107,807 research outputs found
Entity Identification Problem in Big and Open Data
Big and Open Data provide great opportunities to businesses to enhance their competitive advantages if
utilized properly. However, during past few years’ research in Big and Open Data process, we have
encountered big challenge in entity identification reconciliation, when trying to establish accurate
relationships between entities from different data sources. In this paper, we present our innovative Intelligent
Reconciliation Platform and Virtual Graphs solution that addresses this issue. With this solution, we are able
to efficiently extract Big and Open Data from heterogeneous source, and integrate them into a common
analysable format. Further enhanced with the Virtual Graphs technology, entity identification reconciliation
is processed dynamically to produce more accurate result at system runtime. Moreover, we believe that our
technology can be applied to a wide diversity of entity identification problems in several domains, e.g., e-
Health, cultural heritage, and company identities in financial world.Ministerio de Ciencia e InnovaciĂłn TIN2013-46928-C3-3-
UniquID: A Quest to Reconcile Identity Access Management and the Internet of Things
The Internet of Things (IoT) has caused a revolutionary paradigm shift in
computer networking. After decades of human-centered routines, where devices
were merely tools that enabled human beings to authenticate themselves and
perform activities, we are now dealing with a device-centered paradigm: the
devices themselves are actors, not just tools for people. Conventional identity
access management (IAM) frameworks were not designed to handle the challenges
of IoT. Trying to use traditional IAM systems to reconcile heterogeneous
devices and complex federations of online services (e.g., IoT sensors and cloud
computing solutions) adds a cumbersome architectural layer that can become hard
to maintain and act as a single point of failure. In this paper, we propose
UniquID, a blockchain-based solution that overcomes the need for centralized
IAM architectures while providing scalability and robustness. We also present
the experimental results of a proof-of-concept UniquID enrolment network, and
we discuss two different use-cases that show the considerable value of a
blockchain-based IAM.Comment: 15 pages, 10 figure
Big Data Dimensional Analysis
The ability to collect and analyze large amounts of data is a growing problem
within the scientific community. The growing gap between data and users calls
for innovative tools that address the challenges faced by big data volume,
velocity and variety. One of the main challenges associated with big data
variety is automatically understanding the underlying structures and patterns
of the data. Such an understanding is required as a pre-requisite to the
application of advanced analytics to the data. Further, big data sets often
contain anomalies and errors that are difficult to know a priori. Current
approaches to understanding data structure are drawn from the traditional
database ontology design. These approaches are effective, but often require too
much human involvement to be effective for the volume, velocity and variety of
data encountered by big data systems. Dimensional Data Analysis (DDA) is a
proposed technique that allows big data analysts to quickly understand the
overall structure of a big dataset, determine anomalies. DDA exploits
structures that exist in a wide class of data to quickly determine the nature
of the data and its statical anomalies. DDA leverages existing schemas that are
employed in big data databases today. This paper presents DDA, applies it to a
number of data sets, and measures its performance. The overhead of DDA is low
and can be applied to existing big data systems without greatly impacting their
computing requirements.Comment: From IEEE HPEC 201
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