Cultural distance for service composition in cyber–physical–social systems

https://doi.org/10.1016/j.future.2018.06.012Get rights and content

Highlights

  • This paper proposes a service composition approach based on the cultural distance.

  • We propose a novel concept called the preference degree to represent the degree of preference of the users to the services.

  • The proposed approach is evaluated using a real-world service QoS dataset.

  • Experimental results show that our approach can obtain composition services that satisfy specific requirements in a short time.

Abstract

Cyber–physical–social systems can be organized as workflows that interconnect the resources in physical, cyber, and social worlds in real time. This integration of these worlds with various resource as services requires service composition approaches that can integrate essential components and share relevant information in these worlds. Although numerous service composition approaches have been proposed, there still exist many challenges in the representation of user preference of service composition in cyber–physical–social systems. In this paper, a service composition approach based on the cultural distance is proposed to improve the reliability and satisfaction. This approach employs the cultural distance to measure the user preference quantitatively in a simple mathematical form. The user preference degree and the user preference vector are defined to select the service from a global view and an accurate point separately. Then, a 0-1 mixed-integer programming algorithm is used to identify the most suitable services for composition. The experimental results based on two real-world datasets show that the proposed approach for representing the user preference quantitatively is more simple and effective than other approaches. In a word, the proposed approach yields satisfactory performance of service composition in CPSS with regard to the user preference and efficiency.

Introduction

Cyber–physical–social systems (CPSS) is the next generation of intelligent systems which aim at interconnecting cyber, physical and social worlds by monitoring and controlling these worlds [1], and it will lead us to an era of intelligent enterprises and industries [2]. It focuses on coordination of the human, computing and physical resources, and will be applied in intelligent enterprise, intelligent transportation, smart home, intelligent medical and other fields. Currently, CPSS is organized as workflows that are composed of multiple, heterogeneous elements and data types from cyber, physical and social worlds, such as smart phones, tablets, cameras, vehicle, energy meters, computational models, programming abstraction models and social media [[3], [4], [5], [6], [7], [8], [9]]. To facilitate the integration tasks in heterogeneous CPSS workflows, service composition approach that can be used to integrate essential components in these world is required [10].

Service-oriented architecture (SOA) enables the service composition in a loosely coupled way in order to achieve complex functionality by combining basic services [11 ]. It is well-known that with the rapid development of CPSS, the number of essential components published in the three worlds is increasing rapidly. At the same time, the requirements of CPSS users are becoming increasingly complex and personalized. Therefore, although SOA technologies provide an easy way to integrate components within and across organizational boundaries, it is difficult for CPSS users to obtain a service composition that satisfies their specific requirements for quality of service (QoS) [12].

Most existing service composition algorithms considered the user preference are based on the Semantic Web environment, where a user agent works on behalf of its owner and knows the personal preference of users. With the rapid increase in the number of components, it is critical for the agent to identify the service that best matches the user preference in CPSS. Thus, the representation of the user preference and the service composition according to the representation are important research problems in CPSS.

Although numerous service composition approaches based on the user preference have been proposed, for example, researchers have applied a hierarchical task network [13] and fuzzy set theory [[14], [15], [16]] to represent the requirements and preference of users, these approaches have the following two limitations.

First, the previous service composition approaches seek to represent the user preference in as accurate and detailed a manner as possible. However, some users do not realize their real preference clearly or cannot express their preference clearly; therefore, the accurate and detailed representation of the user preference may lead to an inaccurate representation of the user preference in CPSS. Additionally, the result of the service composition is very sensitive to the preference weight in CPSS. In this case, the too accurate representation of the user preference may lead to an unreliable service composition result. Moreover, the accurate and detailed representation of the user preference costs additional time and affects the real-time nature of the service composition in CPSS.

Second, the existing methods are complicated because they always concentrate on obtaining a preference representation for each user and ignore the group or regional characteristics of CPSS users. In many domains, it is desirable to assess such a preference qualitatively rather than quantitatively [17]. Thus, it is not necessary to give the preference representation for each user, only to give the preference representation for each user group formed according to some characteristics.

To meet the personalized requirements of CPSS users and improve the reliability and satisfaction of service composition results in CPSS, this paper aims to propose a new service composition approach considered the user preference. In response to these limitations of existing approaches, the proposed approach does not concentrate on obtaining an accurate preference representation for every user but on finding a simple and powerful representation. Considering that there is a close relationship between the user preference and their cultural background, this paper introduces an important concept in the study of cultural differences, called cultural distance, employs the cultural distance to express the user preference and propose a service composition approach based on cultural distance. In the proposed service composition approach, the representation of the user preference based on the cultural distance groups the CPSS users according to their country/region. Users from the same region have the same preference value. Hence, it does not matter if the preference of some CPSS users is unclear. Thus, the proposed approach in this paper can avoid the two aforementioned problems.

The main contributions of this paper are summarized as follows:

  • This paper proposes a novel representation of the user preference based on the cultural distance, which is a concept used in cultural-difference measurement systems [18]. Compared with the existing representations, the representation of the preference in this paper is simpler and more effective for CPSS.

  • This paper proposes a novel concept called the preference degree to represent the degree of preference of the users to the services. Services with a low preference degree are eliminated to improve the user satisfaction and the real-time nature of the service composition in CPSS.

The remainder of the paper is organized as follows: Section 2 discusses the background of service composition; Section 3 provides a novel representation of the user preference and describes the proposed service composition approach; Section 4 presents the experimental results; And Section 5 concludes the paper and discusses future work.

Section snippets

Background

Service composition technology is the core technology of service-oriented architecture and service-oriented computing and can quickly satisfy the requirements of complex, dynamic, and inter-organizational businesses. In this section, the service composition problem and related research are introduced.

Service composition approach

Aiming to identify the services that best satisfy the needs of users, cultural distance is introduced as a new QoS attribute. Based on this attribute, this paper proposes a simple service composition approach in CPSS that can satisfy the user preference.

As shown in Fig. 1, the proposed approach can be divided into three phases. The first phase is the preference degree computation, where the user preference degree to services is computed and the services with a low preference degree are filtered

Experiment

This paper compares the proposed service-composition approach, which is called SIR, with the MIP approach [42] and two variational approaches of SIR with regard to the user satisfaction and real time using two real-world datasets. The experimental results show that SIR can find a more satisfactory service composition solution in a shorter computation time than other approaches.

Additionally, this paper analyzes the effect of the filtration ratio in SIR and discuss the relationship between the

Conclusion

Cultural distance quantitatively describes the cultural difference in cross-culture study and cross-culture practice. To simply represent a user preference and guarantee the user satisfaction and real-time nature of the service composition in CPSS, this paper proposes a service composition approach based on cultural distance. The service composition approach in this paper uses the cultural distance to compute the preference degree and the preference vector and employs 0–1 MIP to select the best

Acknowledgment

This work was supported in part by the National Science Foundation of China (Grant No. 61472047).

Shangguang Wang received his PhD degree at Beijing University of Posts and Telecommunications in 2011. He is an associate professor at the State Key Laboratory of Networking and Switching Technology (BUPT). He has published more than 100 papers, and played a key role at many international conferences, such as general chair and PC chair. His research interests include service computing, cloud computing, and mobile edge computing. He is a senior member of the IEEE, and the Editor-in-Chief of the

References (43)

  • WangL. et al.

    A parallel file system with application-aware data layout policies for massive remote sensing image processing in digital earth

    IEEE Trans. Parallel Distrib. Syst.

    (2015)
  • MenzelM. et al.

    Cloudgenius: a hybrid decision support method for automating the migration of web application clusters to public clouds

    IEEE Trans. Comput.

    (2015)
  • A. Klein, F. Ishikawa, S. Honiden, Towards network-aware service composition in the cloud, in: Proceedings of the...
  • D. Wu, B. Parsia, E. Sirin, J. Hendler, D. Nau, Automating DAML-S web services composition using SHOP2, in: Proceedings...
  • M. Reformat, D.M. Li, C. Ly, Approximate reasoning and semantic web services, in: Processing of the IEEE Annual Meeting...
  • M.S. Lin, J.S. Xie, H.Q. Guo, H. Wang, Solving QoS-driven Web service dynamic composition as fuzzy constraint...
  • WenJ.J. et al.

    A user preference aware approach to Web services composition

    High Technol. Lett.

    (2007)
  • H. Wang, J. Xu, P. Li, Incomplete Preference-driven Web Service Selection, in: Proceedings of the IEEE International...
  • HofstedeG.

    Cultures and Organizations: Software of the Mind

    (2010)
  • MaY. et al.

    A highly accurate prediction algorithm for unknown web service qos value

    IEEE Trans. Serv. Comput.

    (2017)
  • WangS.G. et al.

    Reputation measurement of cloud services based on unstable feedback ratings

    Internat. J. Web Grid Serv.

    (2015)
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    Shangguang Wang received his PhD degree at Beijing University of Posts and Telecommunications in 2011. He is an associate professor at the State Key Laboratory of Networking and Switching Technology (BUPT). He has published more than 100 papers, and played a key role at many international conferences, such as general chair and PC chair. His research interests include service computing, cloud computing, and mobile edge computing. He is a senior member of the IEEE, and the Editor-in-Chief of the International Journal of Web Science.

    Yan Guo received her bachelor degree in mathematics and applied mathematics from Beijing University of Posts and Telecommunications in 2016. She is a first-year Master’s student at Beijing University of Posts and Telecommunications, and will be a PhD candidate next term. Her research interests include service computing and mobile edge computing.

    Yan Li received his PhD degree at Shanghai University of Finance and Economics in 2012. She is an associate professor of department of information management and information systems in Shanghai International Studies University. In the past 12 years, she devoted to the research of information management and system development. She has published more than 30 papers, and played a key role at many project, such as National Natural Science Foundation of China, National Planning Office of Philosophy and Social Science. Her research interests include service computing, cloud computing, financial crisis and cross-culture studies.

    Ching-Hsien Hsu is a professor and the chairman in the CSIE department at Chung Hua University, Taiwan; He was distinguished chair professor at Tianjin University of Technology, China, during 2012–2016. His research includes high performance computing, cloud computing, parallel and distributed systems, big data analytics and intelligence. He has published 200 papers in these areas, including top journals such as IEEE TPDS, IEEE TSC, IEEE TCC, IEEE TETC, IEEE T-SUSC, IEEE Systems, IEEE Network, IEEE Communications, ACM TOMM. Dr. Hsu is serving as editorial board for a number of prestigious journals, including IEEE TSC, IEEE TCC, IJCS, JoCS. He has been acting as an author/co-author or an editor/co-editor of 10 books from Elsevier, Springer, IGI Global, World Scientific and McGraw-Hill. Dr. Hsu was awarded nine times distinguished award for excellence in research from Chung Hua University. He is vice chair of IEEE TCCLD, executive committee of IEEE TCSC, Taiwan Association of Cloud Computing and an IEEE senior member.

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