=Paper=
{{Paper
|id=Vol-1458/H04_CRC36_Stahl
|storemode=property
|title=Data Quality Adjustments for Pricing on Data
Marketplaces
|pdfUrl=https://ceur-ws.org/Vol-1458/H04_CRC36_Stahl.pdf
|volume=Vol-1458
|dblpUrl=https://dblp.org/rec/conf/lwa/StahlV15
}}
==Data Quality Adjustments for Pricing on Data
Marketplaces==
https://ceur-ws.org/Vol-1458/H04_CRC36_Stahl.pdf
Data Quality Adjustments for Pricing on Data
Marketplaces
Florian Stahl1 and Gottfried Vossen1,2
1
ERCIS, Leonardo-Campus 3, 48149 Münster, Germany,
{Stahl,Vossen}@ercis.de
2
University of Waikato Management School,Private Bag 3105,
Hamilton, 3240, New Zealand
Abstract. Currently, information has become an increasingly impor-
tant production factor which has led to the emergence of data market-
places that leverage big data technologies. However, value attribution
of data is still difficult. This work suggests to discuss what role data
quality can play in this context, particularly: what quality measures are
relevant in the context of big data, how they can be measured, and how
the quality of a data product can be efficiently modified to create differ-
ent versions3 of a data product.
1 Introduction
Information has become an important production factor [6]. This has led to a
point at which data – as the basic unit in which information is exchanged – is
increasingly being traded on data marketplaces, extensively described in [4, 9]
and put on the database research agenda by Balazinska et al. [2, 1]. Basi-
cally, data marketplaces are platforms levering big data technologies that allow
providers and consumers of data and data-related services, such as data min-
ing algorithms, to interact with each other. One prominent German example of
such a data marketplace is MIA4 which employs large computer clusters to crawl
substantial parts of the German Web and to provide an analysis infrastructure
for the gathered data. This is particularly beneficial for small and medium-sized
enterprises as they would otherwise not be able to access and analyse such data.
This paper suggests to discuss what role data quality modifications can play in
the context of data marketplaces and big data applications building on them.
Copyright © 2015 by the paper’s authors. Copying permitted only for private and
academic purposes. In: R. Bergmann, S. Görg, G. Müller (Eds.): Proceedings of
the LWA 2015 Workshops: KDML, FGWM, IR, and FGDB. Trier, Germany, 7.-9.
October 2015, published at http://ceur-ws.org
3
In this work, the term version will be used in its economic sense, i. e., to refer to
different variants of a data product; this is not to be confused with versions as known
from temporal databases.
4
http://mia-marktplatz.de/
419
2 Pricing on Data Marketplaces
Given that the value for data and data-related services is subjective to its con-
sumers [7], it is not surprising that little sense for its value exists in the database
community [2, 1]; this is mainly owing to the fact that data is an information
good with peculiarities, such as resemblance to public goods [12].
One approach to reduce the uncertainty for data providers is to apply re-
verse pricing mechanisms that allow customers to suggest prices, which – if
well-designed – allow for a revelation of the customer’s true willingness to pay.
Reverse pricing has the advantage that customers participate in the pricing pro-
cess, which is generally seen as positive, even if used for price discrimination –
i. e., asking different prices of different customers [3].
Name Your Own Price is such a pricing mechanism which is often employed
in auctions, for instance, ebay’s make offer option. In contrast to established
physical goods, digital goods, such as data, can be sold multiple times because
of the low cost of reproduction. Thus, in order to avoid fierce price wars, it
is recommendable to adapt a data product to a customer’s preferences, which
can also be seen as a further benefit for customers. Although not discussing the
economic intuition behind it, Tang et al. suggested to use a Name Your Own
Price mechanism in the context of data marketplaces. In [10] they suggested to
adapt the completeness of XML data and in [11] they focused on the accuracy
of relational data based on a customer’s bid. Concretely, the provider advertises
a price and customers may suggest a price they are willing to pay. If the bid is
lower than the ask price, completeness or accuracy of the data product will be
lowered to match the offered price. Furthermore, the argument can be made that
the threshold can also be hidden from the buyer. In this case the profit increases
if the suggested price is higher than the requested price.
3 Discussion
The previously mentioned works focus on only one quality dimension. Therefore,
the question remains how this can be adapted to multiple quality criteria, which
has been extensively discussed in [8]. As a starting point we suggest to model
the distribution of discounts to different quality criteria as a multiple-choice
knapsack problem. Given 1) a set of quality criteria, 2) a function that creates
versions for all quality criteria, 3) a function that attributes the ask price to
these versions, and 4) customer as well as vendor preferences for certain quality
criteria, an optimal combination can be calculated even on commodity hardware
for a limited number of quality criteria such as those identified by Naumann [5].
Having made these calculations, the quality of data products has to be ad-
justed to match a customer’s suggested price. However, at the moment it is not
quite clear what data quality dimensions are relevant in the context of big data
analysis applications. Thus, a number of questions arise. Consequently, this work
suggests to discuss the following questions:
– What quality dimensions are relevant for big data applications?
420
– How can they be practically applied to large data sets and how can they be
measured efficiently?
– And most importantly: how can big data architectures be utilised to adapt
the quality of big data products efficiently in order to meet a customer’s
requirements?
References
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Discussion on Pricing Relational Data”. In: In Search of Elegance in the
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Libkin, W. Fan, W.-C. Tan, and M. Fourman. Vol. 8000. Lecture Notes in
Computer Science. Springer Berlin Heidelberg, 2013, pp. 167–173.
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