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Measuring the Price of Data in Commercial Data Marketplaces
| dc.contributor.author | Andres, Santiago | |
| dc.contributor.author | Iordanou, Costas | |
| dc.contributor.author | Laoutaris, Nikolaos | |
| dc.date.accessioned | 2022-10-31T09:40:01Z | |
| dc.date.available | 2022-10-31T09:40:01Z | |
| dc.date.issued | 2022-11-30 | |
| dc.identifier.citation | [1] Advaneo. Access to the world of data. https://www.advaneo-datamarketplace.de/. Last accessed: Jun’22 2] A. Agarwal, M. Dahleh, and T. Sarkar. A marketplace for data: An algorithmic solution. In Proc. of ACM EC, 2019. [3] S. Andrés and N. Laoutaris. A survey of data marketplaces and their business models. ACM SIGMOD Record, 51(3), 2022. [4] AWS. Amazon Web Services Marketplace. https://aws.amazon.com/marketplace. Last accessed: Jun ’22. [5] Battlefin. Better your investments using alternative data. https://www.battlefin. com/. Last accessed: Jun’22. [6] D. Brickley, M. Burgess, and N. Noy. Google dataset search: Building a search engine for datasets in an open web ecosystem. In Proc. of ACM WWW conf., 2019. [7] J. P. Carrascal, C. Riederer, V. Erramilli, M. Cherubini, and R. de Oliveira. Your browsing behavior for a big mac: Economics of personal information online. In Proc. of ACM WWW Conf., 2013. [8] Caruso. Your solution. one platform. multibrand in-vehicle data. https://www. caruso-dataplace.com/. Last accessed: Jun ’22. [9] G. Cattaneo, G. Micheletti, and al. The European data market monitoring tool. key facts and figures, first policy conclusions, data landscape and quantified stories. final study report. European Commission, 2020. [10] S. Chawla, S. Deep, P. Koutris, and Y. Teng. Revenue maximization for query pricing. Proc. of the VLDB Endow., 13, 2019. [11] L. Chen, P. Koutris, and A. Kumar. Towards model-based pricing for machine learning in a data marketplace. In Proc. of ACM SIGMOD, 2019. [12] Clive Humby. Data is the new oil! Keynote at ANA Senior Marketer’s Summit, Kellogg School, 2006. [13] DataRade. Datarade. choose the right data with confidence. https://datarade.ai/. Last accessed: Jun ’22. [14] Dawex. DAWEX Data Exchange, unleash the value of your data. https://www. dawex.com/. Last accessed: jun ’22. [15] DIH. Data intelligence hub. extract value from data securely. [16] A. Ghosh and A. Roth. Selling privacy at auction. In Proc. of the ACM EC ’11, 2011. [17] A. V. Goldberg and J. D. Hartline. Competitiveness via consensus. In Proc. of the 14th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA ’03, page 215–222, USA, 2003. Society for Industrial and Applied Mathematics. [18] A. V. Goldberg, J. D. Hartline, and A. Wright. Competitive auctions and digital goods. In Proc. of the ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics, 2001. [19] Harvard. Dataverse. https://dataverse.harvard.edu/. Last accessed: Jun ’22. [20] M. Hashemi. Web page classification: A survey of perspectives, gaps, and future directions. 79(17–18), 2020. [21] J. R. Heckman, E. Boehmer, E. H. Peters, M. Davaloo, and N. G. Kurup. A pricing model for data markets. In iConference 2015 Proceedings, 2015. [22] N. Henke, J. Bughin, and al. The age of analytics: Competing in a data-driven world. McKinsey Global Institute, 2016. [23] M. Hils, D. W. Woods, and R. Böhme. Measuring the emergence of consent man- agement on the web. In Proceedings of the ACM Internet Measurement Conference, IMC ’20, page 317–332, New York, NY, USA, 2020. ACM. [24] IDSA. International data spaces reference architecture model. International Data Spaces Association, 2022. [25] Kaggle. Datasets. https://www.kaggle.com/datasets. Last accessed: Jun ’22. [26] P. Koutris, P. Upadhyaya, M. Balazinska, B. Howe, and D. Suciu. Querymarket demonstration: Pricing for online data markets. Proc. of the VLDB Endow., 5, 2012. [27] C. Li, D. Y. Li, G. Miklau, and D. Suciu. A theory of pricing private data. 39(4), 2015. [28] LiveRamp. Data marketplace. https://liveramp.com/our-platform/data- marketplace/. Last accessed: Jun ’22. [29] LOTAME. Private data exchange (pdx). trusted data relationships made easy. https://www.lotame.com/pdx/. Last accessed: Jun ’22. [30] C. C. Lukasz Olejnik, Minh-Dung Tran. Selling off privacy at auction. In Proc. of the NDSS Symposium, 2014. [31] A. Löser, F. Stahl, A. Muschalle, and G. Vossen. Pricing approaches for data markets. In Proc. of the International Workshop on Business Intelligence for the Real-Time Enterprise, 2012. [32] C. Niu, Z. Zheng, F. Wu, S. Tang, X. Gao, and G. Chen. Unlocking the value of privacy: Trading aggregate statistics over private correlated data. In Proc. of ACM SIGKDD, 2018. [33] O. Ohrimenko, S. Tople, and S. Tschiatschek. Collaborative machine learning markets with data-replication-robust payments. CoRR, 2019. [34] Otonomo. One-stop shop for vehicle data. https://otonomo.io/. Last accessed: Feb 2022. [35] P. Papadopoulos, N. Kourtellis, P. R. Rodriguez, and N. Laoutaris. If you are not paying for it, you are the product: How much do advertisers pay to reach you? In Proc. of the ACM IMC, 2017. [36] J. Pei. Data pricing – from economics to data science. In Proc. of the ACM SIGKDD, page 3553–3554, 2020. [37] Refinitiv. Data catalog. our data, your way. https://www.refinitiv.com/en/ financial-data. Last accessed: Jun ’22. [38] F. Schomm, F. Stahl, and G. Vossen. Marketplaces for data: An initial survey. ACM SIGMOD Record, 2013. [39] C. Shapiro and H. R. Varian. Information Rules: A Strategic Guide to the Network Economy. Harvard Business School Press, 2000. [40] M. Spiekermann. Data marketplaces: Trends and monetisation of data goods. Intereconomics, 2019. [41] F. Stahl, F. Schomm, L. Vomfell, and G. Vossen. Marketplaces for digital data: Quo vadis? Computer and Information Science, 10, 2017. [42] F. Stahl, F. Schomm, and G. Vossen. The Data Marketplace Survey Revisited. Westf. Wilhelms-Univ., ERCIS, 2014. [43] Veracity. Veracity by dnv gl. find the right tools for your industry needs. https: //store.veracity.com/. Last accessed: Jun ’22. [44] C. Wu, R. Buyya, and K. Ramamohanarao. Cloud pricing models: Taxonomy, survey, and interdisciplinary challenges. ACM Comput. Surv., 2019. [45] H. Yu and M. Zhang. Data pricing strategy based on data quality. Computers and Industrial Engineering, 112:1–10, 2017. | es |
| dc.identifier.uri | https://hdl.handle.net/20.500.12761/1641 | |
| dc.description.abstract | A large number of Data Marketplaces (DMs) have appeared in the last few years to help owners monetise their data, and data buyers optimize their marketing campaigns, train their ML models, and facilitate other data-driven decision processes. In this paper, we present a first of its kind measurement study of the growing DM ecosystem, shedding light on several totally unknown facts about it. We show that data products listed in commercial DMs may cost from few to hundreds of thousands of US dollars. We analyse the prices of different categories of data and the challenges of comparing across DMs. We also analise the pricing of specific sellers and products to identify features that apparently correlate with prices, and we point to the need and the challenges of building a quotation tool for data products based on market data. | es |
| dc.description.sponsorship | EU Horizon 2020 | es |
| dc.language.iso | eng | es |
| dc.title | Measuring the Price of Data in Commercial Data Marketplaces | es |
| dc.type | conference object | es |
| dc.conference.date | 9 December 2022 | es |
| dc.conference.place | Rome, Italy | es |
| dc.conference.title | ACM Data Economy Workshop | * |
| dc.event.type | workshop | es |
| dc.pres.type | paper | es |
| dc.type.hasVersion | AM | es |
| dc.rights.accessRights | open access | es |
| dc.relation.projectID | https://cordis.europa.eu/project/id/101070069 | es |
| dc.relation.projectName | DataBri-X (Data Process & Technological Bricks for expanding digital value creation in European Data Spaces) | es |
| dc.subject.keyword | data economy | es |
| dc.subject.keyword | data marketplace | es |
| dc.subject.keyword | data pricing | es |
| dc.subject.keyword | measurement | es |
| dc.description.refereed | TRUE | es |
| dc.description.status | pub | es |
