Enterprise systems in Russia: 1992–2012

Kataev, Mikhail Yu.; Bulysheva, LarisaA.; Emelyanenko, AlexanderA.; Emelyanenko, V.A.

doi:10.1080/17517575.2012.745618

Cited by 36 publications

(11 citation statements)

References 7 publications

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“…ERP application services are used to integrate the resources in an enterprise and can integrate the internal and external information across the entire organization, including finance, manufacturing, marketing and service, customer relationship management, and so on (Xu, 2011;Wang et al, 2012;Yang et al, 2012;Chen and Fang, 2013;Tan et al, 2013;Wang et al, 2013;Zeng and Skibnieeewski, 2013). The objective of ERP is to facilitate the flow of information among all business functions inside the organization and manage the connections to the outside (Wang et al, 2005Qi et al, 2006;Xu et al, 2006Xu et al, , 2008aXu et al, , 2008bXu et al, , 2009Xu et al, , 2012Li et al, 2008aLi et al, , 2008bLi et al, , 2008cDuan and Xu, 2012;Guo et al, 2012;Li, 2012;Olson and Staley, 2012;Gao et al, 2013;Kataev et al, 2013;Niu et al, 2013;Xu, 2013). ERP systems run on a variety of hardware and network configurations (Li et al, 2008a(Li et al, , 2008b(Li et al, , 2008cWang and Xu, 2008;Wu et al, 2009;Li et al, 2012;He and Xu, 2014).…”

Section: Discussionmentioning

confidence: 99%

An Empirical Study on Critical Factors Affecting Employee Satisfaction

Shan¹,

Li²,

Yao³

et al. 2014

Syst. Res.

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With the rapid development of information technology (IT), IT service quality has become a popular topic in both academic circles and enterprises. Based on the literature, the factors influencing employee satisfaction in Lenovo Group were studied. In terms of users in IT enterprises, a comprehensive evaluation index system of employee satisfaction was established based on a survey and a thorough data analysis. Using the structural equation modelling method, the critical factors affecting employee satisfaction and their interrelationships are identified. This method has practical value and can be used as a reference for employee satisfaction measurement and management in IT service enterprises. Copyright © 2014 John Wiley & Sons, Ltd.

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Section: Discussionmentioning

confidence: 99%

An Empirical Study on Critical Factors Affecting Employee Satisfaction

Shan¹,

Li²,

Yao³

et al. 2014

Syst. Res.

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“…The advances in wireless sensor networks, cloud computing, and other technologies help move the traditional Internet to an intelligent IoT [36], [37]. This trend will lead to further changes in existing systems such as enterprise systems [38]- [40], decision support systems [43]- [47], as well as business processes in general [47]- [49]. In this paper, we have presented a distributed CDM method for service detection, classification, composition, and data processing for the IoT.…”

Section: Discussionmentioning

confidence: 99%

A Distributed Consensus Algorithm for Decision Making in Service-Oriented Internet of Things

Oikonomou

Tryfonas

et al. 2014

IEEE Trans. Ind. Inf.

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-In a service-oriented Internet of things (IoT) deployment, it is difficult to make consensus decisions for services at different IoT edge nodes where available information might be insufficient or overloaded. Existing statistical methods attempt to resolve the inconsistency, which requires adequate information to make decisions. Distributed consensus decision making (CDM) methods can provide an efficient and reliable means of synthesizing information by using a wider range of information than existing statistical methods. In this paper, we first discuss service composition for the IoT by minimizing the multi-parameter dependent matching value. Subsequently, a cluster-based distributed algorithm is proposed, whereby consensuses are first calculated locally and subsequently combined in an iterative fashion to reach global consensus. The distributed consensus method improves the robustness and trustiness of the decision process. Permanent repository link

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“…A framework for data mining of key safety factors from safety-related cases of consumer products is proposed and depicted in Figure 1. Data mining theory, methods and algorithms have been discussed by many authors in the literature (Wilamowski et al, 1999;Wilamowski and Kaynak, 2000;Li and Xu, 2001;Li et al, 2003Li et al, , 2009Li et al, , 2013aLi et al, , 2013bDuan et al, 2007Duan et al, ,2009Shi et al, 2007;Hewlett et al, 2008;Xu et al, 2008;Wilamowski, 2010;Duan and Xu, 2012;Fritzsche et al, 2012;Hunter et al, 2012;Ingvaldsen and Gulla, 2012;Xu et al, 2012;Yang et al, 2012;Yu et al, 2012;Zhang et al, 2012;Bulysheva and Bulyshev, 2013;Katayev et al, 2013;Wang et al, 2013b;Xia et al, 2013;Xing et al, 2013;Zeng et al, 2013aZeng et al, , 2013b. The functional modules in the framework are classified into three phases, that is, cases collection, extraction of impact factors and retrieving of key factors by knowledge reasoning based on the Bayesian network.…”

Section: A Data Mining Frameworkmentioning

confidence: 99%

A Knowledge Engineering Framework for Identifying Key Impact Factors from Safety‐Related Accident Cases

Pan

Wang

et al. 2014

Syst. Res.

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Consumer product safety closely relates to consumer health. In this paper, a knowledge engineering framework is proposed for data mining to identify key safety factors from a large number of consumer product safety cases. Data mining in the framework is performed in three steps. The first step is to collect consumer product safety cases, a case can be semistructured or unstructured, and cases can be collected either manually or automatically by a web spider crawling certain websites. The second step is to extract all safety factors from a number of consumer product safety cases. A new method based on linear chain conditional random field is developed to extract safety factors. The effectiveness of the method has been validated on product cases. The third step is to identify a set of key factors from all safety factors by knowledge reasoning. To illustrate the process of knowledge reasoning, a set of 3192 safety cases of electric products with electric shock accidents is chosen as the case study; a Bayesian network based model is developed to retrieve key safety factors relating to electric shock accidents. The performance of the reasoning model has been verified by a combination of experts' evaluation and experiments, and it has shown the proposed reasoning model can help identify key safety factors of electric shock accidents successfully. Overall, the proposed framework is capable of identifying key safety factors from a large number of consumer product safety cases. Copyright © 2014 John Wiley & Sons, Ltd.

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Enterprise systems in Russia: 1992–2012

Cited by 36 publications

References 7 publications

An Empirical Study on Critical Factors Affecting Employee Satisfaction

An Empirical Study on Critical Factors Affecting Employee Satisfaction

A Distributed Consensus Algorithm for Decision Making in Service-Oriented Internet of Things

A Knowledge Engineering Framework for Identifying Key Impact Factors from Safety‐Related Accident Cases

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