if a relatively complete cloud service credibility comprehensive evaluation framework can be established from the perspective of SaaS cloud service consumers and combined with their trust context, it can not only effectively solve the trust problem between cloud service consumers (CSC) and cloud service provider (CSP), but also has very important practical significance for the popularization and promotion of SaaS service applications. The credibility evaluation of SaaS services in cloud computing environment is often considered as a multi-attribute group decision making (MAGDM) problem. In this paper, the grey relational analysis (GRA) method is extended to the single-valued neutrosophic sets (SVNSs) setting to deal with MAGDM with incomplete weight information. First, the SVNSs are reviewed. In addition, the single-valued neutrosophic number GRA (SVNN-GRA) is established for MAGDM with incomplete weight information, and the computational steps for all designs are listed. Finally, the credibility evaluation of SaaS services in cloud computing environment is given to demonstrate the SVNN-GRA model and some comparative analysis is done to demonstrate the SVNN-GRA.

Improvement of organization and automation of commercial enterprise electronic money accounting in conditions of economy digitalization

In the conditions of digitization of the globalized world economy, Ukrainian enterprises have an informational request for methodical recommendations regarding the technique of conducting and computerized accounting of electronic money payments, which are an innovative form of settlement transactions. The purpose of the study was to form actual methodological recommendations regarding the organization of automated accounting of payments in electronic money at the enterprise, as well as to justify the choice of the necessary software product for this purpose. To achieve the goal, the method of morphological analysis, comparative and critical analysis, system-structural approach, bibliographic method was used. Ukrainian enterprises are suggested to implement accounting work with electronic money in two successive steps. The transformations that will take place in the accounting work for settlement transactions of the enterprise in the case of the introduction of national digital currencies are substantiated. The technological possibilities of using neural networks for processing arrays of data regarding electronic money calculations carried out in payment systems are explained. It has been proven that, according to the forecasted trends, it is expedient for Ukrainian enterprises to switch from local accounting software products to the use of cloud services. Functional capabilities of 12 cloud-based software solutions popular in Ukraine for automating accounting and analytical work were analysed, from the point of view of the possibility of computerized accounting of settlement operations in electronic money. The advantages and disadvantages of individual SaaS services are explained, and the expedient ones are recommended for use. Recommendations were made to adjust the Order on accounting policy, following the scheme of organizational and procedural measures developed in the work. It is proposed to assess the compliance of the existing accounting software product with management information requests that arise when using electronic money to make payments. The recommendations were developed on the basis of a detailed study of the prospects for the further popularization of electronic money. The study is of practical interest for Ukrainian enterprises that involve in their business innovative payment solutions in the conditions of rapid digitalization of the economy

Assessing the Impact of SaaS Quality on Telecom Customer Loyalty: A Social Capital Theory Approach

Purpose: The main purpose of this research is to empirically draw on social capital theory to examine SaaS post-adoption in relation to the clients’ intentions to explore new SaaS service features in order to be loyal clients to the Egyptian telecom operators. Design/methodology/approach: The methodology is based on quantitative approach using a questionnaire tool to gather required data in which a selection of customers from the four Egyptian telecommunication companies have been invited to participate. Structural equation modeling analyses (SEM) using AMOS software version 25 used to analyze data gathered. Findings: The findings of this study included the direct effect between SaaS service quality and customer loyalty is statistically significant, the direct effect between SaaS service quality and social capital is statistically significant, the direct effect between social capital and customer loyalty is statistically significant, and there is partial mediation effect of the social capital in the relationship between SaaS service quality and customer loyalty. Research limitations: The research is undertaken on a single industry and therefore is not necessarily representative of other industries. This research relied on self-reported data from participants in a cross-sectional sample. Empirical implications: Telecom operators should think about strategic initiatives to strengthen the related service quality aspects in order to sustain or even increase their customer loyalty.

SaaS in digital ecosystem of smart priorities for transport and logistics

The objective of the article is to provide an analytical overview of the various aspects of the development and implementation of Saas services in the development of a digital ecosystem of smart priorities for transport and logistics as of March 2023, including an analysis of the industry needs for their further development and effectiveness of their use in the context of new geopolitical challenges. Methods: The work applies the method of analytical review of the state of the Russian market of SaaS technologies, the peculiarities of the development of national digital services in the field of logistics, and ways to improve the efficiency of their application.

A multi-dimensional extensible cloud-native service stack for enterprises

With the widespread acceptance of the cloud-native concept and the emergence of a large number of dedicated cloud-native applications, the service stacks of cloud-native applications have received extensive attention in the industry. To analyze the extensibility problems of service stacks, a cloud-native light-cone model is proposed, which focuses on the dimensions of application, infrastructure, tenant and workflow, and provides a perspective view that reflects the concerns of stakeholders. Based on this model, various challenges in designing extensible cloud-native service stacks are identified by classification. To solve these challenges, a holistic architecture and a set of key technologies are designed, involving unified runtime abstraction, cluster bootstrapped creation, application-specific controllers, etc. Furthermore, the OMStack (Oriental Mind Stack) is implemented, which integrates these technologies and provides a group of PaaS and SaaS services for container cluster (OMCC), artificial intelligence (OMAI), big data (OMBD) and so on. Experimental analysis and production applications demonstrate the practicality, efficiency and reliability of the proposed architecture, stack and services.

Authorization Model for Securing Cloud SaaS Services (Netflix)

At the enterprise level, cloud software as a service (SaaS) is in high demand. As software as a service (SaaS) can be easily provided in a multitenant way, its execution assessment, information inactivity, and authorization are critical issues related to the cloud supplier at the time of providing administrations to sellers. Netflix is a mixed media supplier that gives online streaming from a library of movies and TV arrangement. Netflix services are exclusively based on cloud SaaS. The highlight of the study is to analyze the finest hones over secure cloud environment in authorization perspective based on behavioral biometrics. A conceptual authorization is displayed in this paper to secure cloud services, especially SaaS, when unauthorized access is supported by legitimate users. The use case is taken as Netflix.

SaaS Service Combinatorial Trustworthiness Measurement Method Based on Markov Theory and Cosine Similarity

With the rapid progress of information technology, cloud computing and cloud services are widely accepted and applied to all aspects of social life. In the cloud computing environment, SaaS (Software-as-a-Service) services have become the main form of software services. For SaaS services, evolutionary and iterative development methods have become the main methods of software system construction. For systems with high trustworthiness, the independent trustworthiness of each SaaS service has a great impact on the overall status. However, SaaS services with high independent trustworthiness do not always build highly trusted software systems. The combinatorial trustworthiness between SaaS services is as important as the independent trustworthiness of each SaaS service. This paper takes combinatorial trustworthiness between SaaS services as the research object. Combinatorial trustworthiness measurement method based on Markov and cosine similarity theory is proposed. The feasibility and effectiveness of the proposed method are verified through simulation experiments. Applicable scenarios, advantages, and disadvantages of the proposed method are shown through the comparison of different measurement methods. The proposed method provides theoretical and technical support for users to select SaaS services suitable for their application scenarios, build cloud service systems, and monitor the operation status of cloud service systems.

Интеграция предиктивно-аналитических моделей с IoT-платформой цифрового экомониторинга

The research focuses on the development of applied software systems for automated environmental monitoring. The task of developing and integrating applied software, in particular calculation and analytical models based on machine learning (ML) methods, with an IoT platform of digital eco-monitoring for industrial enterprises is considered. Such a platform is used to create software and hardware systems of CEMS – Continuous Emissions Monitoring System class, designed for continuous monitoring of pollutant emissions into the atmospheric air at production facilities. Use of ML tools integrated with the platform allows to expand significantly the functionality of the existing CEMS, in particular to quickly build new SaaS services for forecasting the dynamics of pollution distribution. Given the high requirements for industrial systems, there is a need to create a specialized software product – an analytical server that implements the management of connected predictive analytical ML models with the required level of service quality, including automatic initialization of new analytical scripts as classes, isolation of individual components, automatic recovery after failures, data security and safety. The paper proposes a scheme of functional and algorithmic interaction between the IoT platform of digital eco- monitoring and the analytical server. The proposed implementation of the analytical server has a hierarchical structure, at the top of which is an application capable of accepting high-level REST requests to initialize calculations in real time. This approach minimizes the impact of one analytical script (class) on another, as well as extending the functionality of the platform in "hot" mode, that is, without stopping or reloading. Results demonstrating automatic initialization and connection of basic ML models for predicting pollutant concentrations are presented.

Hybrid fuzzy clustering to improve services availability in P2P-based SaaS-cloud

Software as a Service is evolving as a leader model for cloud service delivery, enabling service providers to remotely deliver hosted, developed and managed software over the Internet. In parallel, some IT services are moving from traditional Internet services to cloud services based on peer-to-peer technologies. However, the P2P-based cloud is a large-scale, heterogeneous and highly dynamic environment whose performance is highly dependent on its ability to maintain persistent availability of SaaS services. In this paper, we propose an approach for improving SaaS service availability in order to meet service quality requirements and maintain performance in a P2P-Based cloud environment. It is mainly based on a new hybrid clustering mechanism that aims to provide a virtual and optimal infrastructure in order to organize the system peers into distinct clusters represented by virtual nodes forming together a virtual layer. This layer allows not only the distribution of peer providers but also the formation of condensed areas of each service of interest for a set of neighboring peers, which improve the availability probability of services in specific regions. In addition, a service availability measurement model was proposed based on the use of the system’s virtual layer taking into account different entities at different levels. The experimental results show that the proposed approach improves the probability of SaaS service availability and the reliability of the P2P-Cloud system. It responds mainly to the large-scale nature of distributed systems as well as making the best trade-off of maintaining QOS in terms of availability, performance and cost.

Ontology-Based Privacy Data Chain Disclosure Discovery Method for Big Data

To meet user's functional requirements, cloud computing and big data have become the most commonly used computing and data resources. Based on analysis, conversion, extraction and refinement for the big data, a disease can be prevented and group behavior can be predicted. However, each user's private data is also an element in big data. Users must provide private data to the service providers to meet their functional requirements. To gain economic benefits, some SaaS service providers have not been authorized to collect and analyze the user's sensitive private data, as a result, the user's private data is disclosed. In this paper, we propose a private data chain disclosure discovery method, to prevent a user's sensitive privacy information from being illegally disclosed. First, we measure the similarity degree and cost of the disclosure of the private data. Second, according to the similarity degree and cost of disclosure, the disclosure chain and key private data are detected in the process of interaction between user and SaaS service. Third, we propose a discovery framework for the private data chain and demonstrate its feasibility and effectiveness by experiments.

Towards Linguistic-based Evaluation System of Cloud Software as a Service (SaaS) Provider

Cloud Software as a Service (SaaS) is a type of delivering software application by using Cloud computing Infrastructure services. Cloud SaaS used the global Internet connection to offer its services to the client consumers. The selection of Cloud SaaS provider is based on the evaluation mechanism that the Cloud SaaS consumer manage before making the service contract. In this paper, the linguistic-based evaluation of Cloud SaaS quality attributes has been proposed to help the consumer to assess the service for optimal service selection. Our proposed approach has been developed by the combinations of fuzzy logic and TOPSIS MCDM methods. The proposed approach helps the Cloud SaaS consumer to select the optimal service Cloud SaaS service provider. The case study has been proposed in order to demonstrate the proposed approach.

Web-service promotion of SaaS service for mining design

The market of software for automating the processes of mining enterprises is represented by foreign solutions, which increases the dependence of Kazakhstani enterprises on third-party developers. The marketing strategy for promoting the domestic software product focus on conquering the market segment and increasing competitiveness.The study aims to develop a marketing strategy for promoting the national cloud service “3D-quarry” as a SaaS, taking into account the modern development of Kazakhstan and the globalization of the world’s economy. When writing the matrix of strategic analysis, an analysis of pricing models was used. The possibility of using various measures to promote the service was assessed. A marketing strategy for promoting software for subsoil use is proposed, including tactics of online and offline marketing. The choice of a pricing model for a SaaS product for specialized universities and small design organizations is substantiated. Within the SaaS framework, a set of indicators for evaluating the effectiveness of a marketing strategy is proposed. A comparative analysis of the payback period of the project when using different tariff plans is carried out. Planning horizon – 3 years, planned market share – 5%, conversion rate – 1%, expected payback period – 4.4 years.The developed pricing policy allows obtaining competitive advantages in comparison with the main representatives of the software market: in the segment of small design organizations – at the expense of an acceptable price, in the segment of educational institutions – at the expense of the freemium pricing model. Acknowledgment The study was carried out within the framework of the grant of the Ministry of Education and Science of the Republic of Kazakhstan on the topic IRN AP09561619 “Development of the core of the web service “3D-quarry”.

Imputing sentiment intensity for SaaS service quality aspects using T-nearest neighbors with correlation-weighted Euclidean distance

Imputing sentiment intensity for SaaS service quality aspects using T-nearest neighbors with correlation-weighted Euclidean distance

Secure mitigation and migration of virtual machines over hybrid cloud hypervisors infrastructure

As workloads increase in the industry so does the requirement for IT resources. IT companies are now shifting from in-house data centers to cloud-based IT services such as IAAS, PAAS, and SAAS which can offer flexibility at different levels to the developers of the applications working on different projects. Lots of researchers have contributed to migrating virtual machines migration from one cloud service provider to the other in case of backup as a service and other related services. Some of the previous works by some researchers focus on the migration of SAAS and PAAS onto different cloud service provider platforms. This research work focuses on the mitigation and migration of IAAS, PAAS, and SAAS services of the cloud. My contribution in this research will be in how to avoid loss of data during mitigation and migration of the VM from one Hypervisor environment to the other hypervisor environment on a physical machine infrastructure. This will help SME’s to provide end-user access to their data without worrying about the losses which may have incurred if the Mitigation process was not carried out carefully while migrating VM’s from one bare metal Hypervisor environment to the other bare metal Hypervisor environment.

A new temporal locality-based workload prediction approach for SaaS services in a cloud environment

As the paradigm shift toward Software as a Service (SaaS) continues to gain the interest of companies and the scientific community, performances must be optimal. Indeed, cloud providers must provide an optimal quality of service (QoS) for their users in order to survive in such a competitive cloud market. Workload forecasting techniques have been proposed in order to improve capacity planning, ensure efficient management of resources and, hence, maintain SLA contracts with end users. In this context, we propose a new approach to predict the number of requests arriving at a SaaS service in order to prepare the virtualized resources necessary to respond to user requests. The method will be implemented in order to simultaneously achieve a twofold benefit: obtain precise forecast results while optimizing response time. In this regard, we have chosen to control the computation time by dynamizing the size of the sliding window associated to the recent history to be analyzed, since the larger the size of the entry in the prediction model, the more the algorithmic complexity increases. Then, the prediction will be established based on the temporal locality principle and the dynamic assignment of weights to different data points in recent history. Moreover, the proposed method can be extended to cover other uses in prediction. Experiments were carried out to assess the performance of the proposed method using two real workload traces and compared to state-of-the-art methods. The proposed method offers a compromise between the execution time and the accuracy of the prediction.

Development of a cloud SaaS service for a comprehensive research of polymerization processes

The article is devoted to the description of the network architecture of the developed cloud SaaS service for the implementation of a comprehensive study of polymerization processes. The main problem of using local software systems for such tasks is briefly voiced. The main methods and mathematical approaches that allow solving the direct problem of predicting the main molecular characteristics of the produced polymer product for various loading modes of the reaction system and the inverse problem of identifying some of the unknown kinetic parameters are considered. Methods and approaches to the development of algorithms and software are considered separately. As part of the cloud service being developed, a three-tier architecture for accessing data and computing resources is described in detail, the main tasks of each level and the advantages of using this approach are described. The hardware and system requirements for the computing nodes of the developed network application have been written out.

Market-oriented online bi-objective service scheduling for pleasingly parallel jobs with variable resources in cloud environments

In this paper, we study the market-oriented online bi-objective service scheduling problem for pleasingly parallel jobs with variable resources in cloud environments, from the perspective of SaaS (Software-as-as-Service) providers who provide job-execution services. The main process of scheduling SaaS services in clouds is: a SaaS provider purchases cloud instances from IaaS providers to schedule end users’ jobs and charges users accordingly. This problem has several particular features, such as the job-oriented end users, the pleasingly parallel jobs with soft deadline constraints, the online settings, and the variable numbers of resources. For maximizing both the revenue and the user satisfaction rate, we design an online algorithm for SaaS providers to optimally purchase IaaS instances and schedule pleasingly parallel jobs. The proposed algorithm can achieve competitive objectives in polynomial run-time. The theoretical analysis and simulations based on real-world Google job traces as well as synthetic datasets validate the effectiveness and efficiency of our algorithm.

Software as a Service (SaaS) Cloud Computing: An Empirical Investigation on University Students’ Perception

Aim/Purpose: This study aims to propose and empirically validate a model and investigates the factors influencing acceptance and use of Software as a Services cloud computing services (SaaS) from individuals’ perspectives utilizing an integrative model of Theory of Planned Behavior (TPB) and Technology Acceptance Model (TAM) with modifications to suit the objective of the study. Background: Even though SaaS cloud computing services has gained the acceptance in its educational and technical aspects, it is still expanding constantly with emerging cloud technologies. Moreover, the individual as an end-user of this technology has not been given the ample attention pertaining to SaaS acceptance and adoption (AUSaaS). Additionally, the higher education sector needs to be probed regarding AUSaaS perception, not only from a managerial stance, but also the individual. Hence, further investigation in all aspects, including the human factor, deserves deeper inspection. Methodology: A quantitative approach with probability multi-stage sampling procedure conducted utilizing survey instrument distributed among students from three public Malaysian universities. The valid collected responses were 289 Bachelor’s degree students. The survey included the demographic part as well as the items to measure the constructs relationships hypothesized. Contribution: The empirical results disclosed the appropriateness of the integrated model in explaining the individual’s attitude (R2 = 57%), the behavior intention (R2 = 64%), and AUSaaS at the university settings (R2 = 50%). Also, the study offers valuable findings and examines new relationships that considered a theoretical contribution with proven empirical results. That is, the subjective norms effect on attitude and AUSaaS is adding empirical evidence of the model hypothesized. Knowing the significance of social effect is important in utilizing it to promote university products and SaaS applications – developed inside the university – through social media networks. Also, the direct effect of perceived usefulness on AUSaaS is another important theoretical contribution the SaaS service providers/higher education institutes should consider in promoting the usefulness of their products/services developed or offered to students/end-users. Additionally, the research contributes to the knowledge of the literature and is considered one of the leading studies on accepting SaaS services and applications as proliferation of studies focus on the general and broad concept of cloud computing. Furthermore, by integrating two theories (i.e., TPB and TAM), the study employed different factors in studying the perceptions towards the acceptance of SaaS services and applications: social factors (i.e., subjective norms), personal capabilities and capacities (i.e., perceived behavioral control), technological factors (i.e., perceived usefulness and perceived ease of use), and attitudinal factors. These factors are the strength of both theories and utilizing them is articulated to unveil the salient factors affecting the acceptance of SaaS services and applications. Findings: A statistically positive significant influence of the main TPB constructs with AUSaaS was revealed. Furthermore, subjective norms (SN) and perceived usefulness (PU) demonstrated prediction ability on AUSaaS. Also, SN proved a statically significant effect on attitude (ATT). Specifically, the main contributors of intention are PU, perceived ease of use, ATT, and perceived behavioral control. Also, the proposed framework is validated empirically and statistically. Recommendation for Researchers: The proposed model is highly recommended to be tested in different settings and cultures. Also, recruiting different respondents with different roles, occupations, and cultures would likely draw more insights of the results obtained in the current research and its generalizability Future Research: Participants from private universities or other educational institutes suggested in future work as the sample here focused only on public sector universities. The model included limited number of variables suggesting that it can be extended in future works with other constructs such as trialability, compatibility, security, risk, privacy, and self-efficacy. Comparison of different ethnic groups, ages, genders, or fields of study in future research would be invaluable to enhance the findings or reveal new insights. Replication of the study in different settings is encouraged.