Enterprise Users Research Articles

Factors Influencing Intention to Adopt Generative AI Tools in Indonesian Enterprise Users

Indonesian enterprises are in the early stage of adopting generative AI tools. A report from Forbes shows that AI companies around the world have raised around $354 billion for Generative AI technology. One of the key drivers of those funding is to capitalize on the growing market demand. Market demand in Indonesia for Generative AI technology is explored by studying the intention to adopt such tools among enterprise users. Quantitative study reveals that perceived ease of use is a factor influencing the user’s intention to adopt Generative AI Tools in Indonesian enterprises. Recommendations for further research includes exploring more predictive factors and reaching broader target audiences for the study.

Application of machine learning optimization in cloud computing resource scheduling and management

In recent years, cloud computing has been widely used. Cloud computing refers to the centralized computing resources, users through the access to the centralized resources to complete the calculation, the cloud computing center will return the results of the program processing to the user. Cloud computing is not only for individual users, but also for enterprise users. By purchasing a cloud server, users do not have to buy a large number of computers, saving computing costs. According to a report by China Economic News Network, the scale of cloud computing in China has reached 209.1 billion yuan.Rational allocation of resources plays a crucial role in cloud computing. In the resource allocation of cloud computing, the cloud computing center has limited cloud resources, and users arrive in sequence. Each user requests the cloud computing center to use a certain number of cloud resources at a specific time.

UNIFYING ENTERPRISE USER EXPERIENCE: PEGA COSMOS AND ENTERPRISE SUBSTANCES INTEGRATION

Pega, renowned for its low-code platform, introduces an innovative approach to transform enterprise user experiences. This article delves into the integration of Pega Cosmos and Enterprise Substances, offering insights into the Center-Out methodology and its impact on scalability. This paradigm shift toward a unified user experience not only addresses challenges in traditional enterprise applications but also aligns with sustainability goals. KEYWORDS: Pega Cosmos, Software Ecosystem, Enterprise Substances, Low-code platform, Center-Out methodology, Unified user experience, Theme Cosmos

An Enterprise Service Demand Classification Method Based on One-Dimensional Convolutional Neural Network with Cross-Entropy Loss and Enterprise Portrait.

To address the diverse needs of enterprise users and the cold-start issue of recommendation system, this paper proposes a quality-service demand classification method-1D-CNN-CrossEntorpyLoss, based on cross-entropy loss and one-dimensional convolutional neural network (1D-CNN) with the comprehensive enterprise quality portrait labels. The main idea of 1D-CNN-CrossEntorpyLoss is to use cross-entropy to minimize the loss of 1D-CNN model and enhance the performance of the enterprise quality-service demand classification. The transaction data of the enterprise quality-service platform are selected as the data source. Finally, the performance of 1D-CNN-CrossEntorpyLoss is compared with XGBoost, SVM, and logistic regression models. From the experimental results, it can be found that 1D-CNN-CrossEntorpyLoss has the best classification results with an accuracy of 72.44%. In addition, compared to the results without the enterprise-quality portrait, the enterprise-quality portrait improves the accuracy and recall of 1D-CNN-CrossEntorpyLoss model. It is also verified that the enterprise-quality portrait can further improve the classification ability of enterprise quality-service demand, and 1D-CNN-CrossEntorpyLoss is better than other classification methods, which can improve the precision service of the comprehensive quality service platform for MSMEs.

MakeTwin: A reference architecture for digital twin software platform

Digital twin is currently undergoing a significant transformation from the conceptual and theoretical research phase to the implementation and application phase. However, a universally adaptable research and development platform for digital twin is critically needed to meet the development requirements. Specifically, a publicly accessible simulation, testing, and validation platform which can support digital twin model building, data processing, algorithm design, configuration, etc., is urgently required for researchers. Furthermore, for developers from the industry, a low-code development platform that can offer a customizable suite of functions such as model creation, data management, protocol configuration, and visualization is urgently needed. Meanwhile, for enterprise users, there is a lack of an application management platform that can be configured and migrated for various application scenarios, functions, and modes. Therefore, based on the system research of digital twin theories and key technologies by the authors (such as the five-dimension digital twin model, digital twin modeling and digital twin data theory, digital twin standards, and so on), a digital twin software platform reference architecture, namely makeTwin, is proposed and designed, as well as its ten core functions. The workflow of the makeTwin and the interaction mechanisms among its core functions are described. Finally, a digital twin application system for a chemical fiber textile shop floor (CFTS) which was developed according to makeTwin, is introduced, which validates the proposed reference architecture.

How to Enhance Corporate Customers’ Loyalty on Purchasing Service Products? Exploring from the Perspective of Information Search

Building upon the literature’s contribution to organizational procurement, enterprises must confront challenges derived from the uncertainty of collective decision-making and increasing efforts to scan for relevant information among providers. Especially for service products that cannot be tried in advance, how should corporate customers determine the price/performance of products before purchasing? This study explores ways to overcome the effect of these vulnerabilities and advance the quality of service and the loyalty framework to enrich the information processing theory. The results indicate that perceived value and relationship quality have greater effects on loyalty than service quality. Thus, if service providers can maintain amicable relationships and interactions with enterprise users, this will help the vendor provide enterprise users with appropriate product information in their service process, increasing the enterprise users’ loyalty. This study also proposes practical implications for the research results above, providing the information service industry with feasible service model suggestions and maintaining a sustainable competitive advantage. This study addresses relevant challenges faced by practitioners and provides managerial guidance to strengthen customer loyalty.

EAM: Enhanced authentication method to ensure the authenticity and integrity of the data in VM migration to the cloud environment

Cloud is a prominent technology today to provide computing resources to users. In previous days, industries or enterprise users are maintaining their data on-premises. Therefore, it creates many management issues in the industries. Cloud gives solutions to industries to maintain their data in the cloud data center. As a result, many industries are outsourcing their data to the cloud. When outsourcing, the data are migrated along with Virtual Machines (VM). During the migration, the data are vulnerable to attack. As a result, the data may tamper with fault content by the adversarial. Therefore, it is necessary to maintain data authenticity and integrity verification during the migration. This paper proposes an authentication mechanism to verify the data authenticity when migrated from on-premises to off-premises. The paper proposes a novel procedure to migrate the data in the virtual machine. After migration, the data is verified for authenticity using the proposed mechanism. An enhanced hashing procedure is proposed in the paper to verify the data authenticity. The proposed authentication mechanism is simulated in the cloud environment, and results are given in the tables and graphs. The results show that the EAM efficiently provides authentication and integrity of data migrated from on-premises to the cloud data center.

Energy Efficiency Assessment of Industrial Users Based on Improved TOPSIS Grey Correlation Analysis

In order to improve the energy efficiency of industrial users and evaluate the energy efficiency level before optimizing the energy efficiency of enterprises, an energy efficiency evaluation method based on improved TOPSIS grey relational analysis was proposed to find the weak links in energy use. Firstly, three indexes of energy stability, energy consumption per unit output and carbon emission per unit output are selected to construct the energy efficiency index system. Secondly, the index weights are calculated by entropy weight method, and the energy efficiency levels of industrial users are classified, which can be used by the power grid to implement differentiated electricity prices for high-energy consumption enterprises. Last example city 10 typical energy-intensive enterprise users used improved TOPSIS grey correlation analysis method for evaluation of energy efficiency, energy consumption per unit output value is obtained by analysis of the index weight is the largest, the better the performance of the index, the higher the comprehensive evaluation results of energy efficiency, and finally the method compared with other evaluation methods to verify the effectiveness of the proposed method in this paper.

Research on the Establishment, Operation and Management of "Farm Helper" APP

The poverty problem in China has an important connection with the precision sales of agricultural products. We are committed to providing farmers with all-round help, taking "farmers' income increase, agricultural growth, and rural stability" as our mission, and comprehensively helping farmers develop and become rich. Therefore, our team has developed a central APP called "Agricultural Helper", which provides a safe, reliable and convenient platform for farmers to sell agricultural products. Our platform eliminates many redundant steps and directly contacts with enterprises such as fruit processing plants and canning plants. The platform also provides a personal to personal marketing model to further simplify the transportation of agricultural products and ensure the quality of agricultural products, such marketing mode can reduce the purchase cost of enterprise users and individual users. This paper introduces the main functions, products and services of the "agricultural helper" APP, analyzes its marketing strategies, and analyzes its risk types and countermeasures.

Drivers of Artificial Intelligence and Their Effects on Supply Chain Resilience and Performance: An Empirical Analysis on an Emerging Market

The global supply chain has suffered an unprecedented impact Affected by multiple factors such as anti-globalization, rising trade protectionism and the COVID-19 pandemic. Based on the technology-organization-environment framework and resource-based theory, this study attempts to explore and analyze what drives a firm’s willingness to adopt artificial intelligence technology and how such willingess to adopt artificial intelligence technology may contribute to supply chain resilience and supply chain performance. Using survey data collected from 318 firms in China, we empirically test our arguments and hypotheses through the structural equation modeling approach. The results suggest that the relative advantages of enterprise artificial intelligence technology, supply chain collaboration, and environmental uncertainty are the three major factors affecting the adoption of artificial intelligence technology, which subsequently provide a positive impact on supply chain resilience and supply chain performance. This study expands the application field and scope of artificial intelligence technology, fills the relatively large gap in the research on the behavior of enterprise users adopting artificial intelligence technology in the supply chain field. This provides a useful reference for enterprises to adopt artificial intelligence technology.

S&T Innovation Platform Sharing Service Contract Mechanism to Achieve Supply Chain Resilience

While achieving fruitful patents in innovation, enterprises can face bottlenecks in industrial transformation. The fundamental cause of such difficulties is the lack of pilot equipment. To this end, the science and technology (S&T) innovation platform introduces equipment sharing to solve the problems in transforming enterprise patents. Based on the premise that service demand is endogenous to platform service effort and user relationship resilience, this paper introduces revenue-sharing and cost-sharing contract mechanisms. It constructs a Stackelberg game model between S&T innovation platforms and enterprise users. Further, we explore the decision-making optimization involving platform service pricing, service effort, and a user’s relationship resilience. Our main findings are: (1) service pricing and relationship resilience show supermodularity to the platform revenue while showing submodularity to the user revenue. (2) The optimal user relationship resilience always indicates a decreasing trend in the pricing of platform services. (3) The platform and users have their preferences for contract types. When the platform dominates the game, they tend to adopt a revenue-sharing contract. When the users dominate, they are more willing to implement a cost-sharing contract. (4) As the S&T innovation platform strengthens the connection between the platform and users through the revenue- and cost-sharing contracts, it further enhances the supply chain collaboration among equipment suppliers, technology innovation platforms, and users, thereby achieving the purpose of improving supply chain sustainability and resilience. Technological innovation is an essential means to improve supply chain sustainability and resilience.

A Refined Supervision Model of Rice Supply Chain Based on Multi-Blockchain.

With the development of Agriculture 4.0, the requirements for sustainable agriculture and precision agriculture continue to grow. As one of the three major staple foods globally, the quality and safety of rice affect human health as well as social development. To ensure the quality and safety of rice and reduce the flow of problematic rice, a multi-layer blockchain-based rice supply chain refinement supervision model MBRRSM (Multi-blockchain Rice Refined Supervision Model) is proposed from the information level. First, the characteristics of information flow in the rice supply chain are analyzed, and a classification table of key information is constructed. Second, the MBRRSM framework is designed. Based on a multi-party hybrid encryption algorithm, secure multi-party computing algorithm, multi-mode storage mechanism, and SPOP (Supervision Proof of Peers) consensus algorithm, a set of mechanisms is designed for the transmission, use, storage, and consensus of rice data in MBRRSM. Subsequently, the security and performance capabilities of MBRRSM are analyzed. Meanwhile, the SPOP consensus algorithm is analyzed. Finally, a prototype system is built based on MBRRSM and verified through exemplary scenarios in different usage situations. The results show that research on the refined supervision of the rice supply chain based on multi-blockchain can finely supervise all types of data in the rice supply chain, and provide a guarantee for enterprise users to safely transmit and use data with different privacy levels. This study presents a unique research paradigm that introduces the theories and methods of the new information field generation into the field of agricultural research, and thus assists in the strategy implementation of “holding grain in the land and storing grain in technology”.

Scheme Design of Structured Process Data Management System Base on Android

The traditional paper process regulation form wastes a lot of resources and has low circulation efficiency, which is difficult to promote the digital transformation of enterprises. Based on Teamcenter platform, Process knowledge base and bill of processes can be built. Extract and summarize structured process information through Teamcenter and output process regulation form. On this basis, this paper develops an Android Application of process data management, which transmits the electronic process regulation form to the Android Application of process data management through JSON format in the internal LAN of the enterprise, to guide the operation and production. Verified by enterprise users, the process management collaboration platform improves the enterprise business flow, process preparation efficiency and process knowledge reuse rate, reduces many processes data redundancy, and lays a foundation for the digital transformation of enterprise process data management.

Cost-Effective Web Application Replication and Deployment in Multi-Cloud Environment

Multi-cloud is becoming a popular cloud ecosystem because it allows enterprise users to share the workload across multiple cloud service providers to achieve high-quality services with lower operation cost and higher application resilience. In multi-cloud, cloud services are widely distributed at different locations with differentiated prices. Therefore, Web application providers face the challenge to select proper cloud services for application replication and deployment with the aim of minimizing the deployment cost. Meanwhile, the deployed application replicas must satisfy the constraint on request response time to maintain the quality of user experience. To meet the two major requirements, this article studies a new problem of Web application replication and deployment in multi-cloud (WARDMC) that jointly considers both the cost minimization and constraints on average response time, including particularly request processing time and network latency. To address the problem, we develop a new approach named MCApp. MCApp combines iterative mixed integer linear programming with domain-tailored large neighborhood search to optimize both application replicas deployment and user requests dispatching. Extensive experiments using the real-world datasets demonstrate that MCApp significantly outperforms several recently proposed approaches.

Design and Optimized Application of Online Financial Management System Based on Internet of Things

With the rapid development of science and technology, the requirements for the use of financial management systems required by users are also constantly improving; therefore, the financial management system is updated rapidly, and a single financial management mode can no longer meet the needs of users’ development. Designing a management system integrating business and finance can effectively maintain the stability of user’s (enterprises) development. Besides, all aspects of the designed system should be carefully divided and upgraded to adapt to the complex internal operation mode of some enterprise users. This article is mainly based on the use of Internet of Things technology to design a more optimized online financial management system so that its users can have more convenient and accurate access to or processing of certain information. This is to build an online financial management system based on the blockchain technology of the Internet of Things, complex network information transmission detection technology, and multiplexed information transmission technology and to expand the application scope of its functions by building databases, financial information systems, and financial analysis systems. By comparing and analyzing the system constructed in this article with the system proposed by Zhang Yuzhuo earlier, which is suitable for the strategic integration development of his enterprise, we can find that this system has advantages in reliability, simultaneity, and delay, that is, data transmission efficiency. This shows that the online financial management system designed in this paper can be carried out normally in actual activities and has certain advantages.

Research on the design of intelligent virtual simulation experimental teaching platform based on augmented reality

This research developed a product virtual simulation system based on augmented reality technology. The special database of product components was established, and the augmented reality product database of rice cooker was constructed and modified by the user and the enterprise design and production database. The HoloLens head-mounted device and MR Hand-eye interaction technology were used to realize the real-time simulation of the running state of the product design, and the prototype system was assembled. The Unity3D engine adopts the target method based on special features for the 3D display of products, which is a display environment with high requirements for user experience. The experimental simulation platform system has the advantages of strong portability, strong expansibility, realistic rendering effect and high program execution efficiency, which provides a good 3D panoramic display experience and usability experience for students' experiments and enterprise users after future industrialization.

Proactive and intelligent evaluation of big data queries in edge clouds with materialized views

The rise of big data brings extraordinary benefits and opportunities to businesses and governments. Enterprise users can analyze their consumers’ data and infer the business value, such as purchasing goods correlations and customer preferences. To unveil such hidden values from big data, various big data processing frameworks, such as Hadoop and Tensor-flow, are developed. One fundamental approach of accelerating such big data processing frameworks is to efficiently evaluate queries for big data analytics based on materialization of intermediate results. In this paper, we consider problems of dynamic and proactive QoS-aware query evaluation of big data analytics with intermediate result materialization in a mobile edge cloud. We propose a one-shot online algorithm for the dynamic QoS-aware big data query evaluation within a finite time horizon, which can intelligently determine whether some immediate results during query evaluation need to be materialized for later use of other queries, by making use of the Reinforcement Learning (RL) method with predictions. We also devise an online learning algorithm based on the technique of multi-armed bandits, for the proactive QoS-aware big data query evaluation problem with resource reservations, where the query arrival information and their required datasets are uncertain before the actual evaluation of the queries. We finally investigate the performance of the proposed algorithms by experimental simulations, and results show that the performance of the proposed algorithms is promising, by achieving 12% higher system throughput while reducing 50% and 40% average evaluation cost and time per query compared to the comparison benchmarks.

Design of the School-Enterprise Cooperation Management Information Platform Based on the B/S Architecture

The SEC teaching management platform provides a good support for school-enterprise cooperation. This paper conducts research on the SEC management information platform based on the B/S architecture. On the basis of the analysis of the relevant functional needs of the system, the overall functional framework of the system includes the college management platform, enterprise management platform, resource-sharing platform, and user login platform, then the B/S architecture is used to develop the SEC teaching management platform, and the test experiments of the SEC teaching management platform are carried out. The maximum number of concurrent tests shows that the maximum number of concurrencies of the platform is 989. Through the system response time test, the system response time is between 0.2 and 0.45, which meets the system response time requirements. In the CPU use test, the CPU share of the system was between 25% and 31%, meeting the needs of the system. From the above experimental results, the system is relatively high.

A Stackelberg Game Approach toward Migration of Enterprise Applications to the Cloud

With the development of cloud computing, more and more cloud resources are rented or purchased by users. Using an economics approach to achieve cloud resource management has been thought of as a good choice for an enterprise user to complete an application’s migration and deployment into the public cloud. During an application’s migration process, it is important but very challenging to achieve the satisfaction of both the enterprise user and the public cloud provider at the same time. In this paper, we apply an economics approach to investigate the migration optimization problem during the migration process of applications from the enterprise user’s data center to the remote public cloud. We consider the application migration time of the enterprise user and the energy consumption of physical machines, and establish a single static round optimization problem for both the enterprise user and the cloud provider on the premise of satisfying the quality of experience (QoE) based on the Stackelberg game, where the public cloud provider is leader and the enterprise user is follower. Then we propose a novel algorithm to find the optimal physical machine placement for application migration. After that, we further consider that an enterprise user needs to migrate several applications, and extend the single-round static game to the multi-round dynamic game, where the energy consumption costs of the physical machines are reduced by adjusting the states of the physical machines in each round. We finally illustrate the performance of our scheme through some simulation results.

Performance of Micro-Scale Transmission & Reception Diversity Schemes in High Throughput Satellite Communication Networks

The use of Ka and Q/V bands could be a promising solution in order to accommodate higher data rate, interactive services; however, at these frequency bands signal attenuation due to the various atmospheric phenomena and more particularly due to rain could constitute a serious limiting factor in system performance and availability. To alleviate this possible barrier, short- and large-scale diversity schemes have been proposed and examined in the past; in this paper a micro-scale site diversity system is evaluated in terms of capacity gain using rain attenuation time series generated using the Synthetic Storm Technique (SST). Input to the SST was 4 years of experimental rainfall data from two stations with a separation distance of 386 m at the National Technical University of Athens (NTUA) campus in Athens, Greece. Additionally, a novel multi-dimensional synthesizer based on Gaussian Copulas parameterized for the case of multiple-site micro-scale diversity systems is presented and evaluated. In all examined scenarios a significant capacity gain can be observed, thus proving that micro-scale site diversity systems could be a viable choice for enterprise users to increase the achievable data rates and improve the availability of their links.