A classification framework for data marketplaces
Abstract
Trading data as a commodity has
become increasingly popular in recent years, and data marketplaces have
emerged as a new business model where data from a variety of sources can
be collected, aggregated, processed, enriched, bought, and sold. They
are effectively changing the way data are distributed and managed on the
Internet. To get a better understanding of the emergence of data
marketplaces, we have conducted several surveys in recent years to
systematically gather and evaluate their characteristics. This paper
takes a broader perspective and relates data marketplaces as currently
discussed in computer science to the neoclassical notions of market and
marketplace from economics. Specifically, we provide a typology of
electronic marketplaces and discuss their approaches to the distribution
of data. Finally, we provide a distinct definition of data
marketplaces, leading to a classification framework that can provide
structure for the emerging field of data marketplace research.
Keywords
Data-as-a-Service, Data marketplace, Data marketplace survey, Data marketplace development, Classification, Economics, Computer Science
Link Source: http://link.springer.com/article/10.1007/s40595-016-0064-2
A generalized fault-detection software reliability model subject to random operating environments
Link Source: http://link.springer.com/article/10.1007/s40595-016-0065-1
Maximal assortative matching for real-world network graphs, random network graphs and scale-free network graphs
A generalized fault-detection software reliability model subject to random operating environments
Abstract
Many software reliability growth
models (SRGMs) have been developed in the past three decades to
estimate software reliability measures such as the number of remaining
faults and software reliability. The underlying common assumption of
many existing models is that the operating environment and the
developing environment are the same. This is often not the case in
practice because the operating environments are usually unknown due to
the uncertainty of environments in the field. In this paper, we develop a
generalized software reliability model incorporating the uncertainty of
fault-detection rate per unit of time in the operating environments. A
logistic fault-detection software reliability model is derived. Examples
are included to illustrate the goodness of fit of the proposed model
and existing nonhomogeneous Poisson process (NHPP) models based on a set
of failure data. Three goodness-of-fit criteria, such as mean square
error, predictive power, and predictive ratio risk are used as an
example to illustrate model comparisons. The results show that the
proposed logistic fault-detection model fit significantly better than
other existing NHPP models based on all three goodness-of-fit criteria.
Keywords
Non-homogeneous Poisson process, Software reliability growth model, Mean square error, Predictive ratio risk, Predictive power, Logistic fault-detection rateLink Source: http://link.springer.com/article/10.1007/s40595-016-0065-1
Maximal assortative matching for real-world network graphs, random network graphs and scale-free network graphs
Abstract
We define the problem of maximal
assortativity matching (MAM) as a variant of the maximal matching
problem wherein we want to maximize the similarity between the end
vertices (with respect to any particular measure for node weight)
constituting the matching. The MAM algorithm (with a targeted
assortative index value of 1) works on the basis of the assortative
weight of an edge, defined as the product of the number of uncovered
adjacent edges and the absolute difference of the weights of the end
vertices of the edge. The MAM algorithm prefers to include the edge with
the smallest assortativity weight (the assortative weight of the edges
is updated for each iteration) until all the edges in the graph are
covered. We show that the MAM algorithm can be easily adapted to be used
for maximal dissortative matching (MDM) with a targeted assortative
index of −
1
for the matching as well as for maximal node matching (MNM) algorithm
to maximize the percentage of nodes matched. We illustrate the execution
of the MAM, MDM and MNM algorithms on complex network graphs such as
the random network graphs and scale-free network graphs as well as on
real-world network graphs and analyze the tradeoffs.
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