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example of high-volume data that have high value for real-time market trend
monitoring, but decreasing value with time and market volatility [19].
2.2.3.5 Veracity
The veracity dimension of big data includes two aspects: data consistency
(or certainty), which can be defined by statistical reliability of the data, and
data trustworthiness, which is defined by a number of factors, among them
data origin and collection and processing methods, including trusted infra-
structure and facility.
Big data veracity ensures that the data used are trusted, authentic, and pro-
tected from unauthorized access and modification. The data must be secured
during their whole life cycle, from collection from trusted sources to pro-
cessing on trusted computing facilities and storage on protected and trusted
storage facilities.
The following aspects define and need to be addressed to ensure data veracity:
• Integrity of data and linked data (e.g., for complex hierarchical data,
distributed data)
• Data authenticity and (trusted) origin
• Identification of both data and source
• Computer and storage platform trustworthiness
• Availability and timeliness
• Accountability and reputation
Data veracity relies entirely on the security infrastructure deployed and
available from the big data infrastructure [20].
2.3 Research Infrastructures and Infrastructure Requirements
This section refers to and provides a short overview of different scientific
communities, in particular as defined by the European Research Area (ERA)
[21], to define requirements for the infrastructure facility, data-processing and
management functionalities, user management, access control, and security.
2.3.1 Paradigm Change in Modern e-Science
Modern e-science is moving to the data-intensive technologies that are
becoming a new technology driver and require rethinking a number of
infrastructure architecture and operational models, components, solutions,
and processes to address the following general challenges [2, 4]:
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