119 research outputs found
Graph Summarization
The continuous and rapid growth of highly interconnected datasets, which are
both voluminous and complex, calls for the development of adequate processing
and analytical techniques. One method for condensing and simplifying such
datasets is graph summarization. It denotes a series of application-specific
algorithms designed to transform graphs into more compact representations while
preserving structural patterns, query answers, or specific property
distributions. As this problem is common to several areas studying graph
topologies, different approaches, such as clustering, compression, sampling, or
influence detection, have been proposed, primarily based on statistical and
optimization methods. The focus of our chapter is to pinpoint the main graph
summarization methods, but especially to focus on the most recent approaches
and novel research trends on this topic, not yet covered by previous surveys.Comment: To appear in the Encyclopedia of Big Data Technologie
A fractal theory approach to the initial examination of normal faulting in Central Corinthian Gulf, Greece
An application of fractal theory in geological formations in the central Corinthian Gulf, Greece, is presented in an attempt to study the nature of presently active deformation. Fault patterns are approximated under the perspective of fractal theory concept, leading to the conclusion that fractal approach can be considered valid for the region of study. Nevertheless, homogeneity may be expected with the reservation that there are no considerable changes in the viscosities of the ductile layers in the region, so that the characteristic exponent b+1-a is less than zero
SOFOS: Demonstrating the Challenges of Materialized View Selection on Knowledge Graphs
Analytical queries over RDF data are becoming prominent as a result of the
proliferation of knowledge graphs. Yet, RDF databases are not optimized to
perform such queries efficiently, leading to long processing times. A well
known technique to improve the performance of analytical queries is to exploit
materialized views. Although popular in relational databases, view
materialization for RDF and SPARQL has not yet transitioned into practice, due
to the non-trivial application to the RDF graph model. Motivated by a lack of
understanding of the impact of view materialization alternatives for RDF data,
we demonstrate SOFOS, a system that implements and compares several cost models
for view materialization. SOFOS is, to the best of our knowledge, the first
attempt to adapt cost models, initially studied in relational data, to the
generic RDF setting, and to propose new ones, analyzing their pitfalls and
merits. SOFOS takes an RDF dataset and an analytical query for some facet in
the data, and compares and evaluates alternative cost models, displaying
statistics and insights about time, memory consumption, and query
characteristics
mHealth and telemedicine apps: in search of a common regulation
Developments in information and communication technology have changed the way healthcare processes are experienced by both
patients and healthcare professionals: more and more services are now available through computers and mobile devices. Smartphones are
becoming useful tools for managing one’s health, and today, there are many available apps meant to increase self-management, empowerment and quality of life. However, there are concerns about the implications of using mHealth and apps: data protection issues, concerns
about sharing information online, and the patients’ capacity for discerning effective and valid apps from useless ones. The new General
Data Protection Regulation has been introduced in order to give uniformity to data protection regulations among European countries but
shared guidelines for mHealth are yet to develop. A unified perspective across Europe would increase the control over mHealth exploitation,
making it possible to think of mHealth as effective and standard tools for future medical practice
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