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What You Pay for Is What You Get

  • Ruiming Tang
  • Dongxu Shao
  • Stéphane Bressan
  • Patrick Valduriez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8056)

Abstract

In most data markets, prices are prescribed and accuracy is determined by the data. Instead, we consider a model in which accuracy can be traded for discounted prices: “what you pay for is what you get”.

The data market model consists of data consumers, data providers and data market owners. The data market owners are brokers between the data providers and data consumers. A data consumer proposes a price for the data that she requests. If the price is less than the price set by the data provider, then she gets an approximate value. The data market owners negotiate the pricing schemes with the data providers. They implement these schemes for the computation of the discounted approximate values.

We propose a theoretical and practical pricing framework with its algorithms for the above mechanism. In this framework, the value published is randomly determined from a probability distribution. The distribution is computed such that its distance to the actual value is commensurate to the discount. The published value comes with a guarantee on the probability to be the exact value. The probability is also commensurate to the discount. We present and formalize the principles that a healthy data market should meet for such a transaction. We define two ancillary functions and describe the algorithms that compute the approximate value from the proposed price using these functions. We prove that the functions and the algorithm meet the required principles.

Keywords

Distance Function Probability Function Invariance Principle Data Market Optimal Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ruiming Tang
    • 1
  • Dongxu Shao
    • 1
  • Stéphane Bressan
    • 1
  • Patrick Valduriez
    • 2
  1. 1.National University of SingaporeSingapore
  2. 2.INRIA & LIRMMMontpellierFrance

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