A Double Auction for Querying the Web of Data

Moor, Dmitry; Grubenmann, Tobias; Seuken, Sven; Bernstein, Abraham

doi:10.4108/eai.8-8-2015.2260632

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…However, their approach does not easily extend to domains when users join data produced by multiple data providers. Moor et al [2015 and Agarwal et al [2019] emphasize the importance of joining data coming from different data providers. They argue that the combinatorial preferences of buyers is a crucial feature for data markets as many databases can complement each other.…”

Section: Call For Data Marketsmentioning

confidence: 99%

Data markets with dynamic arrival of buyers and sellers

Moor

2019

Proceedings of the 14th Workshop on the Economics of Networks, Systems and Computation

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We propose a market design solution for a market for distributed data. The main challenges addressed by our solution are (1) different data providers produce different databases that can be joined to produce answers for users' queries; (2) data providers have high fixed costs for producing their databases; and (3) buyers and sellers can arrive dynamically to the market. Our design relies on using a Markov chain with states corresponding to different numbers of allocated databases. The transition probabilities between different states are governed by the payments suggested by the market platform to the data providers. The main challenge in this setting is to guarantee dynamic incentive compatibility, i.e., to ensure that buyers and sellers are not incentivized to arrive late to the market or to misreport their costs or values. To achieve this, we disentangle the payments suggested by the market platform to the sellers from the posted prices exposed to the buyers. We prove that the buyer-optimal payments that are exposed to sellers are non-increasing which prevents late arrivals of sellers. Further, we demonstrate that the posted prices exposed to buyers constitute a martingale process (i.e., late arrivals lead to the same expected price). Finally, we show that our design guarantees zero expected average budget deficit and we perform a number of simulations to validate our model.

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Section: Call For Data Marketsmentioning

confidence: 99%

Data markets with dynamic arrival of buyers and sellers

Moor

2019

Proceedings of the 14th Workshop on the Economics of Networks, Systems and Computation

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“…In [13], we first introduced the idea of using a double-auction to sell data on the WoD. Finally, in [10] we introduced our model for a marketplace which allows customers to buy data from decentralized sellers in an integrated way.…”

Section: Data Marketsmentioning

confidence: 99%

Monetization Strategies for the Web of Data

Grubenmann

2018

Companion of the the Web Conference 2018 on the Web Conference 2018 - WWW '18

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Inspired by the World Wide Web, the Web of Data is a network of interlinked data fragments. One of the main advantages of the Web of Data is that all of its content is processable by machines. However, this also has its drawbacks when it comes to monetization of the content: advertisements and donations-two important financial motors in the World Wide Web-do not translate into the Web of Data as they rely on exposing the user to advertisement/call for donations. The remedy this situation, we propose two different monetization strategies for the Web of Data. The first strategy involves a marketplace where users can buy data in an integrated way. The second strategy allows third parties to promote certain data. In return, the sponsors pay money whenever a user follows a link contained in the sponsored data. We identified two different kind of data-commercial and sponsored data-which can benefit from the two respective monetization strategies. With our work, we propose solutions to the problem of financing the creation and maintenance of content in the Web of Data. Monetization Strategies for the Web of Data ABSTRACTInspired by the World Wide Web, the Web of Data is a network of interlinked data fragments. One of the main advantages of the Web of Data is that all of its content is processable by machines. However, this also has its drawbacks when it comes to monetization of the content: advertisements and donations-two important financial motors in the World Wide Web-do not translate into the Web of Data as they rely on exposing the user to advertisement/call for donations.The remedy this situation, we propose two different monetization strategies for the Web of Data. The first strategy involves a marketplace where users can buy data in an integrated way. The second strategy allows third parties to promote certain data. In return, the sponsors pay money whenever a user follows a link contained in the sponsored data. We identified two different kind of data-commercial and sponsored data-which can benefit from the two respective monetization strategies. With our work, we propose solutions to the problem of financing the creation and maintenance of content in the Web of Data.

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“…This paper here represents a significant rework of the old pilot as it proposes a complete model, an improved market analysis, and a prototype implementation instead of a simulation. In [25], we introduced the idea of using a double-auction for the WoD and showed the deficiency of the threshold rule in this setting together with three ways to correct them. However, our approach assumed that we have access to accurate join-estimates to produce satisfying results -an assumption which might be hard to enforce in the WoD.…”

Section: Related Workmentioning

confidence: 99%

FedMark: A Marketplace for Federated Data on the Web

Grubenmann,

Bernstein,

Moor

et al. 2018

Preprint

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The Web of Data (WoD) has experienced a phenomenal growth in the past. This growth is mainly fueled by tireless volunteers, government subsidies, and open data legislations. The majority of commercial data has not made the transition to the WoD, yet. The problem is that it is not clear how publishers of commercial data can monetize their data in this new setting. Advertisement, which is one of the main financial engines of the World Wide Web, cannot be applied to the Web of Data as such unwanted data can easily be filtered out, automatically. This raises the question how the WoD can (i) maintain its grow when subsidies disappear and (ii) give commercial data providers financial incentives to share their wealth of data. In this paper, we propose a marketplace for the WoD as a solution for this data monetization problem. Our approach allows a customer to transparently buy data from a combination of different providers. To that end, we introduce two different approaches for deciding which data elements to buy and compare their performance. We also introduce FedMark, a prototypical implementation of our marketplace that represents a first step towards an economically viable WoD beyond subsidies.

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A Double Auction for Querying the Web of Data

Cited by 6 publications

References 9 publications

Data markets with dynamic arrival of buyers and sellers

Data markets with dynamic arrival of buyers and sellers

Monetization Strategies for the Web of Data

FedMark: A Marketplace for Federated Data on the Web

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