Don`t Buy A Pig In A Poke A Framework for Checking Consumer Requirements In A Data Marketplace

Abstract

The 21st century is the century of data. Data is the new oil and powers many new applications and business models. The increasing value of data leads to the fact requires new possibilities and methods to trade with "data" as a trading good. One of these possibilities is a data marketplace, a digital e-commerce platform like eBay or Amazon, but specifically targeting data, information, and datasets. Since, other than physical trading goods, data is not so easy to verify, a framework is required to characterize data and to validate data against user requirements. This leads to a conflict between security and privacy requirements of data providers and the necessity of checking the data consumer requirements. In this paper, we discuss the specific challenges of data trading and introduce a framework to help users check their specific requirements (Data Quality) as a reference point for a purchase decision. 1.2. Research Methodology The research presented in this paper has been conducted using the Design Science methodology. Instead of formulating hypotheses, Design Science concentrates on developing an artifact in a context [3]. Artifacts are, for example, methods, techniques, notations, and algorithms used in software systems [3].

Data Trading Similarity Signature An Extended Data Trading Framework for Human and Non-Human Actors

“…The Design Science paradigm concentrates on the development of an artifact in a specific context [6]. The preceding work was already developed by using Design Science Research [4]. Respectively, this paper uses the already existing data marketplace as a starting point.…”

“…Data is a kind of digital goods distinguished from other goods by characteristics like non-rivalry, infinite expansibility, and combinability [2]. Furthermore, the value of data is quantified by the news content (also called the level of surprise) [3], which leads to one of the key challenges in data tradingthe product (the dataset itself) cannot be shown before it is sold [4].…”

“…The previously mentioned challenge is already tackled by a work of [4], where the authors present a framework for data trading by designing a data marketplace. This work mainly focuses on the question of how a data consumer can check his specific requirements by checking the data quality:…”

“…Data quality describes the value of a dataset from the data consumer's point of view with regard to the requirements. High data quality means a good match of the requirements and the characteristics of the dataset [4].…”

“…In the preceding work of [4], the main requirements and use cases for a data marketplace were conducted, namely:…”