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

Abstract

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.

Data Origination: Human-Centered Approach for Design, Acquisition, and Utilization of Data

“…Here, the metadata are composed primarily of a set of variables. Variables are the logical set of attributes of a real-world event [15,16], e.g., in the case of healthcare data, "name," "weight," "height," "sex," or "blood type" are the variables. The variables define the elements that make up the events, and each variable has a value represented by numbers and strings.…”

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

“…We can learn and specify the areas and types of required data, e.g., "earthquake data in the great east Japan earthquake" or "tweets during the Christmas season in 2018." A variable is a logical set of data attributes [33], which are important features to understand the structure of data and determines the granularity of the data [25,34]. In "earthquake data of the great east Japan earthquake," for example, "year," "month," "day," and "time" are necessary to learn when the earthquake occurred; the "latitude," "longitude," "depth," and "center name of the earthquake" are to learn where the earthquake occurred; and "magnitude" is to learn the strength of the earthquake.…”

“…For example, if two sets of data information share the common variables of "amount paid," "day," "month," and "purchased item," a link is established between them. This is based on the assumption that the commonality between variable sets indicates similarity between the structural characteristics of data [25,34]. The studies using Ontology-Based Data Access have defined schemas for heterogeneous data integration [36][37][38].…”

“…Using 1,316 providable data, Hayashi & Ohsawa [25] reported that the distribution of the number of variables in the providable data is a power distribution. In this study, though the number of the providable data is smaller than in the aforementioned study, we consider that the providable data follow a power distribution from a global perspective.…”

“…Previous papers have reported research on data marketplaces and data exchange platforms where data are traded as exchangeable economic goods [18,19]. These studies have explored game theory [20], privacy models [21,22], market model for innovative collaboration [5], secure model using blockchain [10], pricing mechanism of data [23,24], and the complex network approach to conceptualize data exchange platforms [25]. Therefore, considering data matching in the data marketplace is a natural extension of these previous works.…”