A multi-sided market of personal data resource allocation: An empirical study of China’s car-hailing platform

The current rapid development of online car-hailing services creates a serious challenge to the existing paradigm of market governance and antitrust policy. However, the debate on the market structure of the car-hailing platform requires more empirical evidence to uncover its functions. This research adopts an interdisciplinary methodology based on computer science and economics, and including software reverse engineering tools applied to the interoperability of the terminal application and resource allocation model, to demonstrate the topological market structure of personal data resources allocation in China’s car-hailing industry. Within the discussion of the hybrid nature of technology and economy, the analysis results clearly show that China’s car-hailing platform services present a multi-sided market structure when seen from the perspective of personal data resource allocation. Personal data resource (PDR), that is considered an essential market resource, is applied as an asset transferred unhindered between platforms via the application programming interface, and thus, creating a new market allocation mechanism. The connection between the car-hailing platforms and social media platforms is an essential aspect of the market competition in the domain. As applications of online platforms increase in the global context, this research offers a new perspective in personal data resource allocation with implications for the governance of the platform economy.

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Data access as a big competitive advantage: evidence from China's car-hailing platforms

Data Technologies and Applications ◽

10.1108/dta-01-2019-0013 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Lei Huang ◽

Yandong Zhao ◽

Guangxi He ◽

Yangxu Lu ◽

Juanjuan Zhang ◽

...

Keyword(s):

Resource Allocation ◽

Personal Data ◽

Data Access ◽

Allocation Mechanism ◽

Content Type ◽

Data Resource ◽

Online Platform ◽

The Social ◽

Essential Components ◽

Resource Allocation Mechanism

PurposeThe online platform is one of the essential components of the platform economy that is constructed by a large scale of the personal data resource. However, accurate empirical test of the competition structure of the data-driven online platform is still less. This research is trying to reveal market allocation structure of the personal data resource of China's car-hailing platforms competition by the empirical data analysis.Design/methodology/approachThis research is applying the social network analysis by R packages, which include k-core decomposition and multilevel community detection from the data connectedness via the decompilation and the examination of the application programming interface of terminal applications.FindingsThis research has found that the car-hailing platforms, which establish more constant personal data connectedness and connectivity with social media platforms, are taking the competitive market advantage within the sample network. Data access discrimination is a complementary method of market power in China's car-hailing industry.Research limitations/implicationsThis research offers a new perspective on the analysis of the multi-sided market from the personal data resource allocation mechanism of the car-hailing platform. However, the measurement of the data connectedness requires more empirical industry data.Practical implicationsThis research reveals the competition structure that relies on personal data resource allocation mechanism. It offers empirical evidence for governance, which is considered as the critical issue of big data research, by reviewing the nature of the data network.Social implicationsIt also reveals the data convergence process of the social system and the technological system.Originality/valueThis research offers a new research method for the real-time regulation of the car-hailing platform.

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A Resource Allocation Model for Desktop Clouds

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Delivery and Adoption of Cloud Computing Services in Contemporary Organizations ◽

10.4018/978-1-4666-8210-8.ch008 ◽

2015 ◽

pp. 199-218 ◽

Cited By ~ 1

Author(s):

Abdulelah Alwabel ◽

Robert John Walters ◽

Gary B. Wills

Keyword(s):

Resource Allocation ◽

Cloud Computing ◽

Cloud Services ◽

New Paradigm ◽

Allocation Mechanism ◽

Desktop Grids ◽

Allocation Model ◽

Resource Allocation Model ◽

Computing Services ◽

New Type

Cloud computing is a new paradigm that promises to move IT a step further towards utility computing, in which computing services are delivered as a utility service. Traditionally, Cloud employs dedicated resources located in one or more data centres in order to provide services to clients. Desktop Cloud computing is a new type of Cloud computing that aims at providing Cloud capabilities at low or no cost. Desktop Clouds harness non dedicated and idle resources in order to provide Cloud services. However, the nature of such resources can be problematic because they are prone to failure at any time without prior notice. This research focuses on the resource allocation mechanism in Desktop Clouds.The contributions of this chapter are threefold. Firstly, it defines and explains Desktop Clouds by comparing them with both Traditional Clouds and Desktop Grids. Secondly, the paper discusses various research issues in Desktop Clouds. Thirdly, it proposes a resource allocation model that is able to handle node failures.

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Task Allocation Model Based on Worker Friend Relationship for Mobile Crowdsourcing

Sensors ◽

10.3390/s19040921 ◽

2019 ◽

Vol 19 (4) ◽

pp. 921 ◽

Cited By ~ 12

Author(s):

Bingxu Zhao ◽

Yingjie Wang ◽

Yingshu Li ◽

Yang Gao ◽

Xiangrong Tong

Keyword(s):

Social Networks ◽

Task Allocation ◽

Location Privacy ◽

Rapid Development ◽

Human Beings ◽

Allocation Mechanism ◽

Allocation Model ◽

Mobile Crowdsourcing ◽

System A ◽

The Relationship

With the rapid development of mobile devices, mobile crowdsourcing has become an important research focus. According to the task allocation, scholars have proposed many methods. However, few works discuss combining social networks and mobile crowdsourcing. To maximize the utilities of mobile crowdsourcing system, this paper proposes a task allocation model considering the attributes of social networks for mobile crowdsourcing system. Starting from the homogeneity of human beings, the relationship between friends in social networks is applied to mobile crowdsourcing system. A task allocation algorithm based on the friend relationships is proposed. The GeoHash coding mechanism is adopted in the process of calculating the strength of worker relationship, which effectively protects the location privacy of workers. Utilizing synthetic dataset and the real-world Yelp dataset, the performance of the proposed task allocation model was evaluated. Through comparison experiments, the effectiveness and applicability of the proposed allocation mechanism were verified.

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A Resource Allocation Model for Desktop Clouds

Web Services ◽

10.4018/978-1-5225-7501-6.ch016 ◽

2019 ◽

pp. 258-279

Author(s):

Abdulelah Alwabel ◽

Robert John Walters ◽

Gary B. Wills

Keyword(s):

Resource Allocation ◽

Cloud Computing ◽

Cloud Services ◽

New Paradigm ◽

Allocation Mechanism ◽

Desktop Grids ◽

Allocation Model ◽

Resource Allocation Model ◽

Computing Services ◽

New Type

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A Resource Allocation Model for Desktop Clouds

Web-Based Services ◽

10.4018/978-1-4666-9466-8.ch017 ◽

2016 ◽

pp. 356-376 ◽

Cited By ~ 1

Author(s):

Abdulelah Alwabel ◽

Robert John Walters ◽

Gary B. Wills

Keyword(s):

Resource Allocation ◽

Cloud Computing ◽

Cloud Services ◽

New Paradigm ◽

Allocation Mechanism ◽

Desktop Grids ◽

Allocation Model ◽

Resource Allocation Model ◽

Computing Services ◽

New Type

Cloud computing is a new paradigm that promises to move IT a step further towards utility computing, in which computing services are delivered as a utility service. Traditionally, Cloud employs dedicated resources located in one or more data centres in order to provide services to clients. Desktop Cloud computing is a new type of Cloud computing that aims at providing Cloud capabilities at low or no cost. Desktop Clouds harness non dedicated and idle resources in order to provide Cloud services. However, the nature of such resources can be problematic because they are prone to failure at any time without prior notice. This research focuses on the resource allocation mechanism in Desktop Clouds. The contributions of this chapter are threefold. Firstly, it defines and explains Desktop Clouds by comparing them with both Traditional Clouds and Desktop Grids. Secondly, the paper discusses various research issues in Desktop Clouds. Thirdly, it proposes a resource allocation model that is able to handle node failures.

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Energy-Efficient Resource Allocation for NOMA-Enabled Internet of Vehicles

Wireless Communications and Mobile Computing ◽

10.1155/2021/7490689 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Xin Chen ◽

Zhuo Ma ◽

Teng Ma ◽

Xu Liu ◽

Ying Chen

Keyword(s):

Resource Allocation ◽

Multiple Access ◽

High Reliability ◽

Rapid Development ◽

Computation Time ◽

Allocation Model ◽

Internet Of Vehicles ◽

V2v Communication ◽

Markov Decision ◽

Energy Efficient Resource Allocation

With the rapid development of Internet of vehicles (IoV) technology, the distribution of vehicles on the highway becomes more dense and the highly reliable communication between vehicles becomes more important. Nonorthogonal multiple access (NOMA) is a promising technology to meet the multiple access volume and the high reliability communication demands of IoV. To meet the Vehicle-to-Vehicle (V2V) communication requirements, a NOMA-based IoV system is proposed. Firstly, a NOMA-based resource allocation model in IoV is developed to maximize the energy efficiency (EE) of the system. Secondly, the established model is transformed into a Markov decision process (MDP) model and a deep reinforcement learning-based subchannel and power allocation (DSPA) algorithm is designed. An event trigger block is used to reduce computation time. Finally, the simulation results show that NOMA can significantly improve the system performance compared to orthogonal multiaccess, and the proposed DSPA algorithm can significantly improve the system EE and reduce the computation time.

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