Recommending View Bundles in Data Marketplaces

For companies to have a competitive advantage, they need to extract relevant information from data and for that, they need to complement their own data with other data sources. Data marketplaces are platforms on which data providers and data consumers do business. However, every data interaction incurs a monetary cost. Therefore, data consumers are interested in buying a set of interesting views that fit their (goal and) budget. Views allow data to be represented visually, enabling users to make sense of patterns and insights. Besides allowing for easier cost control, buying a set of views bundled together increases the chance of finding what consumers want over buying them view-by-view. Selecting the suitable views to compose an interesting bundle is non-trivial, due to the vast number of view combinations that potentially meet the data consumer’s needs. In this paper, we address the problem of view bundle recommendation in data marketplaces, in which the utility of a bundle depends on the interplay among candidate views. We propose the use of Self-Organizing Maps as a means to compute this interplay and use a Genetic Algorithm to design near-optimal bundles. Our empirical results demonstrate that our approach can effectively aid data consumers to find relevant view bundles under budget constraints.