Data Trading With Multiple Owners, Collectors, and Users: An Iterative Auction Mechanism

Cao, Xuanyu; Chen, Yan; Liu, K. J. Ray

doi:10.1109/tsipn.2017.2668144

Cited by 57 publications

(19 citation statements)

References 20 publications

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“…Data Bucket: This service is responsible for gathering data from different IoT solutions. It interfaces with the data owners using a mobile app where it allows data owners to express their privacy preferences, receive recommended data requests, trade data, and negotiate data trading [11]. We will walk you through the Data Bucket app later in this paper in order to demonstrate how data owners may engage with the S 2 aaS ecosystem.…”

Section: Architecture and Componentsmentioning

confidence: 99%

Designing the Sensing as a Service Ecosystem for the Internet of Things

Perera

Barhamgi

et al. 2018

IEEE Internet Things M.

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The Internet of Things (IoT) envisions the creation of an environment where everyday objects (e.g. microwaves, fridges, cars, coffee machines, etc.) are connected to the internet and make users' lives more productive, efficient, and convenient. During this process, everyday objects capture a vast amount of data that can be used to understand individuals and their behaviours. In the current IoT ecosystems, such data is collected and used only by the respective IoT solutions. There is no formal way to share data with external entities. We believe this is very efficient and unfair for users. We believe that users, as data owners, should be able to control, manage, and share data about them in any way that they choose and make or gain value out of them. To achieve this, we proposed the Sensing as a Service (S 2 aaS) model. In this paper, we discuss the Sensing as a Service ecosystem in terms of its architecture, components and related user interaction designs. This paper aims to highlight the weaknesses of the current IoT ecosystem and to explain how S 2 aaS would eliminate those weaknesses. We also discuss how an everyday user may engage with the S 2 aaS ecosystem and design challenges.

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Section: Architecture and Componentsmentioning

confidence: 99%

Designing the Sensing as a Service Ecosystem for the Internet of Things

Perera

Barhamgi

et al. 2018

IEEE Internet Things M.

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show abstract

“…This research is novel in that it enhances understanding of the structural characteristics of data populations on platforms using metadata and a variable-based network approach. To date, there have been studies on data platforms and exchanges based on the game theory [16], business roles and marketability [17], [18], market models [6], trading and pricing models [19], [20], tokenization of data using blockchain based systems [11], data protection and digital rights management models [8], [21], and privacy issues [22], [23], but none based on variables in the data and a network approach. Moreover, we focus not only on shareable data, which are typical of open data, but also sensitive data that generally cannot be shared, such as the private data of corporations and individuals.…”

Section: Introductionmentioning

confidence: 99%

Understanding the Structural Characteristics of Data Platforms Using Metadata and a Network Approach

Hayashi

Ohsawa

2020

IEEE Access

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With the emergence of global platforms for trading and buying/selling data, data have become a profitable commodity. The growth of such platforms has necessitated the further expansion of the scope of data in digital economies. To this end, understanding the nature of available data and their relationships between them has become an important challenge for expanding their use. Thus, in this study, we assumed data on the platforms as a population and metadata as the samples. Thus, we quantitatively investigated the structural characteristics of data platforms, while avoiding the risk of lost business opportunities and privacy issues by not sharing the data themselves. By observing the characteristics of data and variables, we found that the data network had a structure that was locally dense and globally sparse, which is quite similar to networks of human relationships. Moreover, we found that data play different roles on the platforms when divided into sharing conditions, namely, shareable data and sensitive data. Finally, we discussed the potential tactics for individuals who create/use data platforms based on our findings. The contributions of this study include a new framework for data platform observation and a method that uses metadata and a network approach to analyze structural characteristics of data.

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“…Therefore, we made several assumptions in order to understand and discuss data matching in the data market. First, in addition to data providers and users, there are several other stakeholders, such as data brokers and analysts [19,[28][29][30]. Although there is a matching problem considering two or more players [31], for the sake of simplicity, we only considered the matching between the data providers and users.…”

Section: Introductionmentioning

confidence: 99%

TEEDA: An Interactive Platform for Matching Data Providers and Users in the Data Marketplace

Hayashi

Ohsawa

2020

Information

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Improvements in Web platforms for data exchange and trading are creating more opportunities for users to obtain data from data providers of different domains. However, the current data exchange platforms are limited to unilateral information provision from data providers to users. In contrast, there are insufficient means for data providers to learn what kinds of data users desire and for what purposes. In this paper, we propose and discuss the description items for sharing users’ calls for data as data requests in the data marketplace. We also discuss structural differences in data requests and providable data using variables, as well as possibilities of data matching. In the study, we developed an interactive platform, named “treasuring every encounter of data affairs” (TEEDA), to facilitate matching and interactions between data providers and users. The basic features of TEEDA are described in this paper. From experiments, we found the same distributions of the frequency of variables but different distributions of the number of variables in each piece of data, which are important factors to consider in the discussion of data matching in the data marketplace.

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Data Trading With Multiple Owners, Collectors, and Users: An Iterative Auction Mechanism

Cited by 57 publications

References 20 publications

Designing the Sensing as a Service Ecosystem for the Internet of Things

Designing the Sensing as a Service Ecosystem for the Internet of Things

Understanding the Structural Characteristics of Data Platforms Using Metadata and a Network Approach

TEEDA: An Interactive Platform for Matching Data Providers and Users in the Data Marketplace

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